Jay-Jiguang Zhu1, Kang-lin Jiang2, Tanjida Kabir2, Luis Nunez3, Juan Rodriguez Quintero4, Frank Yu Cai3, Daniel Yu-Chun Hsu2, Octavio Arevalo5, Kangyi Zhao6, Jackie Jiaqi Zhang7, Roy Riascos-Castaneda3, Xiaoqian Jiang2, shayan shams8
1Neurosurgery, 2School of Biomedical Informatics, 3Department of Diagnostic and Interventional Imaging, 4Neurology and Neurosurgery, Univ of Texas Health Science Center in Houston, 5Radiology, LSU HEALTH SHREVEPORT, 6Statistics, University of Pittsburgh, 7Family Medicine, Lone Star Family Health Center, 8Applied Data Science, San Jose State University
Objective:
To test the reliability of automatic segmentation models on post-operative MRIs in glioblastoma evaluation.
Background:
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.
Design/Methods:
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.
Results:
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).
Conclusions:
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.