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Updated: Sep 5, 2026
Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
Published on: June 7, 2020
Multimodal Brain Radiomics for Predicting Pathological Subtypes of Lung Cancer Brain Metastases: Impact of Imaging
Jiashi Geng1, Jianyu Xu2, Hongkai Liu2
1Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China (J.G.).
Rationale And Objectives:
To develop and validate a brain imaging-based multimodal radiomics model for noninvasive prediction of pathological subtypes in lung cancer patients with brain metastases, and to evaluate the effects of imaging modalities, computed tomography, contrast-enhanced T1-weighted imaging, T2-weighted imaging (CT, CE-T1WI, T2WI), and spatial ranges (tumor vs whole-brain) on model performance for optimizing diagnosis and treatment strategy.
Materials And Methods:
A total of 255 lung cancer patients with pathologically confirmed brain metastases were retrospectively enrolled and divided into training and test cohorts at a 7:3 ratio. Radiomic features were extracted from tumor and whole-brain regions on CT, CE-T1WI, and T2WI after image preprocessing. Feature selection was performed via Z-score normalization, t-test, Pearson correlation, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. Single-modal, spatial-specific, and CT-magnetic resonance imaging (MRI) multimodal models were constructed using multiple machine learning algorithms. Model performance was validated by receiver operating characteristic-area under the curve (AUC), DeLong test, Brier score, calibration curve, decision curve analysis, integrated discrimination improvement, and net reclassification improvement, with SHapley Additive exPlanations adopted for model interpretation.
Results:
The multimodal fusion model yielded the highest AUC of 0.883 in the test cohort with good calibration and clinical net benefit. The T1WI model outperformed the T2WI, and whole-brain-based models were significantly superior to tumor-region models. Spatial range exerted a greater impact on predictive performance than the imaging sequence. Accordingly, an imaging-clinical integrated model (AUC = 0.890) was established with the objective of further enhancing the model's comprehensive predictive performance and clinical applicability.
Conclusion:
The CT-MRI multimodal radiomics model accurately predicts pathological subtypes of lung cancer brain metastases. Spatial extent is a critical determinant for model performance.
