Imaging Evaluation of Plexiform Neurofibromas in Neurofibromatosis Type 1: A Survey-Based Assessment.

OBJECTIVE: To assess imaging utilization practices across clinical specialists in neurofibromatosis type 1 (NF1) for the evaluation of symptomatic and asymptomatic children and adults with or without plexiform neurofibromas (PN). METHODS: An institutional review board-exempt survey was administered to medical practitioners caring for individuals with NF1 at the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) meeting in September 2019. The survey included questions on respondent demographic data (9 questions), type of imaging obtained for asymptomatic (4 questions) and symptomatic (4 questions) people with and without PN, and utilization of diffusion-weighted imaging (2 questions). RESULTS: Thirty practitioners participated in the survey. Most were academic neuro-oncologists at high-volume (>10 patients/week) NF1 centers. Of 30 respondents, 26 had access to whole-body MRI (WB-MRI). The most common approach to an asymptomatic person without PN was no imaging (adults: 57% [17/30]; children: 50% [15/30]), followed by a screening WB-MRI (adults: 20% [6/30]; children: 26.7% [8/30]). The most common approach to a person with symptoms or known PN was regional MRI (adults: 90% [27/30]; children: 93% [28/30]), followed by WB-MRI (adults: 20% [6/30]; children: 36.7% [11/30]). WB-MRI was most often obtained to evaluate a symptomatic child with PN (37% [11/30]). CONCLUSIONS: More than 90% of practitioners indicated they would obtain a regional MRI in a symptomatic patient without known or visible PN. Otherwise, there was little consensus on imaging practices. Given the high prevalence of PN and risk of malignant conversion in this patient population, there is a need to define imaging-based guidelines for optimal clinical care and the design of future clinical trials.

Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). METHODS: Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. RESULTS: The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. CONCLUSIONS: Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.