NIMG-76. A DEEP LEARNING ALGORITHM FOR FULLY AUTOMATED VOLUMETRIC MEASUREMENT OF MENINGIOMA BURDEN

Abstract

Abstract PURPOSE Meningiomas are the most common primary central nervous system tumor. A subset of these tumors is multiply recurrent despite aggressive treatment with surgery or radiation and causes significant morbidity and mortality. Longitudinal measurement of meningioma burden on post-contrast MRI is the basis of treatment response assessment. As per Response Assessment in Neuro-Oncology (RANO), estimates of tumor size are based on the product of bidirectional diameters of enhancing tumor. However, these 2D segmentations as well as manual volumetric segmentations have high rates of intra- and inter-rater variability, which prohibits accurate longitudinal assessment of tumor size. A rapid and reproducible quantitative method for determining meningioma volume is needed to expedite response assessment in clinical trials. To address this unmet need, we developed a deep learning algorithm that enables fully automated 3D segmentation and volumetric assessment of meningioma burden. METHODS 351 post-contrast T1-weighted brain MRIs from 86 patients with meningioma were obtained from Massachusetts General Hospital and Dana-Farber Cancer Institute. The cohort was unique compared to prior DL segmentation models as it encompassed diverse clinical scenarios (e.g., meningioma of all grades, post-operative and post-radiated meningioma). The MRIs were resampled to 1 mm isotropic resolution and preprocessed using N4 bias field correction and zero-mean intensity normalization. The dataset was split 70%/15%/15% for training/validation/testing on a per patient level. The training data were used with manually generated tumor segmentations to train a convolutional neural network (CNN) with a U-Net architecture and the Dice loss function. RESULTS The median Dice score was 0.6758 on the test set. Additional work is ongoing to optimize performance, with primary focus directed toward improved data preprocessing and ground truth segmentation as the variability in segmentation quality contributed to the lower Dice score, highlighting the need to develop a less biased and more reliable tool to segment meningiomas.