Quantitative Physiologic MRI Parameters Combined with Innovative Machine Learning to Distinguish Glioblastoma from Solitary Brain Metastases

Motivation: There is an unmet need to develop advanced MRI based prediction models to distinguish glioblastomas (GBMs) from solitary brain metastases (BMs) with high-accuracy, as conventional MRI-techniques often yield ambiguous results. Goal(s): The objective is to discriminate GBMs from solitary BMs using physiologic MRI parameters and machine-learning based novel-methods. Approach: Employing diffusion-tensor-imaging (DTI) and dynamic-susceptibility contrast-perfusion weighted imaging (DSC-PWI), the study uses a novel machine-learning approach that integrates multiple features with hyper-tuned models to enhance pattern-recognition and prediction. Results: The innovative-method combining interacted and non-interacted features via hyper-tuned machine-learning models significantly outperformed traditional-methods, thus achieving high accuracy and reliability in differentiating GBMs from BMs. Impact: The integration of quantitative and physiologically-sensitive MRI-parameters with novel machine-learning based algorithms may be promising in distinguishing glioblastomas from solitary brain metastases. This approach may be useful in making prognostication and guiding optimal, personalized patient-treatments in the era of personalized-medicine.