Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
This addresses the need for automated, reliable tumor segmentation in post-operative MRI for glioblastoma patients, reducing variability in current manual methods, though it is incremental as it adapts existing neural network architectures to a new task.
The study tackled the problem of accurately segmenting residual glioblastoma tumors in early post-operative MRI scans to improve estimation of resection extent, achieving a 61% Dice score and about 80% balanced accuracy with models that generalized across multiple hospitals and matched human expert performance.
Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.