[口头报告]Deep Learning can predict pathological complete response directly from rapid frozen sections in rectal cancer - 报告详情

Deep Learning can predict pathological complete response directly from rapid frozen sections in rectal cancer
编号:63 访问权限:仅限参会人 更新:2021-10-30 18:37:15 浏览:159次 口头报告

报告开始:2021年11月13日 15:30 (Asia/Shanghai)

报告时间:15min

所在会议:[PS1] Plenary Session 1 [AI1] Workshop on AI

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摘要
Purpose: To develop and validate a prediction model for evaluating pathological complete response (pCR) to neoadjuvant chemoradiotherapy in patients with rectal cancer from rapid frozen sections.
Experimental Design: Because the prediction of pCR is related to tumor cells,The Cancer Genome Atlas Colon Adenocarcinoma database was enrolled in this study which was used to construct a tumor-positive region screening model by ResNet18. Then, after preprocessing and screening of tumor-positive regions, forty-eight rectal cancer patients (2013-2017) receiving neoadjuvant chemoradiotherapy were enrolled to construct a pCR prediction model based on ResNet50 with weakly supervised learning. And pCR prediction values of all the patients' rapid frozen sections were visualized by heat maps.
Results: For the tumor-positive region screening model, ten-fold cross-validation is used. And the best one has an accuracy rate of 94.1% in training set, 93.8% in validation set, and an AUC of 0.961 in validation. Meanwhile, for the pCR prediction model, a five-fold cross-validation was used. The average accuracy rate is 89.7% in training set, and the average accuracy rate is 78% in validation set.
Conclusion: It’s a breakthrough achievement that using deep learning to obtain the predictions of pCR from rapid frozen sections. The model with high prediction accuracy, strong generalization ability can assist the judgment and prediction of clinical work furtherly.
 
关键字
deep learning,rectal cancer,pathological complete response,rapid frozen sections
报告人
鑫 杨
重庆大学附属肿瘤医院

稿件作者
鑫 杨 重庆大学附属肿瘤医院
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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