Deep Machine Learning Algorithms in Glioblastoma (GBM) MRI evaluation (PL4.005)

Abstract

<h3>Objective:</h3> To test the reliability of automatic segmentation models on post-operative MRIs in glioblastoma evaluation. <h3>Background:</h3> The extent of tumor resection in glioblastoma is an independent prognostic factor associated with clinical outcomes. Because radiological assessment to identify residual tumoral components can be time-consuming and subject to inter-rater variability, the implementation of artificial intelligence (AI) may improve radiological assessment. Hereby, we evaluate performance of different automatic brain tumor segmentation algorithms trained on pre-operative MRI datasets, on post-operative MRI scans of patients with glioblastoma. <h3>Design/Methods:</h3> We evaluated the performance of ten different deep-learning models designed for automated brain tumor segmentation. Models were trained using a publicly available dataset from the Brain Tumor Segmentation (BRaTS) Challenge 2020 and was tested in our institutional MRI scans including pre-operative and post-operative MRI examinations. Semiautomatic segmentation was performed in the institutional scans and was used as the ground truth. Dice Similarity Coefficient (DSC) was used to compare each model’s performance against the ground truth. <h3>Results:</h3> A total of 329 MR examinations were used for training purposes and a separate 100 scans were used to test the deep-learning algorithms. Overall, models demonstrated better performance on pre-operative scans. Among post-operative MRI studies, Knowledge Distillation algorithm exhibited the best performance with a DSC score of 0.836 for whole tumor segmentation. When evaluating specific regions within the tumor, Knowledge Distillation demonstrated a DSC of 0.82 for the FLAIR hyperintense region, 3D Dilated Multi-Fiber Network achieved a DSC of 0.88 in the contrast-enhancing tumoral region, and 3D U-Net exhibited the highest DCS for the non-enhancing core (0.84). <h3>Conclusions:</h3> Deep machine learning has the potential to quantify enhancing and FLAIR volumes in brain MRI from glioblastoma patients with high accuracy. Current automatic segmentation models can perform automatic segmentation on post-operative MRI scans with an 88% accuracy. Further investigations are needed to improve this promising AI technology. <b>Disclosure:</b> An immediate family member of Dr. Zhu has stock in Abbott lab. The institution of Dr. Zhu has received research support from NRG NIH. The institution of Dr. Zhu has received research support from Novocure, Inc. The institution of Dr. Zhu has received research support from Five Prime pharmaceutical. The institution of Dr. Zhu has received research support from Denovo, inc. The institution of Dr. Zhu has received research support from Boston Biomedical Sumitomo Dainippon Pharma Global Oncology. The institution of Dr. Zhu has received research support from CNS pharmaceutical. Mr. HSIEH has nothing to disclose. Ms. Kabir has nothing to disclose. Dr. Nunez has nothing to disclose. Carlos Rodriguez Quintero has nothing to disclose. Dr. Cai has nothing to disclose. Dr. Hsu has nothing to disclose. Dr. Arevalo has nothing to disclose. Kangyi Zhao has nothing to disclose. Jackie Jiaqi Zhang has nothing to disclose. Roy Riascos-Castaneda has nothing to disclose. The institution of Dr. Jiang has received research support from NIH. The institution of Dr. Jiang has received research support from CPRIT. The institution of Dr. Jiang has received research support from NSF. shayan shams has nothing to disclose.