Machine learning combined with CT-based radiomics predicts the prognosis of oesophageal squamous cell carcinoma.

ABSTRACT:

Objectives

This retrospective study aims to develop a machine learning model integrating preoperative CT radiomics and clinicopathological data to predict 3-year recurrence and recurrence patterns in postoperative oesophageal squamous cell carcinoma.

Materials and methods

Tumour regions were segmented using 3D-Slicer, and radiomic features were extracted via Python. LASSO regression selected prognostic features for model integration. Clinicopathological data include tumour length, lymph node positivity, differentiation grade, and neurovascular infiltration. Ultimately, a machine learning model was established by combining the screened imaging feature data and clinicopathological data and validating model performance. A nomogram was constructed for survival prediction, and ri

SUBMITTER: Liu M 

PROVIDER: S-EPMC12488548 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature