Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

ABSTRACT:

Background

Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images.

Methods

This single-center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were

SUBMITTER: Zhang W 

PROVIDER: S-EPMC8209621 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature