Application of machine learning in the prediction of deficient mismatch repair in patients with colorectal cancer based on routine preoperative characterization.

ABSTRACT:

Simple summary

Detecting deficient mismatch repair (dMMR) in patients with colorectal cancer is essential for clinical decision-making, including evaluation of prognosis, guidance of adjuvant chemotherapy and immunotherapy, and primary screening for Lynch syndrome. However, outside of tertiary care centers, existing detection methods are not widely disseminated and highly depend on the experienced pathologist. Therefore, it is of great clinical significance to develop a broadly accessible and low-cost tool for dMMR prediction, particularly prior to surgery. In this study, we developed a convenient and reliable model for predicting dMMR status in CRC patients on routine preoperative characterization utilizing multiple machine learning algorithms. This model will work as an automated

SUBMITTER: Xu D 

PROVIDER: S-EPMC9814116 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature