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Fanizzi A, Latorre A, Bavaro DA, Bove S, Comes MC, Di Benedetto EF, Fadda F, La Forgia D, Giotta F, Palmiotti G, Petruzzellis N, Rinaldi L, Rizzo A, Lorusso V, Massafra R. Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach. Cancer Med 2023; 12:20663-20669. [PMID: 37905688 PMCID: PMC10709715 DOI: 10.1002/cam4.6512] [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: 10/17/2022] [Revised: 07/27/2023] [Accepted: 08/29/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features. METHOD First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. RESULTS The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. CONCLUSION Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.
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Affiliation(s)
| | | | | | - Samantha Bove
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | | | | | | | | | | | | | - Lucia Rinaldi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
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Zhou X, Yu Y, Feng Y, Ding G, Liu P, Liu L, Ren W, Zhu Y, Cao W. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiat Oncol 2023; 18:175. [PMID: 37891611 PMCID: PMC10612200 DOI: 10.1186/s13014-023-02352-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: 02/14/2023] [Accepted: 09/13/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC. METHODS This retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital. RESULTS The deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism. CONCLUSIONS The self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.
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Affiliation(s)
- Xuezhi Zhou
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yi Yu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yanru Feng
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Guojun Ding
- Department of Radiology, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Luying Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Wenjie Ren
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China.
| | - Yuan Zhu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China.
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Long B, Zhang H, Zhang H, Chen W, Sun Y, Tang R, Lin Y, Fu Q, Yang X, Cui L, Wang K. Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study. BMC Med 2023; 21:405. [PMID: 37880716 PMCID: PMC10601110 DOI: 10.1186/s12916-023-03099-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses. METHODS Between January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years' experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results. RESULTS In the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors. CONCLUSIONS The proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses.
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Affiliation(s)
- Bin Long
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
- Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China
| | - Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Han Zhang
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Wen Chen
- Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China
| | - Yang Sun
- Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China
| | - Rui Tang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
- Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China
| | - Yuxuan Lin
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qiang Fu
- Department of Ultrasound, Beijing Civil Aviation General Hospital, Beijing, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ligang Cui
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
- Department of Diagnostic Ultrasound, Peking University Third Hospital, Beijing, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery. BMC Musculoskelet Disord 2023; 24:791. [PMID: 37803313 PMCID: PMC10557221 DOI: 10.1186/s12891-023-06911-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. METHODS We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). RESULTS The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. CONCLUSIONS Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany.
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria.
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria
- Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
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Su GH, Xiao Y, You C, Zheng RC, Zhao S, Sun SY, Zhou JY, Lin LY, Wang H, Shao ZM, Gu YJ, Jiang YZ. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. SCIENCE ADVANCES 2023; 9:eadf0837. [PMID: 37801493 PMCID: PMC10558123 DOI: 10.1126/sciadv.adf0837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 09/06/2023] [Indexed: 10/08/2023]
Abstract
Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.
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Affiliation(s)
- Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and 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 and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center and 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 and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [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: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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Lee J, Yoo SK, Kim K, Lee BM, Park VY, Kim JS, Kim YB. Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncol Lett 2023; 26:422. [PMID: 37664669 PMCID: PMC10472028 DOI: 10.3892/ol.2023.14008] [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: 05/03/2023] [Accepted: 07/19/2023] [Indexed: 09/05/2023] Open
Abstract
Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)-based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Data from patients with localized breast cancer that underwent preoperative MRI between January 2013 and December 2017 were collected. Propensity score matching (PSM) was performed to adjust for clinical factors between patients with and without LRR. Radiomics features were obtained from T2-weighted with and without fat-suppressed MRI and contrast-enhanced T1-weighted with fat-suppressed MRI. In the present study five ML models were designed, three base models (support vector machine, random forest, and logistic regression) and two ensemble models (voting model and stacking model) composed of the three base models, and the performance of each base model was compared with the stacking model. After PSM, 28 patients with LRR and 86 patients without LRR were included. Of these 114 patients, 80 patients were randomly selected to train the models, and the remaining 34 patients were used to evaluate the performance of the trained models. In total, 5,064 features were obtained from each patient, and 47-51 features were selected by applying variance threshold and least absolute shrinkage and selection operator. The stacking model demonstrated superior performance in area under the receiver operating characteristic curve (AUC), with an AUC of 0.78 compared to a range of 0.61 to 0.70 for the other models. An external validation study to investigate the efficacy of the stacking model of the present study was initiated and is still ongoing (Korean Radiation Oncology Group 2206).
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Affiliation(s)
- Joongyo Lee
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 06273, Republic of Korea
| | - Sang Kyun Yoo
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Kangpyo Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Yonsei University Health System, Seoul 06351, Republic of Korea
| | - Byung Min Lee
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Yonsei University Health System, Uijeongbu, Gyeonggi 11765, Republic of Korea
| | - Vivian Youngjean Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
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Jiang W, Du S, Gao S, Xie L, Xie Z, Wang M, Peng C, Shi J, Zhang L. Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response. Insights Imaging 2023; 14:162. [PMID: 37775610 PMCID: PMC10541382 DOI: 10.1186/s13244-023-01492-9] [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: 05/02/2023] [Accepted: 07/25/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups. METHODS Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated. RESULTS Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71). CONCLUSIONS The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. CRITICAL RELEVANCE STATEMENT The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. KEY POINTS • The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.
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Affiliation(s)
- Wenhong Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Siyao Du
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Si Gao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, China
| | - Zichuan Xie
- Guangzhou institute of technology, Xidian University, Guangzhou, China
| | - Mengfan Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Can Peng
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Jing Shi
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.
| | - Lina Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
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Jiang M, Li CL, Luo XM, Chuan ZR, Chen RX, Jin CY. An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading. Acad Radiol 2023; 30:1794-1804. [PMID: 36609032 DOI: 10.1016/j.acra.2022.12.014] [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: 10/13/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors. MATERIALS AND METHODS Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS). RESULTS The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10-4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13-5.56, p = 0.023) and NHG 2 ER+LN- (HR 5.72, 1.24-26.44, p = 0.025) subgroups, and in specific treatment contexts. CONCLUSION The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions.
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Affiliation(s)
- Meng Jiang
- Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
| | - Chang-Li Li
- Department of FSTC Clinic of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xiao-Mao Luo
- Department of Oncology, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhi-Rui Chuan
- Department of Oncology, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Rui-Xue Chen
- Department of Oncology, Wuchang Hospital, Wuhan, China
| | - Chao-Ying Jin
- Department of Oncology, Taizhou Hospital of Zhejiang Province, Taizhou, China
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Jiang H, Guo W, Yu Z, Lin X, Zhang M, Jiang H, Zhang H, Sun Z, Li J, Yu Y, Zhao S, Hu H. A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer. Acad Radiol 2023; 30 Suppl 1:S185-S198. [PMID: 37394412 DOI: 10.1016/j.acra.2023.04.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/18/2023] [Accepted: 04/23/2023] [Indexed: 07/04/2023]
Abstract
RATIONALE AND OBJECTIVES To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters. MATERIALS AND METHODS Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed. RESULTS The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model. CONCLUSION Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.
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Affiliation(s)
- Hao Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Wei Guo
- Department of PET/CT-MRI, Harbin Medical University Cancer Hospital, Harbin, China (W.G.)
| | - Zhuo Yu
- Huiying Medical Technology (Beijing) Co, Beijing, China (Z.Y.)
| | - Xue Lin
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Mingyu Zhang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Affiliated to Capital Medical University, Beijing, China (M.Z.)
| | - Huijie Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.).
| | - Hongxia Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (H.Z., Y.Y.)
| | - Zhongqi Sun
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Jinping Li
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Yanyan Yu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (H.Z., Y.Y.)
| | - Sheng Zhao
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Hongbo Hu
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
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Song SE, Woo OH, Cho Y, Cho KR, Park KH, Kim JW. Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI. Acad Radiol 2023; 30 Suppl 2:S25-S37. [PMID: 37331865 DOI: 10.1016/j.acra.2023.05.025] [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: 04/24/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC). MATERIALS AND METHODS Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N = 27) and non-ALNM (n = 59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method. RESULTS Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio = 1.33, P = 0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features. CONCLUSION A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea (O.H.W.).
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Kyong Hwa Park
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (K.H.P., J.W.K.)
| | - Ju Won Kim
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (K.H.P., J.W.K.)
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Li J, Zhang HL, Yin HK, Zhang HK, Wang Y, Xu SN, Ma F, Gao JB, Li HL, Qu JR. Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. J Magn Reson Imaging 2023; 58:907-923. [PMID: 36527425 DOI: 10.1002/jmri.28570] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear. PURPOSE To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. STUDY TYPE Retrospective. POPULATION Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. FIELD/SEQUENCE T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. ASSESSMENT Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. STATISTICAL TESTS A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant. RESULTS CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). DATA CONCLUSIONS mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hui-Ling Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hong-Kun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hong-Kai Zhang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Shu-Ning Xu
- Department of Digestive Oncology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Jin-Rong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
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Chen C, Han Q, Ren H, Wu S, Li Y, Guo J, Li X, Liu X, Li C, Tian Y. Multiparametric MRI-based model for prediction of local progression of hepatocellular carcinoma after thermal ablation. Cancer Med 2023; 12:17529-17540. [PMID: 37694337 PMCID: PMC10524055 DOI: 10.1002/cam4.6277] [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: 02/06/2023] [Revised: 06/06/2023] [Accepted: 06/11/2023] [Indexed: 09/12/2023] Open
Abstract
PURPOSE To develop a deep learning radiomics of multiparametric magnetic resonance imaging (DLRMM)-based model that incorporates preoperative and postoperative signatures for prediction of local tumor progression (LTP) after thermal ablation (TA) in hepatocellular carcinoma (HCC). METHODS From May 2017 to October 2021, 417 eligible patients with HCC were retrospectively enrolled from three hospitals (one primary cohort [PC, n = 189] and two external test cohorts [ETCs][n = 135, 93]). DLRMM features were extracted from T1WI + C, T2WI, and DWI using ResNet18 model. An integrative model incorporating the DLRMM signature with clinicopathologic variables were further built to LTP risk stratification. The performance of these models were compared by areas under receiver operating characteristic curve (AUC) using DeLong test. RESULTS A total of 1668 subsequences and 31,536 multiparametric MRI slice including T1WI, T2WI, and DWI were collected simultaneously. The DLRMM signatures were extracted from tumor and ablation zone, respectively. Ablative margin, multiple tumors, and tumor abutting major vessels were regarded as risk factors for LTP in clinical model. The AUC of DLRMM model were 0.864 in PC, 0.843 in ETC1, and 0.858 in ETC2, which was higher significantly than those in clinical model (p < 0.001). After integrating clinical variable, DLRMM model obtained significant improvement with AUC of 0.870-0.869 in three cohorts (all, p < 0.001), which can provide the risk stratification for overall survival of HCC patients. CONCLUSIONS The DLRMM model is essential to identify LTP risk of HCC patients who underwent TA and may potentially benefit personalized decision-making.
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Affiliation(s)
- Chao Chen
- Department of Minimal Invasive Intervention RadiologyGanzhou People's HospitalGanzhouChina
| | - Qiuying Han
- Department of CardiologyThe First Affiliated Hospital of Jinan universityGuanghzhouChina
| | - He Ren
- Department of UltrasoundThe Six Medical Center of Chinese PLA General HospitalBeijingChina
| | - Siyi Wu
- Department of Interventional Radiology and Vascular SurgeryThe First Affiliated Hospital of Jinan UniversityGuanghzhouChina
| | - Yangyang Li
- Department of Interventional Radiology and Vascular SurgeryThe First Affiliated Hospital of Jinan UniversityGuanghzhouChina
| | - Jiandong Guo
- Department of Interventional Radiology and Vascular SurgeryThe First Affiliated Hospital of Jinan UniversityGuanghzhouChina
| | - Xinghai Li
- Department of Minimal Invasive Intervention RadiologyGanzhou People's HospitalGanzhouChina
| | - Xiang Liu
- Department of Minimal Invasive Intervention RadiologyGanzhou People's HospitalGanzhouChina
| | - Chengzhi Li
- Department of Interventional Radiology and Vascular SurgeryThe First Affiliated Hospital of Jinan UniversityGuanghzhouChina
| | - Yunfei Tian
- Department of Minimal Invasive Intervention RadiologyGanzhou People's HospitalGanzhouChina
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Lu D, Yan Y, Jiang M, Sun S, Jiang H, Lu Y, Zhang W, Zhou X. Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1173090. [PMID: 37664048 PMCID: PMC10469000 DOI: 10.3389/fonc.2023.1173090] [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: 02/24/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.
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Affiliation(s)
- Dongmei Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yuke Yan
- The Second Department of General Surgery, Gansu Provincial Hospital, Lanzhou, China
| | - Min Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Shaoqin Sun
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Haifeng Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yashan Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Xing Zhou
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
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Li Z, Yu J, Li Y, Liu Y, Zhang M, Yang H, Du Y. Preoperative Radiomics Nomogram Based on CT Image Predicts Recurrence-Free Survival After Surgical Resection of Hepatocellular Carcinoma. Acad Radiol 2023; 30:1531-1543. [PMID: 36653278 DOI: 10.1016/j.acra.2022.12.039] [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: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 01/19/2023]
Abstract
RATIONALE AND OBJECTIVES To construct preoperative models based on CT radiomics, radiologic and clinical features to predict recurrence-free survival (RFS) after liver resection (LR) of BCLC 0 to B stage hepatocellular carcinoma (HCC) and to classify the prognosis. MATERIALS AND METHODS This study retrospectively analyzed 161 HCC patients who underwent radical LR. Two methods, the least absolute shrinkage and selection operator and random survival forest analysis, were performed for radiomics signature (RS) construction. Univariate and multivariate stepwise Cox regression analyses were performed to establish a combined nomogram (RCN) of RS and clinical parameters and a clinical nomogram (CN). The performance of the models was assessed comprehensively using Harrell's concordance index (C-index), the calibration curve, and decision curve analysis. The discrimination accuracy of the models was compared using integrated discrimination improvement index (IDI). The risk stratification effect was assessed with Kaplan-Meier survival analysis and subgroup analysis. RESULTS The RCN achieved a C-index of 0.792/0.758 in the training/validation set, which was higher than the CN, RS, and BCLC stage system. The discriminatory accuracy of the RCN was improved when compared to the CN, RS, and BCLC staging systems (IDI > 0). Decision curve analysis reflected the clinical net benefit of the RCN. The RCN allows risk stratification of patients in different clinical subgroups. CONCLUSION The integrated model combining RS and clinical factors can more effectively predict RFS after LR of BCLC 0 to B stage HCC patients and can effectively stratify the prognostic risk.
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Affiliation(s)
- Zeyong Li
- Department of Radiology, Bishan Hospital of Chongqing Medical University, Bishan, Chongqing, China
| | - Jialin Yu
- Department of Radiology, Xinqiao Hospital, Army Medical University (Third Military Medical University), Shapingba, Chongqing, China
| | - Yehan Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Nanchong, Sichuan, China, 637000
| | - Ying Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Nanchong, Sichuan, China, 637000
| | - Manjing Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Nanchong, Sichuan, China, 637000
| | - Hanfeng Yang
- Department of Radiology and Interventional Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Yong Du
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 Maoyuan South Road, Nanchong, Sichuan, China, 637000.
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Wang H, van der Velden BHM, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts. Radiology 2023; 308:e222841. [PMID: 37552061 DOI: 10.1148/radiol.222841] [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: 08/09/2023]
Abstract
Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.
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Affiliation(s)
- Hui Wang
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Bas H M van der Velden
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Erik Verburg
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Marije F Bakker
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Ruud M Pijnappel
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Wouter B Veldhuis
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Carla H van Gils
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Kenneth G A Gilhuijs
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
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Zeng Q, Ke M, Zhong L, Zhou Y, Zhu X, He C, Liu L. Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy. Acad Radiol 2023; 30:1638-1647. [PMID: 36564256 DOI: 10.1016/j.acra.2022.11.006] [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: 09/28/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics at baseline and after two cycles of neoadjuvant therapy (NAT) and associated longitudinal changes for early prediction of the NAT response in patients with breast cancer. MATERIALS AND METHODS One hundred seventeen patients with breast cancer who underwent DCE-MRI before NAT and after two cycles of NAT from April 2019 to November 2021 were enrolled retrospectively. Patients were randomly divided into a training set (n = 81) and a test set (n = 36) at a ratio of 7:3. Clinical-pathological data and the relative tumor maximum diameter regression value (diameter%) were also collected. A total of 851 radiomic features were extracted from the phase with the most pronounced tumor enhancement on DCE-MRI T1 imaging acquired both pre- and post-treatment. Delta and delta% radiomics features were also calculated. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select features, and a logistic regression model was used to calculate pre-NAT, early-NAT, delta, and delta% radscores and then select among four radscores to build a Fusion radiomics model. The final clinical-radiomics model was constructed by combining fusion radscores and clinical-pathological variables. The discrimination and clinical utility of the models were further evaluated and compared. RESULTS The area under the curve (AUC) values of the fusion radiomics model based on pre-NAT, Delta, and Delta% radscores were 0.868 of 0.825. The clinical-radiomics model integrating Fusion radscores and clinical-pathological variables achieved AUC values of 0.920 of 0.884, which were higher than those of the clinical model constructed by AUC values (0.858/0.831), although no significant improvement was observed in the test set (Delong test, p = 0.196). Decision curve analysis (DCA) showed that the clinical-radiomics model demonstrated more clinical utility than the clinical model. CONCLUSION DCE-MRI-based radiomics features may have potential for pathological complete response (pCR) prediction in the early phase of NAT. By combining radiomics features and clinical-pathological characteristics, higher diagnostic performance can be achieved.
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Affiliation(s)
- Qiao Zeng
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Mengmeng Ke
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Linhua Zhong
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Xuechao Zhu
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Chongwu He
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.).
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Li H, Cai S, Deng L, Xiao Z, Guo Q, Qiang J, Gong J, Gu Y, Liu Z. Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram. Eur Radiol 2023; 33:5298-5308. [PMID: 36995415 DOI: 10.1007/s00330-023-09552-w] [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] [Received: 05/13/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.
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Affiliation(s)
- Haiming Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
| | - Lin Deng
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Zebin Xiao
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qinhao Guo
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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Yu FH, Miao SM, Li CY, Hang J, Deng J, Ye XH, Liu Y. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2023; 33:5634-5644. [PMID: 36976336 DOI: 10.1007/s00330-023-09555-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 02/09/2023] [Accepted: 02/19/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.
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Affiliation(s)
- Fei-Hong Yu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shu-Mei Miao
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Hang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin-Hua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
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Hao X, Xu H, Zhao N, Yu T, Hamalainen T, Cong F. Predicting pathological complete response based on weakly and semi-supervised joint learning from breast cancer MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083773 DOI: 10.1109/embc40787.2023.10340081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Neoadjuvant chemotherapy (NAC) is the standard treatment for breast cancer patients. Patients achieving complete pathological response (pCR) after NAC usually have a good prognosis. However, automatic pCR prediction has been a challenging problem due to lacking well annotations in 3D MRI. Thus far, unifying different annotation information to predict the tumor's early response to NAC has not been systematically addressed. This paper proposes a weakly and semi-supervised joint learning method that integrates attentional features from multi-parametric MRI with radiomic features for predicting pCR to NAC in breast cancer patients. The attention-based multi-instance learning (MIL) is first developed to generate informative MRI bag-level features and mine key instances. The mean-teacher framework is then employed to segment tumor regions in a semi-supervised setting for extracting radiomic features. We perform experiments on 442 patients' data and show that our method achieves an AUC value of 0.85 in pCR prediction, which is superior to comparative methods. It is also shown that learning from multi-parametric MRI outperforms that of single-parameter MRI in pCR prediction.
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Teng X, Zhang J, Zhang X, Fan X, Zhou T, Huang YH, Wang L, Lee EYP, Yang R, Cai J. Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:77. [PMID: 37381020 DOI: 10.1186/s13058-023-01674-9] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Fan
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Elaine Yuen Phin Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Y920, Lee Shau Kee Building, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Hong Kong, China.
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, China.
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Faraz K, Dauce G, Bouhamama A, Leporq B, Sasaki H, Bito Y, Beuf O, Pilleul F. Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches. J Pers Med 2023; 13:1062. [PMID: 37511674 PMCID: PMC10382057 DOI: 10.3390/jpm13071062] [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: 05/31/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.
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Affiliation(s)
- Khuram Faraz
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Grégoire Dauce
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
| | - Amine Bouhamama
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Hajime Sasaki
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
- FUJIFILM Healthcare Corporation, Tokyo 107-0052, Japan
| | | | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Frank Pilleul
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
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Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
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Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
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Gao X, Cui J, Wang L, Wang Q, Ma T, Yang J, Ye Z. The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study. Front Oncol 2023; 13:1205163. [PMID: 37388227 PMCID: PMC10303108 DOI: 10.3389/fonc.2023.1205163] [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: 04/13/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Purpose To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC). Methods This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets. Results The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82). Conclusion The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.
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Affiliation(s)
- Xujie Gao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingli Cui
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of General Surgery, Weifang People’s Hospital, Weifang, Shandong, China
| | - Lingwei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qiuyan Wang
- Department of Radiology, Weifang People’s Hospital, Weifang, Shandong, China
| | - Tingting Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jilong Yang
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Chen X, Yang Z, Huang R, Li Y, Liao Y, Li G, Wang M, Chen X, Dai Z, Fan W. Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features. Cancer Imaging 2023; 23:54. [PMID: 37264446 DOI: 10.1186/s40644-023-00564-9] [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: 10/20/2022] [Accepted: 05/01/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Axillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance. METHODS A total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan-Meier curves were used to analyze the survival outcomes. RESULTS Clinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, Kep, Ve, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS. CONCLUSION PSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.
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Affiliation(s)
- Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Ruibin Huang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, People's Republic of China
| | - Yue Li
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
| | | | - Guijin Li
- MR Application, Siemens Healthineers, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620, China
| | - Xiangguang Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, 515041, People's Republic of China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China.
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Sun YK, Zhou BY, Miao Y, Shi YL, Xu SH, Wu DM, Zhang L, Xu G, Wu TF, Wang LF, Yin HH, Ye X, Lu D, Han H, Xiang LH, Zhu XX, Zhao CK, Xu HX. Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. EClinicalMedicine 2023; 60:102027. [PMID: 37333662 PMCID: PMC10276260 DOI: 10.1016/j.eclinm.2023.102027] [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: 02/06/2023] [Revised: 04/22/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023] Open
Abstract
Background Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa. Methods Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545. Findings The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs. Interpretation 3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted. Funding The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).
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Affiliation(s)
- Yi-Kang Sun
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Bo-Yang Zhou
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Yao Miao
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Yi-Lei Shi
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Shi-Hao Xu
- Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Dao-Ming Wu
- Department of Ultrasound, Fujian Provincial Hospital, Fujian, China
| | - Lei Zhang
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Guang Xu
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Ting-Fan Wu
- Bayer Healthcare, Radiology, Shanghai, China
| | - Li-Fan Wang
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hao-Hao Yin
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Xin Ye
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Dan Lu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hong Han
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Li-Hua Xiang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Xiao-Xiang Zhu
- Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
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Chen K, Wang J, Li S, Zhou W, Xu W. Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging 2023; 50:1869-1880. [PMID: 36808002 DOI: 10.1007/s00259-023-06150-2] [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: 10/31/2022] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods. METHODS One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test. RESULTS A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test. CONCLUSION It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.
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Affiliation(s)
- Kun Chen
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Jian Wang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Shuai Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wen Zhou
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
| | - Wengui Xu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
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Wang X, Hua H, Han J, Zhong X, Liu J, Chen J. Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study. Clin Breast Cancer 2023:S1526-8209(23)00134-9. [PMID: 37321954 DOI: 10.1016/j.clbc.2023.05.010] [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: 03/16/2023] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/17/2023]
Abstract
INTRODUCTION This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram. RESULTS Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit. CONCLUSIONS The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.
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Affiliation(s)
- Xiaolin Wang
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hui Hua
- Department of Thyroid Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Junqi Han
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Zhong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Liu
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Chen
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Huang T, Fan B, Qiu Y, Zhang R, Wang X, Wang C, Lin H, Yan T, Dong W. Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer. Front Med (Lausanne) 2023; 10:1140514. [PMID: 37181350 PMCID: PMC10166881 DOI: 10.3389/fmed.2023.1140514] [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: 01/09/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
Background The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer. Methods One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness. Results Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95). Conclusion The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.
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Affiliation(s)
- Ting Huang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui Zhang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Chaoxiong Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China
| | - Ting Yan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Wentao Dong
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Zhang P, Song X, Sun L, Li C, Liu X, Bao J, Tian Z, Wang X, Yu Z. A novel nomogram model of breast cancer-based imaging for predicting the status of axillary lymph nodes after neoadjuvant therapy. Sci Rep 2023; 13:5952. [PMID: 37045864 PMCID: PMC10097686 DOI: 10.1038/s41598-023-29967-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/14/2023] [Indexed: 04/14/2023] Open
Abstract
This study is aimed to develop and validate a novel nomogram model that can preoperatively predict axillary lymph node pathological complete response (pCR) after NAT and avoid unnecessary axillary lymph node dissection (ALND) for breast cancer patients. A total of 410 patients who underwent NAT and were pathologically confirmed to be axillary lymph node positive after breast cancer surgery were included. They were divided into two groups: patients with axillary lymph node pCR and patients with residual node lesions after NAT. Then the nomogram prediction model was constructed by univariate and multivariate logistic regression. The result of multivariate logistic regression analysis showed that molecular subtypes, molybdenum target (MG) breast, computerized tomography (CT) breast, ultrasound (US) axilla, magnetic resonance imaging (MRI) axilla, and CT axilla (all p < 0.001) had a significant impact on the evaluation of axillary lymph node status after NAT. The nomogram score appeared that AUC was 0.832 (95% CI 0.786-0.878) in the training cohort and 0.947 (95% CI 0.906-0.988) in the validation cohort, respectively. The decision curve represented that the nomogram has a positive predictive ability, indicating its potential as a practical clinical tool.
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Affiliation(s)
- Pengyu Zhang
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiang Song
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Luhao Sun
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chao Li
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiaoyu Liu
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jiaying Bao
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhaokun Tian
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xinzhao Wang
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
- REMEGEN, LTD, 58 Middle Beijing Road, Yantai Economic & Technological Development Area, Yantai, Shandong, China.
| | - Zhiyong Yu
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
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Huang Y, Zhu T, Zhang X, Li W, Zheng X, Cheng M, Ji F, Zhang L, Yang C, Wu Z, Ye G, Lin Y, Wang K. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study. EClinicalMedicine 2023; 58:101899. [PMID: 37007742 PMCID: PMC10050775 DOI: 10.1016/j.eclinm.2023.101899] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 04/04/2023] Open
Abstract
Background Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.
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Affiliation(s)
- YuHong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - 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
| | - XiaoLing Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Li
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - XingXing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - MinYi Cheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - Fei Ji
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - LiuLu Zhang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - CiQiu Yang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - ZhiYong Wu
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
- Corresponding author. Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
| | - GuoLin Ye
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
- Corresponding author. Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, 528000, China.
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Corresponding author. Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, 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
- Corresponding author. 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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Li J, Yin H, Wang Y, Zhang H, Ma F, Li H, Qu J. Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer. Eur Radiol 2023; 33:2746-2756. [PMID: 36512039 DOI: 10.1007/s00330-022-09219-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/01/2022] [Accepted: 10/03/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer. METHODS Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset. RESULTS Both single sequence-based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749-0.914) and 0.820 (95% CI, 0.695-0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046-6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166-5.666, log-rank p = 0.011). CONCLUSIONS The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC. KEY POINTS • This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence-based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.
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Affiliation(s)
- Jing Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Yi Wang
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hongkai Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China
| | - Fei Ma
- Department of Gastrointestinal surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Hailiang Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China.
| | - Jinrong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China.
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Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy. Eur Radiol 2023; 33:2965-2974. [PMID: 36418622 DOI: 10.1007/s00330-022-09264-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/03/2022] [Accepted: 10/22/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..
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Carlini G, Gaudiano C, Golfieri R, Curti N, Biondi R, Bianchi L, Schiavina R, Giunchi F, Faggioni L, Giampieri E, Merlotti A, Dall’Olio D, Sala C, Pandolfi S, Remondini D, Rustici A, Pastore LV, Scarpetti L, Bortolani B, Cercenelli L, Brunocilla E, Marcelli E, Coppola F, Castellani G. Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer. J Pers Med 2023; 13:jpm13030478. [PMID: 36983660 PMCID: PMC10052019 DOI: 10.3390/jpm13030478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/20/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
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Affiliation(s)
- Gianluca Carlini
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Nico Curti
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (R.B.)
| | - Riccardo Biondi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (R.B.)
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Roma, Italy
| | - Enrico Giampieri
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Alessandra Merlotti
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Daniele Dall’Olio
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Claudia Sala
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Sara Pandolfi
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
- National Institute of Nuclear Physics, INFN, 40127 Bologna, Italy
| | - Arianna Rustici
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40138 Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Leonardo Scarpetti
- Dipartimento Diagnostica per Immagini AUSL Romagna, UOC Radiologia Faenza, 48018 Faenza, Italy
| | - Barbara Bortolani
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Laura Cercenelli
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Emanuela Marcelli
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
- Dipartimento Diagnostica per Immagini AUSL Romagna, UOC Radiologia Faenza, 48018 Faenza, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 40138 Bologna, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
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Pan L, He T, Huang Z, Chen S, Zhang J, Zheng S, Chen X. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image. Abdom Radiol (NY) 2023; 48:1246-1259. [PMID: 36859730 DOI: 10.1007/s00261-023-03838-9] [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: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/03/2023]
Abstract
OBJECTIVES Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. METHODS We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. CONCLUSION The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.
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Affiliation(s)
- Lin Pan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Tian He
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Zihan Huang
- School of Future Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Shuai Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Junrong Zhang
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Shaohua Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
| | - Xianqiang Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
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Duan Y, Yang G, Miao W, Song B, Wang Y, Yan L, Wu F, Zhang R, Mao Y, Wang Z. Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J Comput Assist Tomogr 2023; 47:199-204. [PMID: 36790871 DOI: 10.1097/rct.0000000000001426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
PURPOSE Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. METHODS A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. RESULTS Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. CONCLUSION The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.
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Affiliation(s)
- Yanli Duan
- From the Departments of Nuclear Medicine
| | | | | | - Bingxue Song
- Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong
| | | | - Lei Yan
- From the Departments of Nuclear Medicine
| | - Fengyu Wu
- From the Departments of Nuclear Medicine
| | - Ran Zhang
- Huiying Medical Technology Co, Ltd, Beijing
| | - Yan Mao
- Diagnosis and Treatment Centre of Breast Diseases, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Chen H, Wang X, Lan X, Yu T, Li L, Tang S, Liu S, Jiang F, Wang L, Zhang J. A radiomics model development via the associations with genomics features in predicting axillary lymph node metastasis of breast cancer: a study based on a public database and single-centre verification. Clin Radiol 2023; 78:e279-e287. [PMID: 36623978 DOI: 10.1016/j.crad.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 12/27/2022]
Abstract
AIM To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients with breast cancer. MATERIALS AND METHODS Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics-genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis. RESULTS After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve [AUC] = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases. CONCLUSION The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.
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Affiliation(s)
- H Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - X Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - X Lan
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - T Yu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - L Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - S Tang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - S Liu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - F Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - L Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China
| | - J Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, PR China.
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Mao N, Zhang H, Dai Y, Li Q, Lin F, Gao J, Zheng T, Zhao F, Xie H, Xu C, Ma H. Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study. Br J Cancer 2023; 128:793-804. [PMID: 36522478 PMCID: PMC9977865 DOI: 10.1038/s41416-022-02092-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. METHODS Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. RESULTS The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. CONCLUSIONS Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance.
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Affiliation(s)
- Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Yi Dai
- Department of Radiology, Peking University Shenzhen Hospital, 518000, Shenzhen, P. R. China
| | - Qin Li
- Department of Radiology, Fudan University Cancer Center, 200433, Shanghai, P. R. China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, 264005, Yantai, Shandong, P. R. China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Cong Xu
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
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Jailin C, Milioni De Carvalho P, Mohamed S, Vancamberg L, Amr Farouk Ibrahim M, Gomaa MM, Kamal RM, Muller S. Deformable registration with intensity correction for CESM monitoring response to Neoadjuvant Chemotherapy. Biomed Phys Eng Express 2023; 9. [PMID: 36758233 DOI: 10.1088/2057-1976/acba9f] [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: 12/16/2022] [Accepted: 02/09/2023] [Indexed: 02/11/2023]
Abstract
This paper proposes a robust longitudinal registration method for Contrast Enhanced Spectral Mammography in monitoring neoadjuvant chemotherapy. Because breast texture intensity changes with the treatment, a non-rigid registration procedure with local intensity compensations is developed. The approach allows registering the low energy images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding recombined images. The difference of registered images, called residual, makes vanishing the breast texture that did not changed between the two exams. Consequently, this registered residual allows identifying local density and iodine changes, especially in the lesion area. The method is validated with a synthetic NAC case where ground truths are available. Then the procedure is applied to 51 patients with 208 CESM image pairs acquired before and after the chemotherapy treatment. The proposed registration converged in all 208 cases. The intensity-compensated registration approach is evaluated with different mathematical metrics and through the repositioning of clinical landmarks (RMSE: 5.9 mm) and outperforms state-of-the-art registration techniques.
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Affiliation(s)
| | | | | | | | | | | | - Rasha Mohammed Kamal
- Baheya Foundation For Early Detection And Treatment Of Breast Cancer, El Haram, Giza, Egypt
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Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI. Cancers (Basel) 2023; 15:cancers15051439. [PMID: 36900231 PMCID: PMC10001061 DOI: 10.3390/cancers15051439] [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: 01/10/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.
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91
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Liu L, Wan H, Liu L, Wang J, Tang Y, Cui S, Li Y. Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer. Diagnostics (Basel) 2023; 13:diagnostics13040748. [PMID: 36832236 PMCID: PMC9954966 DOI: 10.3390/diagnostics13040748] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/13/2023] [Accepted: 01/28/2023] [Indexed: 02/18/2023] Open
Abstract
This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients' individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (p < 0.05). The Kaplan-Meier analysis distinguished two patient groups with high and low recurrence risk (p = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.
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Affiliation(s)
- Lili Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
- Department of Radiology, Chongqing General Hospital, Chongqing 401120, China
| | - Haoming Wan
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
| | - Li Liu
- Department of Radiology, The People’s Hospital of Yubei District of Chongqing, Chongqing 401120, China
| | - Jie Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yibo Tang
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
| | - Shaoguo Cui
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
- Correspondence: (S.C.); (Y.L.)
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
- Correspondence: (S.C.); (Y.L.)
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92
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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93
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Ma X, Gong J, Hu F, Tang W, Gu Y, Peng W. Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors. J Magn Reson Imaging 2023; 57:633-645. [PMID: 35657093 DOI: 10.1002/jmri.28286] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE Retrospective. POPULATION A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xiaowen Ma
- 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
| | - Feixiang Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Tang
- 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
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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94
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Brancato V, Brancati N, Esposito G, La Rosa M, Cavaliere C, Allarà C, Romeo V, De Pietro G, Salvatore M, Aiello M, Sangiovanni M. A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:1552. [PMID: 36772592 PMCID: PMC9921618 DOI: 10.3390/s23031552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/13/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
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Affiliation(s)
- Valentina Brancato
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Nadia Brancati
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Giusy Esposito
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Massimo La Rosa
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Ciro Allarà
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Giuseppe De Pietro
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Mara Sangiovanni
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
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95
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Lan A, Chen J, Li C, Jin Y, Wu Y, Dai Y, Jiang L, Li H, Peng Y, Liu S. Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1617. [PMID: 36674372 PMCID: PMC9867383 DOI: 10.3390/ijerph20021617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.
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Affiliation(s)
- Ailin Lan
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Junru Chen
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Chao Li
- Department of Vascular Surgery, Southwest Hospital, Army Medical University, 38 Main Street, Gaotanyan, Shapingba, Chongqing 400038, China
| | - Yudi Jin
- Department of Pathology, Chongqing University Cancer Hospital, No. 181, Hanyu Road, Shapingba District, Chongqing 400030, China
| | - Yinan Wu
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Yuran Dai
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Linshan Jiang
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Han Li
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Yang Peng
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Shengchun Liu
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
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96
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Li Y, Fan Y, Xu D, Li Y, Zhong Z, Pan H, Huang B, Xie X, Yang Y, Liu B. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol 2023; 12:1041142. [PMID: 36686755 PMCID: PMC9850142 DOI: 10.3389/fonc.2022.1041142] [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: 09/10/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Objective The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.
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Affiliation(s)
- Yuting Li
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China,Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yaheng Fan
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Dinghua Xu
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yan Li
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Zhangnan Zhong
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Haoyu Pan
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xiaotong Xie
- Department of Minimally Invasive Interventional Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, Suining, China,*Correspondence: Yang Yang, ; Bihua Liu,
| | - Bihua Liu
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China,Department of Radiology, Dongguan People’s Hospital, Dongguan, China,*Correspondence: Yang Yang, ; Bihua Liu,
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97
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Fan M, Wu X, Yu J, Liu Y, Wang K, Xue T, Zeng T, Chen S, Li L. Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer. Front Oncol 2023; 13:1057841. [PMID: 37207135 PMCID: PMC10189126 DOI: 10.3389/fonc.2023.1057841] [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: 09/30/2022] [Accepted: 04/19/2023] [Indexed: 05/21/2023] Open
Abstract
Purpose During neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI). Method In this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance. Results The DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion. Conclusions All these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Xilin Wu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Jiadong Yu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Yueyue Liu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Kailang Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Tailong Xue
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Shujun Chen
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- *Correspondence: Shujun Chen, ; Lihua Li,
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
- *Correspondence: Shujun Chen, ; Lihua Li,
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98
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Dagıstanli S, Sonmez S, Bulut N, Kose AM. Evaluation of treatment responses among subgroups of breast cancer patients receiving neoadjuvant chemotherapy. J Cancer Res Ther 2023; 19:S821-S826. [PMID: 38384061 DOI: 10.4103/jcrt.jcrt_1409_22] [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: 07/07/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND Breast MRIs are helpful for determining treatment plans, responses, and prospective survival analyses. In this retrospective cross-sectional study, we compared the preoperative MRI treatment response to neoadjuvant chemotherapy (NAC) administration with the postoperative pathological response in breast cancer patients. MATERIALS AND METHODS We analyzed data from 108 hospitalized patients receiving NAC between 2020 and 2022. We used MRI to evaluate the treatment response to NAC in patients with locally advanced breast cancers who had not received any prior treatment. We recorded the longest diameter of the primary tumor and the numbers of secondary tumors and axillary lymph nodes. In addition, we examined the correlation between the MRI response rate and pathological specimen results. RESULTS In our subgroup analyses, we found the best pathological response in patients with luminal B (Ki-67 index >14%) breast cancer and positivity for both hormone receptor and HER-2 markers. After comparing the pathological and radiological treatment responses in tumors and lymph nodes, the sensitivities were 90.3% for the pathological assessment and 42.8% for the radiological assessment, while the accuracies were 84.2% for the pathological assessment and 61.1% for the radiological assessment. CONCLUSION Using MRI techniques and sequence intervals and examining the histopathological characteristics of tumors may help increase the accuracy of the pathological complete response.
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Affiliation(s)
- Sevinc Dagıstanli
- Department of General Surgery, Kanuni Sultan Suleyman Research and Training Hospital, Istanbul, Turkey
| | - Suleyman Sonmez
- Department of Radiology, Kanuni Sultan Suleyman Research and Training Hospital, Istanbul, Turkey
| | - Nilufer Bulut
- Department of Medical Oncology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ali Mertcan Kose
- Department of Computer Programming, Vocational School, Istanbul Ticaret University, Istanbul, Turkey
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Sun L, Tian H, Ge H, Tian J, Lin Y, Liang C, Liu T, Zhao Y. Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes. Front Oncol 2023; 13:1107850. [PMID: 36959806 PMCID: PMC10028183 DOI: 10.3389/fonc.2023.1107850] [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: 12/23/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Purpose The aim of this study is to improve the accuracy of classifying luminal or non-luminal subtypes of breast cancer by using computer algorithms based on DCE-MRI, and to validate the diagnostic efficacy of the model by considering the patient's age of menarche and nodule size. Methods DCE-MRI images of patients with non-specific invasive breast cancer admitted to the Second Affiliated Hospital of Dalian Medical University were collected. There were 160 cases in total, with 84 cases of luminal type (luminal A and luminal B and 76 cases of non-luminal type (HER 2 overexpressing and triple negative). Patients were grouped according to thresholds of nodule sizes of 20 mm and age at menarche of 14 years. A cross-attention multi-branch net CAMBNET) was proposed based on the dataset to predict the molecular subtypes of breast cancer. Diagnostic performance was assessed by accuracy, sensitivity, specificity, F1 and area under the ROC curve (AUC). And the model is visualized with Grad-CAM. Results Several classical deep learning models were included for diagnostic performance comparison. Using 5-fold cross-validation on the test dataset, all the results of CAMBNET are significantly higher than the compared deep learning models. The average prediction recall, accuracy, precision, and AUC for luminal and non-luminal types of the dataset were 89.11%, 88.44%, 88.52%, and 96.10%, respectively. For patients with tumor size <20 mm, the CAMBNET had AUC of 83.45% and ACC of 90.29% for detecting triple-negative breast cancer. When classifying luminal from non-luminal subtypes for patients with age at menarche years, our CAMBNET model achieved an ACC of 92.37%, precision of 92.42%, recall of 93.33%, F1of 92.33%, and AUC of 99.95%. Conclusions The CAMBNET can be applied in molecular subtype classification of breasts. For patients with menarche at 14 years old, our model can yield more accurate results when classifying luminal and non-luminal subtypes. For patients with tumor sizes ≤20 mm, our model can yield more accurate result in detecting triple-negative breast cancer to improve patient prognosis and survival.
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Affiliation(s)
- Liang Sun
- The College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Haowen Tian
- The College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Hongwei Ge
- The College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Juan Tian
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yuxin Lin
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Chang Liang
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Tang Liu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- *Correspondence: Tang Liu, ; Yiping Zhao,
| | - Yiping Zhao
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- *Correspondence: Tang Liu, ; Yiping Zhao,
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Luo Z, Li J, Liao Y, Huang W, Li Y, Shen X. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:611-626. [PMID: 37005907 DOI: 10.3233/xst-221352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
PURPOSE This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.
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Affiliation(s)
- Zhendong Luo
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jing Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Wenxiao Huang
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yulin Li
- Department of Radiology, Peking Universtiy Shenzhen Hospital, Shenzhen, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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