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Yuan J, Wu M, Qiu L, Xu W, Fei Y, Zhu Y, Shi K, Li Y, Luo J, Ding Z, Sun X, Zhou S. Tumor habitat-based MRI features assessing early response in locally advanced nasopharyngeal carcinoma. Oral Oncol 2024; 158:106980. [PMID: 39151333 DOI: 10.1016/j.oraloncology.2024.106980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/08/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024]
Abstract
OBJECTIVE The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors. METHODS A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833). CONCLUSION The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.
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Affiliation(s)
- Jinling Yuan
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Mengxing Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lei Qiu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Weilin Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yinjiao Fei
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yuchen Zhu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Kexin Shi
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yurong Li
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Jinyan Luo
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Zhou Ding
- Department of Radiation Oncology, Lianshui County People's Hospital, Huai'an 223400, Jiangsu, China.
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| | - Shu Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
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Lee HJ, Lee JH, Lee JE, Na YM, Park MH, Lee JS, Lim HS. Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI. Sci Rep 2024; 14:21691. [PMID: 39289507 PMCID: PMC11408492 DOI: 10.1038/s41598-024-72581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 09/09/2024] [Indexed: 09/19/2024] Open
Abstract
This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644-0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614-0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699-0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.
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Affiliation(s)
- Hyo-Jae Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jeong Hoon Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jong Eun Lee
- Department of Radiology and the Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Yong Min Na
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
- Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Ji Shin Lee
- Department of Pathology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
- Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea.
- Chonnam National University Medical School, Gwangju, Republic of Korea.
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Qi YJ, Su GH, You C, Zhang X, Xiao Y, Jiang YZ, Shao ZM. Radiomics in breast cancer: Current advances and future directions. Cell Rep Med 2024; 5:101719. [PMID: 39293402 DOI: 10.1016/j.xcrm.2024.101719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/10/2024] [Accepted: 08/14/2024] [Indexed: 09/20/2024]
Abstract
Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.
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Affiliation(s)
- Ying-Jia Qi
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xu Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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You C, Su GH, Zhang X, Xiao Y, Zheng RC, Sun SY, Zhou JY, Lin LY, Wang ZZ, Wang H, Chen Y, Peng WJ, Jiang YZ, Shao ZM, Gu YJ. Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection. NPJ Precis Oncol 2024; 8:193. [PMID: 39244594 PMCID: PMC11380684 DOI: 10.1038/s41698-024-00666-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 07/24/2024] [Indexed: 09/09/2024] Open
Abstract
Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model's biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy.
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Affiliation(s)
- Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xu Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Zhou Wang
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yan Chen
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Förnvik D, Borgquist S, Larsson M, Zackrisson S, Skarping I. Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy. Eur J Radiol 2024; 178:111624. [PMID: 39029241 DOI: 10.1016/j.ejrad.2024.111624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 05/15/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024]
Abstract
PURPOSE Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT. METHODS A total of 149 women (mean age 53 years, pCR rate 22 %) with breast cancer treated with NACT at Skane University Hospital, Sweden, between 2014 and 2019, were prospectively included in this observational cohort study (ClinicalTrials.gov: NCT02306096). DBT images from both the cancer and contralateral healthy breasts acquired at three time points: pre-NACT, after two cycles of NACT, and after the completion of six cycles of NACT (pre-surgery) were analyzed. The deep learning AI system used to predict pCR consisted of a backbone 3D ResNet and an attention and prediction module. The GradCAM method was used to obtain insights into the model decision basis through a quantitative analysis of the importance maps on the validation set. Moreover, specific model choices were motivated by ablation studies. RESULTS The AI model reached an AUC of 0.83 (95% CI: 0.63-1.00) (test set). The spatial correlation of importance maps for input volumes from the same patient but at different time points was high, possibly indicating that the model focuses on the same areas during decision-making. CONCLUSIONS We demonstrate a high discriminative performance of our algorithm for predicting pCR/non-pCR. Availability of larger datasets would permit more comprehensive training of the models and more rigorous evaluation of their prediction performance for future patients.
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Affiliation(s)
- Daniel Förnvik
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Skane University Hospital, Malmö, Sweden; Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden.
| | - Signe Borgquist
- Department of Oncology, Aarhus University Hospital/Aarhus University, Denmark; Division of Oncology, Department of Clinical Sciences, Lund University, Sweden.
| | | | - Sophia Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University and Department of Radiology, Skane University Hospital, Malmö, Sweden.
| | - Ida Skarping
- Division of Oncology, Department of Clinical Sciences, Lund University, Sweden; The Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden.
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Zhu T, Huang YH, Li W, Wu CG, Zhang YM, Zheng XX, Zhang TF, Lin YY, Liu ZY, Ye GL, Lin Y, Wu ZY, Wang K. A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial. Br J Cancer 2024; 131:692-701. [PMID: 38918556 PMCID: PMC11333754 DOI: 10.1038/s41416-024-02726-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER The clinical trial numbers were NCT03154749 and NCT04858529.
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Affiliation(s)
- Teng Zhu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Yu-Hong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Wei Li
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Can-Gui Wu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Yi-Min Zhang
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
| | - Xing-Xing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Ting-Feng Zhang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Ying-Yi Lin
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
- Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Zai-Yi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Guo-Lin Ye
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhi-Yong Wu
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
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Chen S, Zheng B, Tang W, Ding S, Sui Y, Yu X, Zhong Z, Kong Q, Liu W, Guo Y. The longitudinal changes in multiparametric MRI during neoadjuvant chemotherapy can predict treatment response early in patients with HER2-positive breast cancer. Eur J Radiol 2024; 178:111656. [PMID: 39098252 DOI: 10.1016/j.ejrad.2024.111656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 07/17/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024]
Abstract
PURPOSE To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features. METHODS A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models. RESULTS Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets. CONCLUSIONS The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.
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Affiliation(s)
- Siyi Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Bingjie Zheng
- Department of Radiology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 150001, China.
| | - Wenjie Tang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Shishen Ding
- Department of Radiology, Liuzhou People's Hospital, Guangxi Medical University, Liuzhou 545006, China.
| | - Yi Sui
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Xiaomeng Yu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Zhidan Zhong
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Qingcong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Tianhe District, Guangzhou 510630, China.
| | - Weifeng Liu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
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Ramtohul T, Lepagney V, Bonneau C, Jin M, Menet E, Sauge J, Laas E, Romano E, Bello-Roufai D, Mechta-Grigoriou F, Vincent Salomon A, Bidard FC, Langer A, Malhaire C, Cabel L, Brisse HJ, Tardivon A. Use of Pretreatment Perfusion MRI-based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy. Radiology 2024; 312:e240575. [PMID: 39225608 DOI: 10.1148/radiol.240575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or Ktrans), volume fraction of extravascular and extracellular space (Ve), and maximum contrast agent uptake rate (Slopemax) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (P < .001), washout enhancement (P = .01), androgen receptor expression (P = .009), and programmed death ligand 1 expression (P = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Rauch in this issue.
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Affiliation(s)
- Toulsie Ramtohul
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Victoire Lepagney
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Claire Bonneau
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Maxime Jin
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Emmanuelle Menet
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Juliette Sauge
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Enora Laas
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Emanuela Romano
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Diana Bello-Roufai
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Fatima Mechta-Grigoriou
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Anne Vincent Salomon
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - François-Clément Bidard
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Adriana Langer
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Caroline Malhaire
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Luc Cabel
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Hervé J Brisse
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
| | - Anne Tardivon
- From the Department of Radiology (T.R., V.L., M.J., C.M., H.J.B., A.T.), Department of Diagnostic and Theranostic Medicine-Pathology (J.S., A.V.S.), Department of Surgical Oncology (E.L.), Department of Medical Oncology (E.R.), Stress and Cancer Laboratory (F.M.G.), and INSERM U830 (F.M.G.), Institut Curie, PSL University, 26 rue d'Ulm, 75005 Paris, France; Department of Surgical Oncology and INSERM U900, Statistical Methods for Precision Medicine, Institut Curie, University of Versailles Saint-Quentin-en-Yvelines, Saint-Cloud, France (C.B.); Departments of Diagnostic and Theranostic Medicine-Pathology (E.M.), Medical Oncology (D.B.R., F.C.B., L.C.), and Radiology (A.L.), Institut Curie, PSL University, Saint-Cloud, France; Department of Immunology, PSL University, Paris, France (E.R.); and Circulating Tumor Biomarkers Laboratory, Department of Translational Research, Institut Curie, Paris, France (F.C.B.)
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Wang G, Guo Q, Shi D, Zhai H, Luo W, Zhang H, Ren Z, Yan G, Ren K. Clinical Breast MRI-based Radiomics for Distinguishing Benign and Malignant Lesions: An Analysis of Sequences and Enhanced Phases. J Magn Reson Imaging 2024; 60:1178-1189. [PMID: 38006286 DOI: 10.1002/jmri.29150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear. PURPOSE To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)-guided biopsy. STUDY TYPE Retrospective. POPULATION 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137). FIELD STRENGTH/SEQUENCE 3.0-T, fast spoiled gradient-echo and fast spin-echo T1-weighted imaging (T1WI), fast spin-echo T2-weighted imaging (T2WI), echo-planar diffusion-weighted imaging (DWI), and fast spoiled gradient-echo contrast-enhanced MRI (CE-MRI). ASSESSMENT Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE-MRI, CE-MRI + DWI, CE-MRI + DWI + T2WI, CE-MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad-score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad-score) at sensitivity >98%. STATISTICAL TESTS Wilcoxon paired-samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7). RESULTS For radiomics models, CE-MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833-0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%. DATA CONCLUSION The CE-MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%-specificity) from 68.4% to 44.7% while retaining sensitivity >98%. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Qiu Guo
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Huige Zhai
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Wenbin Luo
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Zhendong Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Gen Yan
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen university, Xiamen, Fujian, China
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Zheng S, Yang Z, Du G, Zhang Y, Jiang C, Xu T, Li B, Wang D, Qiu Y, Lin D, Zhang X, Shen J. Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics. Eur Radiol 2024; 34:6132-6144. [PMID: 38363315 DOI: 10.1007/s00330-024-10641-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/17/2024]
Abstract
OBJECTIVES To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. METHODS A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. CONCLUSIONS Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.
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Affiliation(s)
- Shaoyan Zheng
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
| | - Guangzhou Du
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Yiwei Zhang
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Chuling Jiang
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Tingting Xu
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Bingli Li
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Danfeng Wang
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Ya Qiu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
| | - Daiying Lin
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China.
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China.
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
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11
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Lin X, Wei R, Xu Z, Zhuo S, Dou J, Sun H, Li R, Yang R, Lu Q, An C, Chen H. A deep learning model for personalized intra-arterial therapy planning in unresectable hepatocellular carcinoma: a multicenter retrospective study. EClinicalMedicine 2024; 75:102808. [PMID: 39296944 PMCID: PMC11407998 DOI: 10.1016/j.eclinm.2024.102808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 08/15/2024] [Accepted: 08/15/2024] [Indexed: 09/21/2024] Open
Abstract
Background Unresectable Hepatocellular Carcinoma (uHCC) poses a substantial global health challenge, demanding innovative prognostic and therapeutic planning tools for improved patient management. The predominant treatment strategies include Transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC). Methods Between January 2014 and November 2021, a total of 1725 uHCC patients [mean age, 52.8 ± 11.5 years; 1529 males] received preoperative CECT scans and were eligible for TACE or HAIC. Patients were assigned to one of the four cohorts according to their treatment, four transformer models (SELECTION) were trained and validated on each cohort; AUC was used to determine the prognostic performance of the trained models. Patients were stratified into high and low-risk groups based on the survival scores computed by SELECTION. The proposed AI-based treatment decision model (ATOM) utilizes survival scores to further inform final therapeutic recommendation. Findings In this study, the training and validation sets included 1448 patients, with an additional 277 patients allocated to the external validation sets. The SELECTION model outperformed both clinical models and the ResNet approach in terms of AUC. Specifically, SELECTION-TACE and SELECTION-HAIC achieved AUCs of 0.761 (95% CI, 0.693-0.820) and 0.805 (95% CI, 0.707-0.881) respectively, in predicting ORR in their external validation cohorts. In predicting OS, SELECTION-TC and SELECTION-HC demonstrated AUCs of 0.736 (95% CI, 0.608-0.841) and 0.748 (95% CI, 0.599-0.865) respectively, in their external validation sets. SELECTION-derived survival scores effectively stratified patients into high and low-risk groups, showing significant differences in survival probabilities (P < 0.05 across all four cohorts). Additionally, the concordance between ATOM and clinician recommendations was associated with significantly higher response/survival rates in cases of agreement, particularly within the TACE, HAIC, and TC cohorts in the external validation sets (P < 0.05). Interpretation ATOM was proposed based on SELECTION-derived survival scores, emerges as a promising tool to inform the selection among different intra-arterial interventional therapy techniques. Funding This study received funding from the Beijing Municipal Natural Science Foundation, China (Z190024); the Key Program of the National Natural Science Foundation of China, China (81930119); The Science and Technology Planning Program of Beijing Municipal Science & Technology Commission and Administrative Commission of Zhongguancun Science Park, China (Z231100004823012); Tsinghua University Initiative Scientific Research Program of Precision Medicine, China (10001020108); and Institute for Intelligent Healthcare, Tsinghua University, China (041531001).
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Affiliation(s)
- Xiaoqi Lin
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Ran Wei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China
| | - Ziming Xu
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Shuiqing Zhuo
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China
| | - Jiaqi Dou
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Haozhong Sun
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Rui Li
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Runyu Yang
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
| | - Qian Lu
- Tsinghua Changgung Hospital, School of Clinical Medicine, Institute for Precision Medicine, Tsinghua University, Beijing, 100190, China
| | - Chao An
- Department of Minimal Invasive Intervention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China
| | - Huijun Chen
- School of Biomedical Engineering, Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100019, China
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12
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Shi SY, Li YA, Qiang JW. Multiparametric MRI-based radiomics nomogram for differentiation of primary mucinous ovarian cancer from metastatic ovarian cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04542-y. [PMID: 39215773 DOI: 10.1007/s00261-024-04542-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVE To develop a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram and evaluate its performance in differentiating primary mucinous ovarian cancer (PMOC) from metastatic ovarian cancer (MOC). METHODS A total of 194 patients with PMOC (n = 72) and MOC (n = 122) confirmed by histology were randomly divided into the primary cohort (n = 137) and validation cohort (n = 57). Radiomics features were extracted from axial fat-saturated T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences of each lesion. The effective features were selected by minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression to develop a radiomics model. Combined with clinical features, multivariate logistic regression analysis was employed to develop a radiomics nomogram. The efficiency of nomogram was evaluated using the receiver operating characteristic (ROC) curve analysis and compared using DeLong test. Finally, the goodness of fit and clinical benefit of nomogram were assessed by calibration curves and decision curve analysis, respectively. RESULTS The radiomics nomogram, by combining the mpMRI radiomics features with clinical features, yielded area under the curve (AUC) values of 0.931 and 0.934 in the primary and validation cohorts, respectively. The predictive performance of the radiomics nomogram was significantly superior to the radiomics model (0.931 vs. 0.870, P = 0.004; 0.934 vs. 0.844, P = 0.032), the clinical model (0.931 vs. 0.858, P = 0.005; 0.934 vs. 0.847, P = 0.030), and radiologists (all P < 0.05) in the primary and validation cohorts, respectively. The decision curve analysis revealed that the nomogram could provide higher net benefit to patients. CONCLUSION The mpMRI-based radiomics nomogram exhibited notable predictive performance in differentiating PMOC from MOC, emerging as a non-invasive preoperative imaging approach.
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Affiliation(s)
- Shu Yi Shi
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yong Ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Changzhi People's Hospital, Changzhi, Shanxi, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
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13
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Yang Z, Liu C. Research on the application of radiomics in breast cancer: A bibliometrics and visualization analysis. Medicine (Baltimore) 2024; 103:e39463. [PMID: 39213225 PMCID: PMC11365679 DOI: 10.1097/md.0000000000039463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/24/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024] Open
Abstract
Breast cancer is the most prevalent form of cancer worldwide. Therefore, improved disease detection has emerged as a focal point in clinical studies. At the forefront of innovation, radiomics has the capability to extract comprehensive insights from medical images, ultimately enhancing the accuracy of diagnostic procedures. There has been rapid growth in the field of radiomics research on breast cancer in the past few years. We explored pertinent research articles in the Web of Science Core Collection database to gain a thorough understanding of breast cancer radiomics. We used CiteSpace to conduct a bibliometric analysis of the annual distribution of different nations, institutions, journals, authors, keywords, and references in the field of breast cancer radiomics. GraphPad Prism software was used to examine and graph yearly and country-specific trends and the proportions of publications. The tools utilized for the visualization of science mapping included CiteSpace and VOSviewer. Of the 891 publications, most were original articles (731, 91.09%) and a few were reviews (160, 8.91%). Most academic research has been published in China and the United States. The study centers predominantly consisted of major academic institutions, such as Fudan University and the Chinese Academy of Sciences, with some of their members being prominent figures in the field. Pinker, Katja has published the largest number of research papers. The majority of these studies have been published in medical journals focusing on radiology and oncology in recent years. In the realm of cutting-edge medical research, the top two keywords, magnetic resonance imaging and machine learning stand at the forefront as current areas of intense focus. Breast cancer radiomics is advancing rapidly, presenting numerous opportunities and obstacles. Our study of the literature in this academic area aimed to pinpoint the primary themes addressed in the studies and anticipate prospective avenues for research.
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Affiliation(s)
- Zhe Yang
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, China
| | - Chenglong Liu
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, China
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14
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Ai Y, Zhu X, Zhang Y, Li W, Li H, Zhao Z, Zhang J, Ning B, Li C, Zheng Q, Zhang J, Jin J, Li Y, Xie C, Jin X. MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer. Radiother Oncol 2024; 197:110328. [PMID: 38761884 DOI: 10.1016/j.radonc.2024.110328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND AND PURPOSE Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. MATERIAL AND METHODS 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. RESULTS A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. CONCLUSION In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.
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Affiliation(s)
- Yao Ai
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaoyang Zhu
- Department of Radiotherapy, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Yu Zhang
- Department of Information Division, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenlong Li
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Heng Li
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zeshuo Zhao
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jicheng Zhang
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Boda Ning
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chenyu Li
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiao Zheng
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yiran Li
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Xiance Jin
- Radiotherapy Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
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15
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Lin Y, Wang J, Li M, Zhou C, Hu Y, Wang M, Zhang X. Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models. Breast 2024; 76:103737. [PMID: 38696854 PMCID: PMC11070644 DOI: 10.1016/j.breast.2024.103737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 04/05/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024] Open
Abstract
PURPOSE Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment. MATERIALS AND METHODS A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %. CONCLUSIONS Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.
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Affiliation(s)
- Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Jifei Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Meizhi Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Chunxiang Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Yangling Hu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Mengyi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China.
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Tong Y, Hu Z, Wang H, Huang J, Zhan Y, Chai W, Deng Y, Yuan Y, Shen K, Wang Y, Chen X, Yu J. Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model. Eur Radiol 2024; 34:5477-5486. [PMID: 38329503 PMCID: PMC11255056 DOI: 10.1007/s00330-024-10609-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/24/2023] [Accepted: 01/01/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVES Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. METHODS A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. RESULTS DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. CONCLUSIONS DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. CLINICAL RELEVANCE STATEMENT We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. KEY POINTS • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.
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Affiliation(s)
- Yiwei Tong
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Zhaoyu Hu
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Haoyu Wang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Jiahui Huang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Ying Zhan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yinhui Deng
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kunwei Shen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Xiaosong Chen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China.
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17
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Li X, Li C, Wang H, Jiang L, Chen M. Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer. PeerJ 2024; 12:e17683. [PMID: 39026540 PMCID: PMC11257043 DOI: 10.7717/peerj.17683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
Background Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. Methods This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. Results LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). Conclusion The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.
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Affiliation(s)
- Xue Li
- Radiology, Beijing Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Chunmei Li
- Radiology, Beijing Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Hong Wang
- Radiology, Beijing Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Lei Jiang
- Radiology, Beijing Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Min Chen
- Radiology, Beijing Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
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18
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Mohamed RM, Panthi B, Adrada BE, Boge M, Candelaria RP, Chen H, Guirguis MS, Hunt KK, Huo L, Hwang KP, Korkut A, Litton JK, Moseley TW, Pashapoor S, Patel MM, Reed B, Scoggins ME, Son JB, Thompson A, Tripathy D, Valero V, Wei P, White J, Whitman GJ, Xu Z, Yang W, Yam C, Ma J, Rauch GM. Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Sci Rep 2024; 14:16073. [PMID: 38992094 PMCID: PMC11239818 DOI: 10.1038/s41598-024-66220-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 06/28/2024] [Indexed: 07/13/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
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Affiliation(s)
- Rania M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Koc University Hospital, Istanbul, Turkey
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mary S Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Kelly K Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tanya W Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Miral M Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Brandy Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA.
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Pistel M, Brock L, Laun FB, Erber R, Weiland E, Uder M, Wenkel E, Ohlmeyer S, Bickelhaupt S. Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions. Diagnostics (Basel) 2024; 14:1427. [PMID: 39001317 PMCID: PMC11241112 DOI: 10.3390/diagnostics14131427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.
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Affiliation(s)
- Mona Pistel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Luise Brock
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Radiologie München, 80331 München, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
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Lin JY, Ye JY, Chen JG, Lin ST, Lin S, Cai SQ. Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research. Acad Radiol 2024; 31:3004-3014. [PMID: 38151383 DOI: 10.1016/j.acra.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 12/29/2023]
Abstract
Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.
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Affiliation(s)
- Jun-Yuan Lin
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Jia-Yi Ye
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Jin-Guo Chen
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Shu-Ting Lin
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Shu Lin
- Center of Neurological and Metabolic Research, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.Y., J.G.C., S.T.L., S.L.); Group of Neuroendocrinology, Garvan Institute of Medical Research, 384 Victoria St, Sydney, Australia (S.L.)
| | - Si-Qing Cai
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.).
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21
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Guo J, Meng W, Li Q, Zheng Y, Yin H, Liu Y, Zhao S, Ma J. Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Bioengineering (Basel) 2024; 11:663. [PMID: 39061745 PMCID: PMC11274092 DOI: 10.3390/bioengineering11070663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/13/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.
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Affiliation(s)
- Jiamin Guo
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
| | - Wenjun Meng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China;
| | - Qian Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Yichen Zheng
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd., No. 62 East Fourth Ring Middle Road, Chaoyang District, Beijing 100025, China;
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Shuang Zhao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Ji Ma
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
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22
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Xu C, Wang Z, Wang A, Zheng Y, Song Y, Wang C, Yang G, Ma M, He M. Breast Cancer: Multi-b-Value Diffusion Weighted Habitat Imaging in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy. Acad Radiol 2024:S1076-6332(24)00360-X. [PMID: 38890032 DOI: 10.1016/j.acra.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/20/2024]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to ascertain whether the utilization of multiple b-value diffusion-weighted habitat imaging, a technique that depicts tumor heterogeneity, could aid in identifying breast cancer patients who would derive substantial benefit from neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS This prospective study enrolled 143 women (II-III breast cancer), who underwent multi-b-value diffusion-weighted imaging (DWI) in 3-T magnetic resonance (MR) before NAC. The patient cohort was partitioned into a training set (consisting of 100 patients, of which 36 demonstrated a pathologic complete response [pCR]) and a test set (featuring 43 patients, 16 of whom exhibited pCR). Utilizing the training set, predictive models for pCR, were constructed using different parameters: whole-tumor radiomics (ModelWH), diffusion-weighted habitat-imaging (ModelHabitats), conventional MRI features (ModelCF), along with combined models ModelHabitats+CF. The performance of these models was assessed based on the area under the receiver operating characteristic curve (AUC) and calibration slope. RESULTS In the prediction of pCR, ModelWH, ModelHabitats, ModelCF, and ModelHabitats+CF achieved AUCs of 0.733, 0.722, 0.705, and 0.756 respectively, within the training set. These scores corresponded to AUCs of 0.625, 0.801, 0.700, and 0.824 respectively in the test set. The DeLong test revealed no significant difference between ModelWH and ModelHabitats (P = 0.182), between ModelHabitats and ModelHabitats+CF (P = 0.113). CONCLUSION The habitat model we developed, incorporating first-order features along with conventional MRI features, has demonstrated accurate predication of pCR prior to NAC. This model holds the potential to augment decision-making processes in personalized treatment strategies for breast cancer.
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Affiliation(s)
- Chao Xu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (C.X.)
| | - Zhihong Wang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Hematology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Z.W.)
| | - Ailing Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Yunyan Zheng
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.)
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China (Y.S.)
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China (A.W., C.W., G.Y.)
| | - Mingping Ma
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.)
| | - Muzhen He
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China (C.X., Z.W., Y.Z., M.M., M.H.); Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China (Y.Z., M.M., M.H.).
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23
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Liu J, Li X, Wang G, Zeng W, Zeng H, Wen C, Xu W, He Z, Qin G, Chen W. Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach. J Magn Reson Imaging 2024. [PMID: 38850180 DOI: 10.1002/jmri.29405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE Retrospective. POPULATION Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 4.
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Affiliation(s)
- Jialing Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xu Li
- Department of Radiotherapy, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Gang Wang
- Department of Radiology, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong Province, China
| | - Weixiong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Hui Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Chanjuan Wen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Weimin Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
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Xie T, Gong J, Zhao Q, Wu C, Wu S, Peng W, Gu Y. Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer. BMC Med Imaging 2024; 24:136. [PMID: 38844842 PMCID: PMC11155097 DOI: 10.1186/s12880-024-01311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, USA
| | - Siyu Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Tian R, Duan X, Xing F, Zhao Y, Liu C, Li H, Kong N, Cao R, Guan H, Li Y, Li X, Zhang J, Wang K, Yang P, Wang C. Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03192-1. [PMID: 38836956 DOI: 10.1007/s11548-024-03192-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA. MATERIALS AND METHODS After careful selection, data from 142 patients were randomly divided into a training set (n = 99) and a test set (n = 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P < 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis. RESULTS In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681-0.906), the radiomic model was 0.854 (95% CI 0.743-0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804-0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814-1.000), the radiomic model was 0.709 (95% CI 0.541-0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842-1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value. CONCLUSION We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.
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Affiliation(s)
- Run Tian
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xudong Duan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Fangze Xing
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiwei Zhao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - ChengYan Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Heng Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ning Kong
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ruomu Cao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Huanshuai Guan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiyang Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinghua Li
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jiewen Zhang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Kunzheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Pei Yang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Chunsheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Lee YD, Kim HG, Seo M, Moon SK, Park SJ, You MW. Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images. Int J Colorectal Dis 2024; 39:78. [PMID: 38789861 PMCID: PMC11126485 DOI: 10.1007/s00384-024-04651-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/17/2024] [Indexed: 05/26/2024]
Abstract
PURPOSE This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images. MATERIALS AND METHODS In total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group. RESULTS Approximately 49% of the patients were in the good response (GR) group based on mrTRG (73/148) and 26.9% based on pTRG (28/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87). CONCLUSION Radiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.
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Affiliation(s)
- Yong Dae Lee
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
- Department of Medicine, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
| | - Miri Seo
- Department of Medicine, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
| | - Sung Kyoung Moon
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
| | - Seong Jin Park
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea
| | - Myung-Won You
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea.
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Dou J, Jiang N, Zeng J, Wang S, Tian S, Shan S, Li Y, Xu Z, Lin X, Jin S, Dong J, Chen H. Novel 3D morphological characteristics for congenital biliary dilatation diagnosis: a case-control study. Int J Surg 2024; 110:2614-2624. [PMID: 38376858 DOI: 10.1097/js9.0000000000001204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Congenital biliary dilatation (CBD) necessitates the timely removal of dilated bile ducts. Accurate differentiation between CBD and secondary biliary dilatation (SBD) is crucial for treatment decisions, and identification of CBD with intrahepatic involvement is vital for surgical planning and supportive care. This study aimed to develop quantitative models based on bile duct morphology to distinguish CBD from SBD and further identify CBD with intrahepatic involvement. MATERIALS AND METHODS The retrospective study included 131 CBD and 209 SBD patients between December 2014 and December 2021 for model development, internal validation, and testing. A separate cohort of 15 CBD and 34 SBD patients between January 2022 and December 2022 was recruited for temporally-independent validation. Quantitative shape-based (Shape) and diameter-based (Diam) morphological characteristics of bile ducts were extracted to build a CBD diagnosis model to distinguish CBD from SBD and an intrahepatic involvement identification model to classify CBD with/without intrahepatic involvement. The diagnostic performance of the models was compared with that of experienced hepatobiliary surgeons. RESULTS The CBD diagnosis model using clinical, Shape, and Diam characteristics showed good performance with an AUROC of 0.942 (95% CI: 0.890-0.994), AUPRC of 0.917 (0.855-0.979), accuracy of 0.891, sensitivity of 0.950, and F1-score of 0.864. The model outperformed two experienced surgeons in accuracy, sensitivity, and F1-score. The intrahepatic involvement identification model using clinical, Shape, and Diam characteristics yielded outstanding performance with an AUROC of 0.944 (0.879-1.000), AUPRC of 0.982 (0.947-1.000), accuracy of 0.932, sensitivity of 0.971, and F1-score of 0.957. The models demonstrated generalizable performance on the temporally-independent validation cohort. CONCLUSIONS This study developed two robust quantitative models for distinguishing CBD from SBD and identifying CBD with intrahepatic involvement, respectively, based on morphological characteristics of the bile ducts, showing great potential in risk stratification and surgical planning of CBD.
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Affiliation(s)
- Jiaqi Dou
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Nan Jiang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jianping Zeng
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Siyuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Song Tian
- Philips Healthcare, Beijing, People's Republic of China
| | - Siqiao Shan
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Yuze Li
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Ziming Xu
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Xiaoqi Lin
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Shuo Jin
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Huijun Chen
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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He X, Ji J, Qdaisat A, Esteva FJ, Yeung SCJ. Long-term overall survival of patients who undergo breast-conserving therapy or mastectomy for early operable HER2-Positive breast cancer after preoperative systemic therapy: an observational cohort study. LANCET REGIONAL HEALTH. AMERICAS 2024; 32:100712. [PMID: 38495316 PMCID: PMC10943473 DOI: 10.1016/j.lana.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/19/2024]
Abstract
Background Understanding the survival outcomes associated with breast-conserving therapy (BCT) and mastectomy after preoperative systemic therapy (PST) enables clinicians to provide more personalized treatment recommendations. However, lack of firm survival benefit data limits the breast surgery choices of human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who receive PST. We sought to determine whether BCT or mastectomy after PST for early operable HER2-positive breast cancer is associated with better long-term survival outcomes and determine the degree to which PST response affects this association. Methods In this observational cohort study, we compared the long-term survival outcomes of BCT and mastectomy after PST for HER2-positive breast cancer and evaluated the impact of PST response on the relationship between breast surgery performed and survival outcomes. Our cohort included 625 patients with early operable HER2-positive breast cancer who received PST followed by BCT or mastectomy between January 1998 and October 2009. These patients also received standard postoperative radiation, trastuzumab, and endocrine therapy as indicated clinically. We used propensity score matching to assemble mastectomy and BCT cohorts with similar baseline characteristics and used Kaplan-Meier plots and Cox proportional hazards regression to detect associations between surgery types and outcomes. Furthermore, in this study, we analyzed the original data of 625 patients using the inverse probability of treatment weighting (IPTW) method to enhance the reliability of the comparison between the mastectomy and BCT cohorts by addressing potential confounding variables. Findings Propensity score matching yielded cohorts of 221 patients who received BCT and 221 patients who underwent mastectomy. At the median follow-up time of 9.9 years, compared with BCT, mastectomy was associated with worse overall survival (hazard ratio, 1.66; 95% confidence interval [CI]: 1.08-2.57; P = 0.02). In patients who had axillary lymph node pathological complete response, mastectomy was associated with worse overall survival before matching (hazard ratio, 2.17; 95% CI: 1.22-3.86; P < 0.01) and after matching (hazard ratio, 2.12; 95% CI: 1.15-3.89; P = 0.02). Among patients with pathological complete response in the breast, the survival results did not differ significantly between BCT and mastectomy patients. IPTW method validated that BCT offers better overall survival in patients who had axillary lymph node pathological complete response. Interpretation People with HER2-positive breast cancer who have already had PST are more likely to survive after BCT, especially if they get a pathological complete response in the axillary lymph nodes. These findings underscore the necessity for further investigation into how responses to PST can inform the choice of surgical intervention and the potential impact on overall survival. Such insights could lead to the development of innovative tools that support personalized surgical strategies in the management of breast cancer. Funding This work was supported by grants from the Nantong Science and Technology Project (JCZ2022079), Nantong Health Commission Project (QA2021031, MSZ2023040) and National Natural Science Foundation of China (No. 82394430).
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Affiliation(s)
- Xuexin He
- Department of Medical Oncology, Huashan Hospital of Fudan University, Shanghai, China
- Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiali Ji
- Department of Medical Oncology, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China
| | - Aiham Qdaisat
- Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Francisco J. Esteva
- Division of Hematology/Oncology, Northwell Health Cancer Institute at Lenox Hill Hospital, New York, NY, USA
| | - Sai-Ching J. Yeung
- Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Lai J, Chen Z, Liu J, Zhu C, Huang H, Yi Y, Cai G, Liao N. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. Int J Surg 2024; 110:2162-2177. [PMID: 38215256 PMCID: PMC11019980 DOI: 10.1097/js9.0000000000001082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/27/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. METHODS In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. RESULTS For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). CONCLUSION Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.
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Affiliation(s)
- Jianguo Lai
- Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong
| | - Zijun Chen
- The Second Clinical School of Southern Medical University, Guangzhou
| | - Jie Liu
- Department of Breast Cancer, Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University
| | - Chao Zhu
- Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University
| | - Haoxuan Huang
- Department of Urology, Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Ying Yi
- Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong
| | - Gengxi Cai
- Department of Breast Surgery, The First People’s Hospital of Foshan, Foshan, Guangdong
| | - Ning Liao
- Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong
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Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schäfgen B, Togawa R, Killinger K, Heil J, Pfob A, Golatta M. Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer? Eur Radiol 2024; 34:2560-2573. [PMID: 37707548 PMCID: PMC10957593 DOI: 10.1007/s00330-023-10238-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/17/2023] [Accepted: 08/01/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVES Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy. METHODS We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST. We developed a support vector machine algorithm using pretreatment ultrasound and tomosynthesis radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measures were area under the curve (AUC) and false-negative rate (FNR). RESULTS We included 720 patients, 504 in the development set and 216 in the validation set. Median age was 51.6 years and 33.6% (242 of 720) achieved pCR. The addition of radiomics features significantly improved the performance of the algorithm (AUC 0.72 to 0.81; p = 0.007). The FNR of the multi-modal radiomics and clinical algorithm was 6.7% (10 of 150 with missed residual cancer). Surface/volume ratio at tomosynthesis and peritumoral entropy characteristics at ultrasound were the most relevant radiomics. Hormonal receptors and HER-2 status were the most important clinical predictors. CONCLUSION A multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features may aid in predicting residual cancer after NAST. Pending prospective validation, this may facilitate individually tailored NAST regimens. CLINICAL RELEVANCE STATEMENT Multi-modal radiomics using pretreatment ultrasound and tomosynthesis showed significant improvement in assessing response to NAST compared to an algorithm using clinical variables only. Further prospective validation of our findings seems warranted to enable individualized predictions of NAST outcomes. KEY POINTS • We proposed a multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features to predict response to neoadjuvant breast cancer treatment. • Compared with the clinical algorithm, the AUC of this integrative algorithm is significantly higher. • Used prior to the initiative of therapy, our algorithm can identify patients who will experience pathologic complete response following neoadjuvant therapy with a high negative predictive value.
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Affiliation(s)
- Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Chris Sidey-Gibbons
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Juliane Nees
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Fabian Riedel
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Benedikt Schäfgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Riku Togawa
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Kristina Killinger
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA.
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
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Zheng G, Peng J, Shu Z, Jin H, Han L, Yuan Z, Qin X, Hou J, He X, Gong X. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms. J Cancer Res Clin Oncol 2024; 150:147. [PMID: 38512406 PMCID: PMC10957588 DOI: 10.1007/s00432-024-05680-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 03/03/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT. METHODS A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR. RESULTS For signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model. CONCLUSION The addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.
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Affiliation(s)
- Guangying Zheng
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
- Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Jiaxuan Peng
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Hui Jin
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Lu Han
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Zhongyu Yuan
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Xue Qin
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Jie Hou
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Xiangyang Gong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.
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Zhang L, Gao Q, Dou Y, Cheng T, Xia Y, Li H, Gao S. Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram. Front Oncol 2024; 14:1345576. [PMID: 38577327 PMCID: PMC10991753 DOI: 10.3389/fonc.2024.1345576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
Objective To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma. Methods A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA). Results Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram. Conclusion The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.
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Affiliation(s)
- Lu Zhang
- Department of Medical Imaging, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qiuru Gao
- Department of Radiology, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, China
| | - Yincong Dou
- Department of Medical Imaging, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tianming Cheng
- Department of Medical Imaging, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuwei Xia
- Artificial Intelligence Technology, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Hailiang Li
- Department of Radiology, Henan Provincial Cancer Hospital, Zhengzhou, Henan, China
| | - Song Gao
- Department of Orthopedics, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Qi H, Hou Y, Zheng Z, Zheng M, Qiao Q, Wang Z, Sun X, Xing L. Clinical characteristics and MRI based radiomics nomograms can predict iPFS and short-term efficacy of third-generation EGFR-TKI in EGFR-mutated lung adenocarcinoma with brain metastases. BMC Cancer 2024; 24:362. [PMID: 38515096 PMCID: PMC10956298 DOI: 10.1186/s12885-024-12121-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/13/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Predicting short-term efficacy and intracranial progression-free survival (iPFS) in epidermal growth factor receptor gene mutated (EGFR-mutated) lung adenocarcinoma patients with brain metastases who receive third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapy was of great significance for individualized treatment. We aimed to construct and validate nomograms based on clinical characteristics and magnetic resonance imaging (MRI) radiomics for predicting short-term efficacy and intracranial progression free survival (iPFS) of third-generation EGFR-TKI in EGFR-mutated lung adenocarcinoma patients with brain metastases. METHODS One hundred ninety-four EGFR-mutated lung adenocarcinoma patients with brain metastases who received third-generation EGFR-TKI treatment were included in this study from January 1, 2017 to March 1, 2023. Patients were randomly divided into training cohort and validation cohort in a ratio of 5:3. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) regression. Logistic regression analysis and Cox proportional hazards regression analysis were used to screen clinical risk factors. Single clinical (C), single radiomics (R), and combined (C + R) nomograms were constructed in short-term efficacy predicting model and iPFS predicting model, respectively. Prediction effectiveness of nomograms were evaluated by calibration curves, Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to compare the iPFS of high and low iPFS rad-score patients in the predictive iPFS R model and to compare the iPFS of high-risk and low-risk patients in the predictive iPFS C + R model. RESULTS Overall response rate (ORR) was 71.1%, disease control rate (DCR) was 91.8% and median iPFS was 12.67 months (7.88-20.26, interquartile range [IQR]). There were significant differences in iPFS between patients with high and low iPFS rad-scores, as well as between high-risk and low-risk patients. In short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.867 (0.835-0.900, 95%CI) and 0.803 (0.753-0.854, 95%CI), while in iPFS model, the C-indexes were 0.901 (0.874-0.929, 95%CI) and 0.753 (0.713-0.793, 95%CI). CONCLUSIONS The third-generation EGFR-TKI showed significant efficacy in EGFR-mutated lung adenocarcinoma patients with brain metastases, and the combined line plot of C + R can be utilized to predict short-term efficacy and iPFS.
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Affiliation(s)
- Haoran Qi
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Yichen Hou
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Zhonghang Zheng
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Mei Zheng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Qiang Qiao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Zihao Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, China.
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Liu J, Leng X, Liu W, Ma Y, Qiu L, Zumureti T, Zhang H, Mila Y. An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer. Front Oncol 2024; 14:1285511. [PMID: 38500656 PMCID: PMC10946249 DOI: 10.3389/fonc.2024.1285511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/20/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.
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Affiliation(s)
- Jinhui Liu
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Xiaoling Leng
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Wen Liu
- Artificial Intelligence and Smart Mine Engineering Technology Center, Xinjiang Institute of Engineering, Urumqi, China
| | - Yuexin Ma
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lin Qiu
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Tuerhong Zumureti
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Haijian Zhang
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yeerlan Mila
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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You J, Huang Y, Ouyang L, Zhang X, Chen P, Wu X, Jin Z, Shen H, Zhang L, Chen Q, Pei S, Zhang B, Zhang S. Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study. EClinicalMedicine 2024; 69:102499. [PMID: 38440400 PMCID: PMC10909626 DOI: 10.1016/j.eclinm.2024.102499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 03/06/2024] Open
Abstract
Background Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).
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Affiliation(s)
- Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yue Huang
- Department of Ultrasound, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lizhu Ouyang
- Department of Ultrasound, Shunde Hospital of Southern Medical University, Foshan, Guangdong, China
| | - Xiao Zhang
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Pei Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xuewei Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Hui Shen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shufang Pei
- Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Ma J, Chen K, Li S, Zhu L, Yu Y, Li J, Ma J, Ouyang J, Wu Z, Tan Y, He Z, Liu H, Pan Z, Li H, Liu Q, Song E. MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study. Eur Radiol 2024; 34:1774-1789. [PMID: 37658888 DOI: 10.1007/s00330-023-10144-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/18/2023] [Accepted: 07/08/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.
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Affiliation(s)
- Jiafan Ma
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Shunrong Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Liling Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yunfang Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Jingwu Li
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ma
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ouyang
- Department of Breast Surgery, Tungwah Hospital, Sun Yat-sen University, Dongguan, 523413, China
| | - Zhuo Wu
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yujie Tan
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zifan He
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haiqing Liu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zhilong Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Yang S, Zhang W, Liu C, Li C, Hua K. Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer. Ann Med Surg (Lond) 2024; 86:805-810. [PMID: 38333288 PMCID: PMC10849352 DOI: 10.1097/ms9.0000000000001412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/09/2023] [Indexed: 02/10/2024] Open
Abstract
Objective Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomics features that could distinguish metastatic lymph nodes (LNs) from hypermetabolic benign LNs, in addition to conventional indicators. Methods PET/CT images of 106 patients with early-stage cervical cancer from 2019 to 2021 were retrospectively analyzed. The tumor lesions and LN regions of PET/CT images were outlined with SeeIt, and then radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select features. The final selected radiomics features of LNs were used as predictors to construct a machine learning model to predict LN metastasis. Results The authors determined two morphological coefficient characteristics of cervical lesions (shape - major axis length and shape - mesh volume), one first order characteristics of LNs (first order - 10 percentile) and two gray-level co-occurrence matrix (GLCM) characteristics of LNs (GLCM - id and GLCM - inverse variance) were closely related to LN metastasis. Finally, a neural network was constructed based on the radiomic features of the LNs. The area under the curve of receiver operating characteristic (AUC-ROC) of the model was 0.983 in the training set and 0.860 in the test set. Conclusion The authors constructed and demonstrated a neural network based on radiomics features of PET/CT to evaluate the risk of single LN metastasis in early-stage cervical cancer.
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Affiliation(s)
- Shimin Yang
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Wenrui Zhang
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunli Liu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunbo Li
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
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Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 PMCID: PMC11362966 DOI: 10.1016/j.ejca.2023.113504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
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Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
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Yu Y, Wang Z, Wang Q, Su X, Li Z, Wang R, Guo T, Gao W, Wang H, Zhang B. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Front Oncol 2024; 13:1249339. [PMID: 38357424 PMCID: PMC10865896 DOI: 10.3389/fonc.2023.1249339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/16/2024] Open
Abstract
Purpose To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. Method MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. Results The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. Conclusion We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.
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Affiliation(s)
- Yimiao Yu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhibo Wang
- Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qi Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaohui Su
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhenghao Li
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruifeng Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianhui Guo
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen Gao
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiji Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Biyuan Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Xu N, Guo X, Ouyang Z, Ran F, Li Q, Duan X, Zhu Y, Niu X, Liao C, Yang J. Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer. Heliyon 2024; 10:e24371. [PMID: 38298695 PMCID: PMC10827766 DOI: 10.1016/j.heliyon.2024.e24371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose The aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients. Methods We retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results The radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness. Conclusions The results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.
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Affiliation(s)
- Nan Xu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xiaobin Guo
- Department of Radiology, Fuwai Central China Cardiovascular Hospital, Fuwai Road, Zhengzhou, Henan, 461464, PR China
| | - Zhiqiang Ouyang
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Fengming Ran
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Qinqing Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xirui Duan
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Yu Zhu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xiaofeng Niu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Chengde Liao
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Jun Yang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
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Zhang MQ, Liu XP, Du Y, Zha HL, Zha XM, Wang J, Liu XA, Wang SJ, Zou QG, Zhang JL, Li CY. Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI. Br J Radiol 2024; 97:228-236. [PMID: 38263817 PMCID: PMC11027305 DOI: 10.1093/bjr/tqad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/01/2023] [Accepted: 10/31/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). METHODS A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. RESULTS Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. CONCLUSION A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. ADVANCES IN KNOWLEDGE A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.
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Affiliation(s)
- Man-Qi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xin-Pei Liu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yu Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xiao-Ming Zha
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Jue Wang
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xiao-An Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Shou-Ju Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qi-Gui Zou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Jiu-Lou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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Janssen LM, Janse MHA, Penning de Vries BBL, van der Velden BHM, Wolters-van der Ben EJM, van den Bosch SM, Sartori A, Jovelet C, Agterof MJ, Ten Bokkel Huinink D, Bouman-Wammes EW, van Diest PJ, van der Wall E, Elias SG, Gilhuijs KGA. Predicting response to neoadjuvant chemotherapy with liquid biopsies and multiparametric MRI in patients with breast cancer. NPJ Breast Cancer 2024; 10:10. [PMID: 38245552 PMCID: PMC10799888 DOI: 10.1038/s41523-024-00611-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 12/07/2023] [Indexed: 01/22/2024] Open
Abstract
Accurate prediction of response to neoadjuvant chemotherapy (NAC) can help tailor treatment to individual patients' needs. Little is known about the combination of liquid biopsies and computer extracted features from multiparametric magnetic resonance imaging (MRI) for the prediction of NAC response in breast cancer. Here, we report on a prospective study with the aim to explore the predictive potential of this combination in adjunct to standard clinical and pathological information before, during and after NAC. The study was performed in four Dutch hospitals. Patients without metastases treated with NAC underwent 3 T multiparametric MRI scans before, during and after NAC. Liquid biopsies were obtained before every chemotherapy cycle and before surgery. Prediction models were developed using penalized linear regression to forecast residual cancer burden after NAC and evaluated for pathologic complete response (pCR) using leave-one-out-cross-validation (LOOCV). Sixty-one patients were included. Twenty-three patients (38%) achieved pCR. Most prediction models yielded the highest estimated LOOCV area under the curve (AUC) at the post-treatment timepoint. A clinical-only model including tumor grade, nodal status and receptor subtype yielded an estimated LOOCV AUC for pCR of 0.76, which increased to 0.82 by incorporating post-treatment radiological MRI assessment (i.e., the "clinical-radiological" model). The estimated LOOCV AUC was 0.84 after incorporation of computer-extracted MRI features, and 0.85 when liquid biopsy information was added instead of the radiological MRI assessment. Adding liquid biopsy information to the clinical-radiological resulted in an estimated LOOCV AUC of 0.86. In conclusion, inclusion of liquid biopsy-derived markers in clinical-radiological prediction models may have potential to improve prediction of pCR after NAC in breast cancer.
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Affiliation(s)
- L M Janssen
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - M H A Janse
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - B B L Penning de Vries
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - B H M van der Velden
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | | | - A Sartori
- Agena Bioscience GmbH, Hamburg, Germany
| | - C Jovelet
- Stilla Technologies, Villejuif, France
| | - M J Agterof
- Department of Medical Oncology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - D Ten Bokkel Huinink
- Department of Medical Oncology, Alexander Monro Hospital, Bilthoven, The Netherlands
| | - E W Bouman-Wammes
- Department of Medical Oncology, Albert Schweitzer Hospital, Dordrecht, The Netherlands
| | - P J van Diest
- Department of Pathology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - E van der Wall
- Department of Medical Oncology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - S G Elias
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - K G A Gilhuijs
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
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Hu C, Qiao X, Xu Z, Zhang Z, Zhang X. Machine learning-based CT texture analysis in the differentiation of testicular masses. Front Oncol 2024; 13:1284040. [PMID: 38293700 PMCID: PMC10826395 DOI: 10.3389/fonc.2023.1284040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/26/2023] [Indexed: 02/01/2024] Open
Abstract
Purpose To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively. Methods We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers. Results 985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively. Conclusion Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.
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Affiliation(s)
- Can Hu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Urology, Suzhou Xiangcheng People’s Hospital, Suzhou, China
| | - Xiaomeng Qiao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Zhenyu Xu
- Department of Urology, The Affiliated Hospital of Nanjing University of Traditional Chinese Medicine: Traditional Chinese Medicine Hospital of Kunshan, Kunshan, China
| | - Zhiyu Zhang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xuefeng Zhang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Zhong H, Wang T, Hou M, Liu X, Tian Y, Cao S, Li Z, Han Z, Liu G, Sun Y, Meng C, Li Y, Jiang Y, Ji Q, Hao D, Liu Z, Zhou Y. Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. Ann Surg Oncol 2024; 31:421-432. [PMID: 37925653 DOI: 10.1245/s10434-023-14424-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/26/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.
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Affiliation(s)
- Hao Zhong
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Mingyu Hou
- Department of Pathology, Qingdao University Affiliated Qingdao Women and Children's Hospital, Qingdao, Shandong, People's Republic of China
| | - Xiaodong Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yulong Tian
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Shougen Cao
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zequn Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zhenlong Han
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Gan Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yuqi Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Cheng Meng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yujun Li
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanxia Jiang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Qinglian Ji
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zimin Liu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanbing Zhou
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
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Gullo RL, Partridge SC, Shin HJ, Thakur SB, Pinker K. Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring. AJR Am J Roentgenol 2024; 222:e2329933. [PMID: 37850579 PMCID: PMC11196747 DOI: 10.2214/ajr.23.29933] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA, USA 98109, USA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Du Y, Cai M, Zha H, Chen B, Gu J, Zhang M, Liu W, Liu X, Liu X, Zong M, Li C. Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study. Eur Radiol 2024; 34:136-148. [PMID: 37518678 DOI: 10.1007/s00330-023-09995-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 04/20/2023] [Accepted: 06/02/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVES To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). MATERIALS AND METHODS In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration. RESULTS The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84-0.91), 0.89 (95% CI, 0.84-0.94) and 0.95 (95% CI, 0.92-0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84-0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful. CONCLUSIONS The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI. CLINICAL RELEVANCE STATEMENT The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer. KEY POINTS • The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone. • The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets). • A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).
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Affiliation(s)
- Yu Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Mengjun Cai
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Hailing Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Baoding Chen
- Department of Ultrasound, Affiliated Hospital of Jiangsu University, 438 Jiefang Road, Zhenjiang, 212050, China
| | - Jun Gu
- Department of Ultrasound, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215002, China
| | - Manqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Wei Liu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Xinpei Liu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
| | - Cuiying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Huang Y, Cao Y, Hu X, Lan X, Chen H, Tang S, Li L, Cheng Y, Gong X, Wang W, Jiang F, Yin T, Wang X, Zhang J. Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study. J Magn Reson Imaging 2023. [PMID: 38109316 DOI: 10.1002/jmri.29188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. PURPOSE To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. STUDY TYPE Multicenter, longitudinal. POPULATION Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. FIELD STRENGTH/SEQUENCE Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence. ASSESSMENT Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. STATISTICAL TESTS Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. RESULTS In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). DATA CONCLUSION The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Yao Huang
- School of Medicine, Chongqing University, Chongqing, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Ying Cao
- School of Medicine, Chongqing University, Chongqing, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Huifang Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Sun Tang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Yue Cheng
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Xueqin Gong
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Wei Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Fujie Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
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