Preoperative prediction of the Lauren classification in gastric cancer using automated nnU-Net and radiomics: a multicenter study.

ABSTRACT:

Objectives

To develop and validate a deep learning model based on nnU-Net combined with radiomics to achieve autosegmentation of gastric cancer (GC) and preoperative prediction via the Lauren classification.

Methods

Patients with a pathological diagnosis of GC were retrospectively enrolled in three medical centers. The nnU-Net autosegmentation model was developed using manually segmented datasets and evaluated by the Dice similarity coefficient (DSC). The CT images were processed by the nnU-Net model to obtain autosegmentation results and extract radiomic features. The least absolute shrinkage and selection operator (LASSO) method selects optimal features for calculating the Radscore and constructing a radiomic model. Clinical characteristics and the Radscore were integrated

SUBMITTER: Cao B 

PROVIDER: S-EPMC11861772 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature