IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY

Abstract BACKGROUND The prognosis of brain tumors is variable in clinical practice if it only relies on human interpretation of magnetic resonance imaging (MRI). The automatic segmentation of brain tumors in MRI enables quantitative analysis in support of clinical trials and personalized patient care. We developed benchmarked deep learning-based tools that are generalizable to the volumetric quantification of various tumor types across diverse populations. METHODS We participated in the well-established international brain tumor segmentation challenge (BraTS 2023) and benchmarking competition. The challenge made available 4,500 multi-national brain tumor cases with multi-sequence MRIs, including pediatric high-grade gliomas (PED), i.e., high-grade astrocytoma and diffuse midline glioma, and adult gliomas, brain metastases (MET) and intracranial meningiomas (MEN). Each case comprises four MRI volumes: T1, contrast-enhanced T1, T2, and T2-FLAIR. Manual segmentations were provided to establish ground truth for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Our framework used a model ensemble strategy based on two state-of-the-art deep learning models: a convolutional neural network (nnU-Net) and a vision transformer (Swin UNETR) and was tested for broader applicability across multiple tumor types. The framework was trained on 99, 1,000, and 165 cases and validated on 45, 141, and 31 unseen cases for PED, MEN, and MET, respectively. Automatic segmentations were evaluated by lesion-wise volume overlap (Dice similarity score, DSC) and Hausdorff distance (HD). RESULTS In the evaluation on independent unseen test datasets, our automatic tool was ranked first for PED, third for MEN, and fourth for MET volumetric analysis. Our method resulted in PED lesion-wise DSC of 0.733, 0.782, 0.817 and HD (mm) of 75.93, 25.54, 24.18 for ET, TC, and WT, respectively. CONCLUSIONS These brain tumor volumetric analysis tools are readily available to be efficiently tested on diverse datasets. Automatic MRI analysis provides consistent quantitative data for multi-institutional protocols and clinical trials.