A deep learning algorithm for fully automated volumetric measurement of meningioma burden
Abstract Background We sought to develop a deep learning (DL) model to enable fully automated 3D segmentation and volumetric assessment of meningioma burden with a specific emphasis on generalizing to high-grade and posttreatment meningiomas to improve interobserver variability and decrease reader time investment in tumor response assessment. Methods In total, 450 postcontrast T1-weighted brain MRIs from 104 patients with meningiomas were obtained from Massachusetts General Hospital and Dana-Farber Cancer Institute. The cohort was unique among prior DL segmentation models in that it encompassed meningiomas of all grades, postoperative, and postradiated meningiomas. Preprocessed MRIs and manually generated tumor segmentations were used to train a U-Net with a joint Dice-cross entropy loss function. Results When tested on internal data, our model achieved a median Dice of 0.741 and a median 95th percentile Hausdorff Distance (HD95) of 26 mm on a high-grade test set and a median Dice of 0.848 and median HD95 of 1.41 mm on a test set with low-grade tumors. Lesion-wise metrics were equivalent to global metrics for low-grade tumors, which contained only single lesions, but were substantially lower for high-grade tumors, with a median lesion-wise Dice of 0.45 and median lesion-wise HD95 of 130 mm, reflecting greater difficulty delineating individual high-grade lesions. Our model also generalized well to 1000 studies from 944 patients selected from the public BraTS dataset, achieving a median Dice of 0.923 and median HD95 of 2.24 mm. Conclusions The study produced a model that addresses an unmet need for automated volumetric measurements of meningiomas and created a reliable metric for quantifying meningioma burden. In comparison to prior DL approaches, our model achieved competitive performance on external data and improved Dice scores on high-grade and posttreatment meningiomas. The trained model, volumetric evaluation code, and accompanying documentation are available online at https://github.com/mccle/tumor_segmentation.