Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.

BACKGROUND

Early identification of the primary tumor types in brain metastases (BMs) is crucial for developing effective treatment strategies. This study aimed to evaluate the potential of multiparametric MRI (mpMRI)-based habitat radiomics analysis in differentiating the pathological types of BMs.

MATERIALS AND

METHODS

Pre-treatment MR images from 328 BMs patients at a single center were retrospectively collected and randomly divided into a training set (229 cases) and a test set (99 cases). Tumor regions were manually segmented on contrast-enhanced T1-weighted images (CE-T1WI), and the K-means clustering algorithm was employed to classify the tumor into four distinct sub-regions. Radiomics features were extracted separately from each sub-region to construct the habitat model. The resulting habitat model was compared alongside a traditional whole-tumor radiomics model, a clinical model, and a combined model (integrating habitat and clinical variables). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), as well as accuracy.

RESULTS

The combined model achieved the highest overall performance (training AUC: 0.992, accuracy: 0.952; test AUC: 0.939, accuracy: 0.845), outperforming the habitat model (training AUC: 0.965, accuracy: 0.876; test AUC: 0.888, accuracy: 0.835), traditional radiomics model (training AUC: 0.984, accuracy: 0.866; test AUC: 0.884, accuracy: 0.754), and clinical model (training AUC: 0.788, accuracy: 0.731; test AUC: 0.716, accuracy: 0.653). However, class-specific evaluation revealed substantial performance variation, with F1-scores of 0.874 for lung cancer BMs, but only 0.333 and 0.200 for breast and gastrointestinal cancer BMs, respectively.

CONCLUSIONS

This study demonstrates that while habitat radiomics shows potential for classifying BMs, its current performance is constrained by class imbalance and scanner heterogeneity. Consequently, our primary contribution lies in providing a critical baseline and a clear direction, prioritizing data-centric solutions as the essential next step for the field.