Anqi Wang1, Ruiqi Ding2, Jing Zhang3, Beibei Zhang4, Xiaolin Huang5, Haiyang Zhou6. 1. Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China. 2. Department of Automation, Shanghai Jiao Tong University, Shanghai, China. 3. Department of Pathology, Changzheng Hospital, Navy Medical University, Shanghai, China. 4. Department of Dermatology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. trpfzbb@126.com. 5. Department of Automation, Shanghai Jiao Tong University, Shanghai, China. xiaolinhuang@sjtu.edu.cn. 6. Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China. haiyang1985_1@aliyun.com.
Abstract
AIM: We hypothesize that machine learning of histomorphological features can predict response to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC). METHOD: This retrospective study included 146 LARC patients who received NAT followed by surgery. The pathologists scanned the H&E slides of pretreatment tumor biopsy into whole slide images (WSIs). We randomly split patients into the primary and validation sets with a ratio of 80%:20%. We cut the WSIs into smaller parts (sample amount: 200-500) and used a convolutional neural network (CNN) to process these blocks directly. Then, a graph neural network (GNN) was applied to train the model in the primary set. The independent validation set was used to assess the performance of the model. RESULT: Our model could provide indicative information to identify the patients who were most likely to benefit from NAT. When the sample amount reached 500, the tile-level classifier for distinguishing poor response from good response produced an AUC of 0.779 in the primary set and 0.733 in the validation set. CONCLUSION: In this pilot study, we propose a novel predictive model of therapeutic response to NAT in LARC using a routine diagnostic tool employed in daily practice.
AIM: We hypothesize that machine learning of histomorphological features can predict response to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC). METHOD: This retrospective study included 146 LARC patients who received NAT followed by surgery. The pathologists scanned the H&E slides of pretreatment tumor biopsy into whole slide images (WSIs). We randomly split patients into the primary and validation sets with a ratio of 80%:20%. We cut the WSIs into smaller parts (sample amount: 200-500) and used a convolutional neural network (CNN) to process these blocks directly. Then, a graph neural network (GNN) was applied to train the model in the primary set. The independent validation set was used to assess the performance of the model. RESULT: Our model could provide indicative information to identify the patients who were most likely to benefit from NAT. When the sample amount reached 500, the tile-level classifier for distinguishing poor response from good response produced an AUC of 0.779 in the primary set and 0.733 in the validation set. CONCLUSION: In this pilot study, we propose a novel predictive model of therapeutic response to NAT in LARC using a routine diagnostic tool employed in daily practice.
Authors: Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos Journal: Nat Med Date: 2018-09-17 Impact factor: 53.440
Authors: Vetri Sudar Jayaprakasam; Viktoriya Paroder; Peter Gibbs; Raazi Bajwa; Natalie Gangai; Ramon E Sosa; Iva Petkovska; Jennifer S Golia Pernicka; James Louis Fuqua; David D B Bates; Martin R Weiser; Andrea Cercek; Marc J Gollub Journal: Eur Radiol Date: 2021-07-29 Impact factor: 7.034