Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.

ABSTRACT:

Background

Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further improved accuracy. In this study, we aimed to construct a combined prognostic model using deep learning-extracted features from digital pathology images of pancreatic ductal adenocarcinoma (PDAC) alongside clinical predictive indicators and to explore its prognostic value.

Methods

A retrospective analysis was conducted on 142 postoperative pathologically confirmed PDAC cases. These cases were divided into training (n = 114) and testing sets (n = 28) at an 8:2 ratio. Tumor whole-slide imaging features were extracted and screened to construct a pathological risk model based on a pre-trained deep learning model. Clinical and pathologic

SUBMITTER: Hu K 

PROVIDER: S-EPMC11528996 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature