Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study
Bao Li,
Fengling Li,
Zhenyu Liu,
FangPing Xu,
Guolin Ye,
Wei Li,
Yimin Zhang,
Teng Zhu,
Lizhi Shao,
Chi Chen,
Caixia Sun,
Bensheng Qiu,
Hong Bu,
Kun Wang,
Jie Tian
Affiliations
Bao Li
Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Fengling Li
Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China; Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
Zhenyu Liu
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China
FangPing Xu
Department of Pathology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
Guolin Ye
The First People's Hospital of Foshan, Foshan, 528000, China
Wei Li
The First People's Hospital of Foshan, Foshan, 528000, China
Yimin Zhang
Diagnosis & Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, Shantou, 515000, China
Teng Zhu
Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
Lizhi Shao
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Chi Chen
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China
Caixia Sun
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China
Bensheng Qiu
Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China
Hong Bu
Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China; Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China; Corresponding author. Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Kun Wang
Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Corresponding author. Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou 510080, China
Jie Tian
Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, 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, People's Republic of China, Beijing, 100191, China; Corresponding author. Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China.
Introduction: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. Materials and methods: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. Results: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). Conclusion: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.
