MUSIOMICS: A multi-region radiomics framework that outperforms single-region analysis in classifying malignant...

Radiomic studies in lung cancer have primarily analyzed single-domain features extracted separately from intranodular (Zone-1) or perinodular (Zone-2) regions, potentially overlooking their biological interdependence. We developed MUSIOMICS (Multiregional Unified and Spatially Integrated Oncologic Model for Imaging-based Connected Structures), a multi-region radiomic framework, and constructed two region-dependent delta-radiomic models (Delta-1 and Delta-2). Their performance was evaluated and validated in classifying primary versus metastatic pulmonary nodules.

A total of 443 malignant pulmonary nodules (training set, n = 360; test set, n = 83) were retrospectively analyzed. Zone-1 and Zone-2 vol were delineated using LIFEx software. The MUSIOMICS framework was applied to construct two spatial delta-radiomic models that extracted features from both zones of different biological roles (e.g. Zone-1 and Zone-2). These features of different strengths were then fused into a single effective delta-feature. Predictive models were developed in the training dataset using a two-stage feature selection strategy and three classifiers (Random Forest, AdaBoost, and Support Vector Machine [SVM]). A two-sample t-test was applied to both the training and independent test datasets to identify reproducible statistically significant (RSS) delta-features. SHapley Additive exPlanations (SHAP) analysis was performed to rank feature importance and identify informative delta-features. RSS and informative delta-features together were combined to characterize the spatial delta-radiomic models.

In the independent test dataset, spatial delta-radiomic models (Delta-1: 82%, Delta-2: 81%) outperformed single-region models (Zone-1: 75%, Zone-2: 67%), producing a 6-15% improvement in predictive accuracy. Among all classifiers, Delta-1 combined with SVM achieved the highest performance (accuracy, 86%; area under receiver operating characteristic curve, 0.90). The t-test identified 58 and 48 RSS delta-features for Delta-1 and Delta-2, respectively, with 40 overlapping across both models. Among classifiers, GLCM_DV and Intensity_QCD were consistently top-ranked contributors in Delta-1, with Intensity_QCD identified as the most informative feature in both Delta-1 and Delta-2.

Spatial delta-radiomics based on MUSIOMICS integrates complementary information from biologically connected intranodular and perinodular compartments, achieving higher and more reproducible predictive performance than conventional single-zone models for characterizing malignant pulmonary nodules.