Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemorad
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ORIGINAL ARTICLE – COLORECTAL CANCER
Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study Lizhi Shao, PhD1,2, Zhenyu Liu, PhD2,3, Lili Feng, MD4, Xiaoying Lou, MD5, Zhenhui Li, MD6, Xiao-Yan Zhang, MD7, Xiangbo Wan, MD4, Xuezhi Zhou, PhD2,8, Kai Sun, PhD2,8, Da-Fu Zhang, MD6, Lin Wu, MD9, Guanyu Yang, PhD1,10, Ying-Shi Sun, MD7, Ruihua Xu, MD11, Xinjuan Fan, MD5, and Jie Tian, PhD2,3,8,12 1
School of Computer Science and Engineering, Southeast University, Nanjing, China; 2CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China; 3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; 4Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; 5Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; 6Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China; 7Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China; 8 Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, China; 9Department of Pathology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China; 10LIST, Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China; 11State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China; 12Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
Lizhi Shao, Zhenyu Liu, Lili Feng, Xiaoying Lou, Zhenhui Li, XiaoYan Zhang have contributed equally to this work and should be considered as co-first authors. Guanyu Yang and Ying-Shi Sun are co-first authors.
Electronic supplementary material The online version of this article (https://doi.org/10.1245/s10434-020-08659-4) contains supplementary material, which is available to authorized users. Ó The Author(s) 2020 First Received: 3 January 2020 R. Xu, MD e-mail: [email protected] X. Fan, MD e-mail: [email protected] J. Tian, PhD e-mail: [email protected]
ABSTRACT Background. The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning. Patients and Methods. A total of 981 consecutive patients with evaluation of response according to tumor regression
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