Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison

Abstract

Background: The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms.

Methods: To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used.

Results: The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast ET, 0.77 for edema, and 0.81 for surgical cavities. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort.

Conclusions: Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.

Keywords: deep learning; glioblastomas; neural network; postoperative; segmentation.

Figure 1.

A descriptive example of the segmentations predicted by the models included in the comparison. The segmentations include the following labels: residual enhancing tumor, edema, and surgical cavity. The predicted labels are shown as a pair of images overlaid on (left) T1ce and (right) T2w. The visual distinction between the labels is consistent across the images for clarity.

Figure 2.

Examples of predictions made by the RH-GlioSeg-nnU-Net model. The classification status of the patient’s resection extension (EOR) is indicated as either correct or incorrect. The ground truth and predicted segmentations are overlaid on T1 contrast-enhancing (T1ce) and T1 weighted (T1w) images to facilitate differentiation between blood remnants and residual enhancing tumors. The last column shows Dice Similarity Coefficient values for each label: enhancing tumor (ET), edema (ED), and surgical cavity (CAV). For cases with an empty label, the result is expressed as a classification task using the Correct Classification Rate (CCR).