Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.

ABSTRACT:

Background

The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN.

Methods

We retrospectively collected clinical data from 2,118 patients with left-sided CRC and contrast-enhanced CT images from 310 of these patients. From this data, a test set, a training set, and a temporal validation set were constructed. Logistic regression models were used to develop a clinical model, a CT model, and a radiomics model, which were then integrated into a combined model using logical rules. Finally, these models were evaluated using metrics suc

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12039209 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature