Development and application of a prognostic model based on radiomics and artificial intelligence for patients with lung adenocarcinoma brain metastasis.

Authors

Congying Zheng, Xinyuan Yang, Musen Ye, Kai Tang, Shubin Wang

Published in

Frontiers in oncology. Volume 16. Pages 1864327. Epub Jun 30, 2026.

Abstract

Lung cancer with brain metastasis (LCBM) impairs survival in lung adenocarcinoma. High postoperative recurrence rates highlight the necessity of accurate prognostic tools. This study aimed to develop an integrated radiomics-clinical model to improve survival prediction in lung adenocarcinoma patients with brain metastasis.
The cohort of 176 patients with LCBM was randomly divided into a training set (n=123) and a test set (n=53). The identification of clinical risk factors was performed using both univariate and multivariate logistic regression analyses. A radiomics model was developed based on radiomic features extracted from preoperative magnetic resonance imaging (MRI), following selection with Least Absolute Shrinkage and Selection Operator (LASSO) regression. The performance of the combined nomogram, which integrated significant clinical and radiomic features, was evaluated by the area under the receiver operating characteristic curve (AUC), along with calibration and decision curve analyses.
Multivariate analysis established the EGFR mutation status, number of brain metastases, and Lung-molGPA score as independent prognostic determinants. Performance evaluation of the radiomics model yielded AUCs of 0.862 in the training set and 0.829 in the test set, indicating robust diagnostic performance. The combined nomogram demonstrated superior predictive performance, with AUC values of 0.904 and 0.874 in the training and test sets, respectively, along with good calibration and clinical utility in both cohorts.
These findings demonstrate the combined utility of integrating radiomics with clinical parameters to enhance prognostic accuracy, enabling personalized treatment stratification in LCBM and improving clinical decision-making and risk stratification.

PMID:
42453883
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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