A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases

Novel radiotherapeutic strategies in the management of brain metastases: Challenging the dogma

The role of radiation therapy in the management of brain metastasis is evolving. Advancements in machine learning techniques have improved our ability to both detect brain metastasis and our ability to contour substructures of the brain as critical organs at risk. Advanced imaging with PET tracers and magnetic resonance imaging-based artificial intelligence models can now predict tumor control and differentiate tumor progression from radiation necrosis. These advancements will help to optimize dose and fractionation for each patient's lesion based on tumor size, histology, systemic therapy, medical comorbidities/patient genetics, and tumor molecular features. This review will discuss the current state of brain directed radiation for brain metastasis. We will also discuss future directions to improve the precision of stereotactic radiosurgery and optimize whole brain radiation techniques to improve local tumor control and prevent cognitive decline without forming necrosis.

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline

PURPOSE

Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans.

METHODS

SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5 mm, 834 > 10 mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations.

RESULTS

SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10 mm, 0.90±0.22 and 0.97±0.12 for metastases > 5 mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10 mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13.

CONCLUSION

Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.