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Yao J, Jia X, Zhou W, Zhu Y, Chen X, Zhan W, Zhou J. Predicting axillary response to neoadjuvant chemotherapy using peritumoral and intratumoral ultrasound radiomics in breast cancer subtypes. iScience 2024; 27:110716. [PMID: 39280600 PMCID: PMC11399604 DOI: 10.1016/j.isci.2024.110716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 06/29/2024] [Accepted: 08/08/2024] [Indexed: 09/18/2024] Open
Abstract
To explore machine learning (ML)-based breast tumor peritumoral (P) and intratumoral ultrasound radiomics signatures (IURS) for predicting axillary response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) with node-positive. A total of 435 patients were divided into hormone receptor (HR)+/human epidermal growth factor receptor (HER)2-, HER2+, and triple-negative (TN) subtypes. ML classifiers including random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) were applied to construct PURS, IURS, and the combined P-IURS radiomics models. SVM of the TN subtype obtained the most favorable performance with an AUC of 0.917 (95%CI: 0.859, 0.960) in PURS models, RF of the HER2+ subtype yielded the highest efficacy in IURS models [AUC = 0.935 (95%CI: 0.843, 0.976)]. The RF-based combined P-IURS model of the HER2+ subtype improved the efficacy to a maximum AUC of 0.952 (95%CI: 0.868, 0.994). ML-based US radiomics can be a promising biomarker to predict axillary response.
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Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaosong Chen
- Department of Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Mao N, Bao Y, Dong C, Zhou H, Zhang H, Ma H, Wang Q, Xie H, Qu N, Wang P, Lin F, Lu J. Delta Radiomics Based on MRI for Predicting Axillary Lymph Node Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients. Acad Radiol 2024:S1076-6332(24)00529-4. [PMID: 39271381 DOI: 10.1016/j.acra.2024.07.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 09/15/2024]
Abstract
PURPOSE To develop and test a radiomics nomogram based on magnetic resonance imaging (MRI) and clinicopathological factors for predicting the axillary pathologic complete response (apCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients with axillary lymph node (ALN) metastases. MATERIALS AND METHODS A total of 319 patients who underwent MRI examination and received NAC treatment were enrolled from two centers, and the presence of ALN metastasis was confirmed by biopsy pathology before NAC. The radiomics features were extracted from regions of interest of ALNs before (pre-radiomics) and after (post-radiomics) NAC. The difference of features before and after NAC, named delta radiomics, was calculated. The variance threshold, selectKbest and least absolute shrinkage and selection operator algorithm were used to select radiomics features. Radscore was calculated by a linear combination of selected features, weighted by their respective coefficients. The univariate and multivariate logistic regression was used to select the clinicopathological factors and radscores, and a radiomics nomogram was built by multivariable logistic regression analysis. The performance of the nomogram was evaluated by the area under the receiver operator characteristic curve (AUC), decision curve analysis (DCA) and calibration curves. Furthermore, to explore the biological basis of radiomics nomogram, 16 patients with RNA-sequence data were included for genetic analysis. RESULTS The radiomics nomogram was constructed by two radscores (post- and delta- radscores) and one clinicopathological factor (progesterone hormone, PR), and showed powerful predictive performance in both internal and external test sets, with AUCs of 0.894 (95% confidence interval [CI], 0.877-0.959) and 0.903 (95% CI, 0.801-0.986), respectively. The calibration curves and DCA showed favorable consistency and clinical utility. With the assistance of nomogram, the rate of unnecessary ALND would be reduced from 60.42% to 21.88%, and the rate of final benefit rate would be increased from 39.58% to 70.83%. Moreover, genetic analysis revealed that high apCR prediction scores were associated with the upregulation of immune-mediated genes and pathways. CONCLUSION The radiomics nomogram showed great performance in predicting apCR after NAC for breast cancer patients, which could help clinicians to identify patients with apCR and avoid unnecessary axillary lymph node dissection.
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Affiliation(s)
- Ning Mao
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing, P R China (N.M., J.L.); Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Yuhan Bao
- Breast center, The Second Hospital of Shandong University, Jinan, Shandong, P R China (Y.B.)
| | - Chuntong Dong
- Department of Radiology, Qingdao Cardiovascular Hospital, Qingdao, Shandong, P R China (C.D.)
| | - Heng Zhou
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, P R China (H.Z.)
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Qi Wang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Nina Qu
- Department of Ultrasound, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.Q.)
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, P R China (P.W.)
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P R China (N.M., H.M., H.X., F.L.); Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Shandong, P R China (N.M., H.Z., H.M., Q.W., H.X., F.L.)
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing, P R China (N.M., J.L.).
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Zhu T, Huang YH, Li W, Wu CG, Zhang YM, Zheng XX, Zhang TF, Lin YY, Liu ZY, Ye GL, Lin Y, Wu ZY, Wang K. A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial. Br J Cancer 2024; 131:692-701. [PMID: 38918556 PMCID: PMC11333754 DOI: 10.1038/s41416-024-02726-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER The clinical trial numbers were NCT03154749 and NCT04858529.
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Affiliation(s)
- Teng Zhu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Yu-Hong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Wei Li
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Can-Gui Wu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Yi-Min Zhang
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
| | - Xing-Xing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Ting-Feng Zhang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Ying-Yi Lin
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
- Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Zai-Yi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Guo-Lin Ye
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhi-Yong Wu
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
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Zou Y, Xue M, Hossain MI, Zhu Q. Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:076007. [PMID: 39050779 PMCID: PMC11268382 DOI: 10.1117/1.jbo.29.7.076007] [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: 04/23/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/27/2024]
Abstract
Significance We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. Aim We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. Approach We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. Results The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. Conclusion The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.
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Affiliation(s)
- Yun Zou
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Minghao Xue
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Md Iqbal Hossain
- Washington University in St. Louis, Imaging Science, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
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Yaghoobpoor S, Fathi M, Ghorani H, Valizadeh P, Jannatdoust P, Tavasol A, Zarei M, Arian A. Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review. Eur J Radiol Open 2024; 12:100561. [PMID: 38699592 PMCID: PMC11063585 DOI: 10.1016/j.ejro.2024.100561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/29/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
Background and objective Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound. Methods A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment. Results Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data. Conclusion This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.
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Affiliation(s)
- Shirin Yaghoobpoor
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Student Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Mobina Fathi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Student Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Hamed Ghorani
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Parya Valizadeh
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Payam Jannatdoust
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Arian Tavasol
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Student Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Melika Zarei
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Department of Radiology and Nuclear Medicine, Paramedical School, Kermanshah University of Medical Sciences, Kermanshah, Islamic Republic of Iran
| | - Arvin Arian
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
- Cancer Research Institute, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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Wang H, Wang K, Ma S, Gao G, Wang X. Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation. J Appl Clin Med Phys 2024; 25:e14244. [PMID: 38146796 PMCID: PMC11005965 DOI: 10.1002/acm2.14244] [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: 05/10/2023] [Revised: 07/16/2023] [Accepted: 12/03/2023] [Indexed: 12/27/2023] Open
Abstract
OBJECTIVES To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa). METHODS A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n = 70) and test (n = 30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision-recall (PR) curve analysis were used to analyze the models' performance. RESULTS Sixty-five patients without BCR (BCR-) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR- and BCR+ groups or between the training and test datasets (all p > 0.05). The AUCs were 0.637 (95% CI: 0.434-0.838), 0.841 (95% CI: 0.695-0.940), 0.840 (95% CI: 0.698-0.983), and 0.808 (95% CI: 0.627-0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different. CONCLUSION The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa.
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Affiliation(s)
- Huihui Wang
- Department of RadiologyPeking University First HospitalBeijingChina
| | - Kexin Wang
- School of Basic Medical SciencesCapital Medical UniversityBeijingChina
| | - Shuai Ma
- Department of RadiologyPeking University First HospitalBeijingChina
| | - Ge Gao
- Department of RadiologyPeking University First HospitalBeijingChina
| | - Xiaoying Wang
- Department of RadiologyPeking University First HospitalBeijingChina
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Li Z, Ma Q, Gao Y, Qu M, Li J, Lei J. Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis. Eur Radiol 2024; 34:930-942. [PMID: 37615764 DOI: 10.1007/s00330-023-10155-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/09/2023] [Accepted: 07/08/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVE This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis. RESULTS For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses. CONCLUSION MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings. CLINICAL RELEVANCE STATEMENT Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients. KEY POINTS • MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.
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Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Qinqin Ma
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Mengmeng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jinkui Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China.
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Lee HJ, Nguyen AT, Song MW, Lee JE, Park SB, Jeong WG, Park MH, Lee JS, Park I, Lim HS. Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography. Korean J Radiol 2023; 24:498-511. [PMID: 37271204 DOI: 10.3348/kjr.2022.0731] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/30/2023] [Accepted: 04/30/2023] [Indexed: 06/06/2023] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. MATERIALS AND METHODS This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. RESULTS Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CONCLUSION CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.
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Affiliation(s)
- Hyo-Jae Lee
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Myung Won Song
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Seol Bin Park
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Won Gi Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ji Shin Lee
- Department of Pathology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea
- Department of Data Science, Chonnam National University, Gwangju, Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea.
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10
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Murtas F, Landoni V, Ordòñez P, Greco L, Ferranti FR, Russo A, Perracchio L, Vidiri A. Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis. Front Oncol 2023; 13:1152158. [PMID: 37251915 PMCID: PMC10213670 DOI: 10.3389/fonc.2023.1152158] [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: 01/27/2023] [Accepted: 04/24/2023] [Indexed: 05/31/2023] Open
Abstract
Objective This study aimed to develop a clinical-radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions. Materials and methods A total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index. Results All three clinical-radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively. Conclusion The clinical-radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening.
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Affiliation(s)
- Federica Murtas
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Valeria Landoni
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Pedro Ordòñez
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Andrea Russo
- Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Letizia Perracchio
- Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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11
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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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Liu S, Du S, Gao S, Teng Y, Jin F, Zhang L. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients. BMC Cancer 2023; 23:15. [PMID: 36604679 PMCID: PMC9817310 DOI: 10.1186/s12885-022-10496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 12/29/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic contrast-enhanced (DCE) MRI for early prediction of the axillary pathological complete response (pCR) of breast cancer patients after neoadjuvant chemotherapy (NAC). METHODS A total of 120 patients with ALN-positive breast cancer who underwent breast MRI before and after the first cycle of NAC between October 2018 and May 2021 were prospectively included in this study. Patients were divided into a training (n = 84) and validation (n = 36) cohort based on the temporal order of their treatments. Radiomic features were extracted from the largest slice of targeted ALN on DCE-MRI at pretreatment and after one cycle of NAC, and their changes (delta-) were calculated and recorded. Logistic regression was then applied to build radiomic models using the pretreatment (pre-), first-cycle(1st-), and changes (delta-) radiomic features separately. A clinical model was also built and combined with the radiomic models. The models were evaluated by discrimination, calibration, and clinical application and compared using DeLong test. RESULTS Among the three radiomic models, the ALN delta-radiomic model performed the best with AUCs of 0.851 (95% CI: 0.770-0.932) and 0.822 (95% CI: 0.685-0.958) in the training and validation cohorts, respectively. The clinical model yielded moderate AUCs of 0.742 (95% CI: 0.637-0.846) and 0.723 (95% CI: 0.550-0.896), respectively. After combining clinical features to the delta-radiomics model, the efficacy of the combined model (AUC = 0.932) in the training cohort was significantly higher than that of both the delta-radiomic model (Delong p = 0.017) and the clinical model (Delong p < 0.001) individually. Additionally, in the validation cohort, the combined model had the highest AUC (0.859) of any of the models we tested although this was not statistically different from any other individual model's validation AUC. Calibration and decision curves showed a good agreement and a high clinical benefit for the combined model. CONCLUSION This preliminary study indicates that ALN-based delta-radiomic model combined with clinical features is a promising strategy for the early prediction of downstaging ALN status after NAC. Future axillary MRI applications need to be further explored.
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Affiliation(s)
- Shasha Liu
- grid.412636.40000 0004 1757 9485Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001 China
| | - Siyao Du
- grid.412636.40000 0004 1757 9485Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001 China
| | - Si Gao
- grid.412636.40000 0004 1757 9485Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001 China
| | - Yuee Teng
- grid.412636.40000 0004 1757 9485Departments of Medical Oncology and Thoracic Surgery, The First Hospital of China Medical University, Shenyang, 110001 China
| | - Feng Jin
- grid.412636.40000 0004 1757 9485Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, 110001 China
| | - Lina Zhang
- grid.412636.40000 0004 1757 9485Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001 China
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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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