Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement.

Research paper by Ken K Chang, Andrew L AL Beers, Harrison X HX Bai, James M JM Brown, K Ina KI Ly, Xuejun X Li, Joeky T JT Senders, Vasileios K VK Kavouridis, Alessandro A Boaro, Chang C Su, Wenya Linda WL Bi, Otto O Rapalino, Weihua W Liao, Qin Q Shen, Hao H Zhou, et al.

Indexed on: 15 Feb '20Published on: 14 Jun '19Published in: Neuro-oncology


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 FLAIR hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bi-dimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Two cohorts of patients were used for this study. One consisted of 843 pre-operative MRIs from 843 patients with low- or high-grade gliomas from four institutions and the second consisted 713 longitudinal, post-operative MRI visits from 54 patients with newly diagnosed glioblastomas (each with two pre-treatment "baseline" MRIs) from one institution. 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, respectivelyon the cohort of post-operative 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 pre-operative FLAIR hyperintensity, post-operative FLAIR hyperintensity, and post-operative contrast-enhancing tumor volumes, respectively. Lastly, the ICC for comparing manually and automatically derived longitudinal changes in tumor burden was 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex post-treatment settings, although further validation in multi-center clinical trials will be needed prior to widespread implementation. © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.