Keywords: Diagnosis/Prediction, Tumor, Glioblastomas, Solitary_Brain_Metastases, Quantitative_Physiologic, MRI, Innovative_Hyper-tuned_Machine_Learning
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.
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