Deep learning–assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer.

Abstract

24 Background: Deficient mismatch repair (or microsatellite instability) is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing is time-consuming and incur substantial costs. We developed and validated a deep learning-based classifiers (MMR-Scopy) to detect mismatch repair-deficient status from routine colonoscopy images. Methods: We retrospectively obtained the colonoscopy images from the imaging database at Endoscopic Center of the Sixth Affiliated Hospital of Sun Yat-sen University. Colorectal cancer images from patients that had undergone colonoscopy procedures, surgery and the mismatch repair immunohistochemistry test were eligible for this study. Colonoscopy images from a prospective trial (Neoadjuvant PD-1 blockade by toripalimab with or without celecoxib in mismatch repair-deficient or microsatellite instability-high locally advanced colorectal cancer, PICC) were used to test MMR-Scopy. The primary outcome was dMMR status. The primary performance metrics were accuracy, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). Results: A total of 5226 eligible images from 892 tumors from the consecutive patients were utilized to develop and validate the deep learning model. 2105 CRC images from 306 tumors were randomly selected to form model development dataset with a class-balanced approach. 3121 images of 488 pMMR tumors and 98 dMMR tumors were used to form the independent dataset. The MMR-Scopy model achieved an AUROC of 0.948 (95% CI 0.919- 0.977) on the test dataset. On the independent validation dataset, the AUROC was 0.807 (0.760- 0.854), and the NPV in was 94.2% (95% CI 0.918-0.967). On the PICC dataset, the model identified 29 tumors among the 33 dMMR tumors (87.88%). Conclusions: MMR-Scopy achieved a high NPV in detecting dMMR colorectal cancers. This model might serve as an automatic screening tool, one that might potentially assist doctors in determining a new treatment strategy. [Table: see text]