P04.06 Can BraTS Toolkit Brain Tumor Segmentation Algorithms be relevant when dealing with post-operative MRI in clinical practice?

Abstract

Segmentation of brain tumors is crucial for radiotherapy plans and treatment outcome evaluation. As manual segmentation (MS) is a time-consuming task, many algorithms to automate the process were proposed over the past decades. BraTS toolkit (BTK) offers a solution for automatic brain tumor segmentation in 3 steps: a Preprocessor for image conversion and registration, a Segmentor generating segmentations from 4 deep-learning based algorithms and a Fusionator combining the results. As most algorithms published, BTK was trained on pre-operative data. Yet, as surgery is the first treatment of glioblastomas, most MRIs in clinical practice are post-operative images. This study aimed to assess whether segmentation of post-operative brain tumors could benefit from an initial automatic segmentation (AS) using BTK.MRI dataset from the multicenter, STERIMGLI clinical trial provided 92 series from 25 patients with a unifocal recurrence of glioblastoma. AS were generated using BTK Preprocessor and Segmentor. Out of the 4 algorithms output, the best AS was selected after visual appraisal. AS contained 3 labels: T1w contrast enhanced tumor (ET), flair edema (ED) and non-enhanced tumor (NET). AS were then reviewed by a radiation oncologist and a neuroradiologist to produce the MS. ET and ED were corrected; surgical cavity (SC) was also segmented, either from the NET label or de novo. Dice-score, Hausdorff Distance (HD) and Average Hausdorff Distance (AHD) were used to quantify the similarity between AS and MS for each label.AS succeeded to label 89.3% (82/92), 100% (92/92) and 85.8% (79/92) of ET, ED and NET respectively. Among the 4 algorithms in BTK, Zyx_2019 produced 36% of AS, mic-dkfz 32%, xfeng 18%, lfb_rwth 14%. Mean Dice-scores of 75.8%, 94.8% were found for ET and ED respectively. Mean HD and AHD were 25.2mm (±39.7), 2.8mm (±10.9) for ET; 14.9mm (±25.8), 0.9mm (±5.8) for ED. Concerning SC, Dice-scores were <0.1 for 49% (39/79), >0.6 for 30% (42/79).BraTS Toolkit was trained to segment necrosis as the label NET. Still, it detected the surgical cavity and saved time for the MS in 51% of post-operative cases. Even though BTK was designed to segment pre-operative brain tumors, similarity metrics show that minimal or no manual corrections are necessary most of the time when used to segment ET and ED on post-operative MRI acquired in clinical routine. The development of a unique AS tool for pre and post-operative images would be useful in clinical practice.