CT radiomics for noninvasive prediction of histologic differentiation in gastric cancer.

Authors

Rixin Su, Yu Zhang, Jie Cao, Fangfang Chen, Xuemeng Li, Ping Li, Geng Bian

Published in

Medical physics. Volume 53. Issue 8. Pages e70605.

Abstract

The histological differentiation grade of gastric cancer critically influences treatment and prognosis. While CT radiomics shows promise for noninvasive prediction, its relationship with gene expression remains unclear.
This study aimed to develop a clinical-radiomics model for predicting tumor differentiation in gastric cancer patients and to explore the underlying mechanisms.
We retrospectively analyzed clinical data and CT images from 162 gastric cancer patients, who were randomly assigned to training and validation cohorts. The least absolute shrinkage and selection operator (LASSO) method was used to select features and construct the Rad-score. Subsequently, clinical-radiomics models were built and evaluated for their predictive efficacy and clinical incremental value. Furthermore, hub genes were screened, and their associated pathways were investigated using machine learning, bioinformatics analysis, and experimental validation.
A clinical-radiomics model based on N stage, M stage and Rad-score was developed. The receiver operating characteristic (ROC) curves indicated that the model had preliminary evidence of predictive potential within this single-center cohort (training AUC = 0.872, validation AUC = 0.935). The calibration curves indicated a reasonable concordance between the observed values and the predicted outcomes in this retrospective sample. The decision curve analysis demonstrated a net benefit that requires further confirmation in external cohorts. The clinical impact curve (CIC) demonstrated the model's potential clinical applicability, which warrants validation in prospective settings. Sequencing data further revealed that the key gene IGHG1 was significantly associated with the Rad-score, with potential mechanisms involving the TGF-beta signaling pathway.
The clinical-radiomics model, incorporating N stage, M stage, and Rad-score, serves as a preliminary research tool for assessing tumor differentiation in gastric cancer patients within a single-center setting. External validation is required before any consideration of clinical generalization. Radiomics enables noninvasive evaluation of differentiation status while generating hypotheses regarding its underlying mechanisms.

PMID:
42507312
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.