Development and validation of a phase-stable computed tomography radiomics model for brain metastasis risk stratification in non-small cell lung cancer.
- 1Department of Radiology, the Key Laboratory of Advanced Interdisciplinary Studies Center, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Abstract
Brain metastasis (BM) is a frequent complication of non-small cell lung cancer (NSCLC), but individualized risk stratification remains limited. We evaluated whether computed tomography (CT) radiomic features reproducible across non-enhanced and contrast-enhanced phases could improve BM risk prediction and whether integration with clinical biomarkers and semantic CT findings provided additional value. This retrospective study included 226 patients with pathologically confirmed NSCLC who underwent pretreatment multiphase chest CT and contrast-enhanced brain magnetic resonance imaging. BM was defined as metastasis detected at baseline or during follow-up. Patients were randomly divided into training (n=181) and held-out test (n=45) sets. Radiomic features reproducible between non-contrast-enhanced CT (NE-CT) and venous-phase contrast-enhanced CT (CE-CT) were defined as phase-stable using an intraclass correlation coefficient greater than 0.80. Radiomic features were reduced using univariable filtering, correlation analysis, and least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation. Clinical biomarkers and semantic CT features were integrated with phase-stable radiomics. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), classification metrics, calibration, and decision curve analysis. Among single-sequence models, NE-CT showed the best discrimination (AUC, 0.901; 95% confidence interval (CI): 0.858-0.945 in training and 0.765; 95% CI: 0.618-0.912 in testing). The phase-stable radiomics model achieved AUCs of 0.832 (95% CI: 0.773-0.891) and 0.793 (95% CI: 0.657-0.929), respectively. Adding clinical biomarkers improved test performance (AUC, 0.909), whereas further addition of semantic CT findings yielded similar discrimination (AUC, 0.908). The fully combined model had test accuracy of 0.867, sensitivity of 0.667, specificity of 0.939, positive predictive value of 0.800, and negative predictive value of 0.886. Within thin-slice CT protocols of 2 mm or less, differences in contrast phase and reconstruction kernel had limited influence on model discrimination. Phase-stable CT radiomics provided reproducible BM risk stratification in NSCLC. Integration with clinical biomarkers improved discrimination, whereas semantic CT findings provided limited additional gain. These findings support further multicenter validation of phase-stability-guided CT radiomics for BM risk assessment.
Related
- Development and internal validation of a clinicopathological and CT radiomics based prediction model for extracranial distant metastasis after radical resection in early-stage non-small cell lung cancer
- MRI-Based Radiomics Model for Pretreatment Prediction of Pathological Lymph Node Metastasis in Neuroendocrine Carcinoma of the Cervix: Dual-Center Development and External Validation.
- Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study.
- PET/CT radiomics for noninvasive histologic risk stratification in resected lung adenocarcinoma: a single-system development and internal validation study.
- Multimodal integration of carotid plaque ultrasound radiomics and dynamic systemic immune-inflammation trajectories for subacute risk stratification after stroke thrombectomy: development and internal validation of an exploratory prognostic model
BibTeX
@article{li2026development,
title = {{Development and validation of a phase-stable computed tomography radiomics model for brain metastasis risk stratification in non-small cell lung cancer.}},
author = {Zimeng Li and Xiaoying Xia and Jinmiao Chen and Yifei Du and Jiaxuan Zhou and Yeqing Zhu and Xinchun Li and Qi Wan},
journal = {Translational cancer research},
year = {2026},
month = sep,
doi = {10.21037/tcr-2026-1231},
url = {https://europepmc.org/article/MED/42997722},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and validation of a phase-stable computed tomography radiomics model for brain metastasis risk stratification in non-small cell lung cancer.
AU - Zimeng Li
AU - Xiaoying Xia
AU - Jinmiao Chen
AU - Yifei Du
AU - Jiaxuan Zhou
AU - Yeqing Zhu
AU - Xinchun Li
AU - Qi Wan
PY - 2026
DA - 2026/09/28
JO - Translational cancer research
DO - 10.21037/tcr-2026-1231
UR - https://europepmc.org/article/MED/42997722
AB - Brain metastasis (BM) is a frequent complication of non-small cell lung cancer (NSCLC), but individualized risk stratification remains limited. We evaluated whether computed tomography (CT) radiomic features reproducible across non-enhanced and contrast-enhanced phases could improve BM risk prediction and whether integration with clinical biomarkers and semantic CT findings provided additional value. This retrospective study included 226 patients with pathologically confirmed NSCLC who underwent pretreatment multiphase chest CT and contrast-enhanced brain magnetic resonance imaging. BM was defined as metastasis detected at baseline or during follow-up. Patients were randomly divided into training (n=181) and held-out test (n=45) sets. Radiomic features reproducible between non-contrast-enhanced CT (NE-CT) and venous-phase contrast-enhanced CT (CE-CT) were defined as phase-stable using an intraclass correlation coefficient greater than 0.80. Radiomic features were reduced using univariable filtering, correlation analysis, and least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation. Clinical biomarkers and semantic CT features were integrated with phase-stable radiomics. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), classification metrics, calibration, and decision curve analysis. Among single-sequence models, NE-CT showed the best discrimination (AUC, 0.901; 95% confidence interval (CI): 0.858-0.945 in training and 0.765; 95% CI: 0.618-0.912 in testing). The phase-stable radiomics model achieved AUCs of 0.832 (95% CI: 0.773-0.891) and 0.793 (95% CI: 0.657-0.929), respectively. Adding clinical biomarkers improved test performance (AUC, 0.909), whereas further addition of semantic CT findings yielded similar discrimination (AUC, 0.908). The fully combined model had test accuracy of 0.867, sensitivity of 0.667, specificity of 0.939, positive predictive value of 0.800, and negative predictive value of 0.886. Within thin-slice CT protocols of 2 mm or less, differences in contrast phase and reconstruction kernel had limited influence on model discrimination. Phase-stable CT radiomics provided reproducible BM risk stratification in NSCLC. Integration with clinical biomarkers improved discrimination, whereas semantic CT findings provided limited additional gain. These findings support further multicenter validation of phase-stability-guided CT radiomics for BM risk assessment.
SN - 2218-676X
SN - 2219-6803
ER -