A Multiparametric MRI‑Based Deep Learning Model for Binary Triage of Lung Versus Non‑lung Brain Metastases: A Multicenter Proof‑of‑Concept Study.

Identifying the origin of brain metastases (BM) is essential for personalized treatment, particularly in patients with carcinoma of unknown primary or multiple primary neoplasms. We aimed to develop a multiparametric MRI-based three-dimensional (3D) deep learning (DL) fusion model for noninvasive differentiation of lung cancer (LC) versus non-lung cancer (NLC) origin BM. A total of 458 patients with 585 BM lesions from three centers were retrospectively included. A 3D DL model was developed using paired CE-T1 and T2-FLAIR sequences. The model employs a shared-weight Siamese encoder to extract cross-modal spatial features, combined with spatial attention mechanisms that highlight diagnostically relevant tumor regions. To capture intratumoral heterogeneity, Local Moran's I spatial autocorrelation analysis was used to partition each lesion into four biologically interpretable habitat subregions, generating subregion-level feature tokens. Cross-modal attention fusion was then applied to integrate complementary information from both sequences at both whole-lesion and subregion levels. Model robustness was evaluated using sensitivity analyses across histologic subtypes and at the patient level. Ablation studies were conducted to compare multimodal and single-modality performance and a multi-reader study to assess the model's clinical utility in assisting radiologists. The fusion model achieved an area under the receiver operating characteristic curve (AUC) of 0.925, significantly outperforming single-modality models using CE-T1 (AUC = 0.817) and T2-FLAIR (AUC = 0.802). Sensitivity analyses demonstrated robust performance across all subtypes (AUCs, 0.895-0.956). The patient-level analysis yielded an AUC of 0.942. Radiologists' performance improved with model assistance (accuracy trainee, 0.500-0.583; experienced, 0.595-0.679; expert, 0.690-0.845). The proposed 3D DL fusion model demonstrated strong performance in differentiating LC from NLC BM and improved radiologists' accuracy, supporting its potential as a noninvasive decision-support tool for BM origin assessment.