Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images.

ABSTRACT:

Objectives

Metachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images.

Methods

Six-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the v

SUBMITTER: Xiao C 

PROVIDER: S-EPMC9010865 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature