Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning

Background Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time‐consuming and impeded by intra‐ and interrater variabilities. Purpose To investigate the performance of a deep‐learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. Study Type Retrospective. Population Sixty‐nine scans (at initial and/or follow‐up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. Field Strength/Sequence T1−/T2‐weighted, T1‐weighted contrast‐enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. Assessment Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. Statistical Tests Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank‐sum test for comparison of DSCs obtained in initial and follow‐up imaging. Results The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3, median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3, median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P