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

Chang, Ken, Beers, Andrew, Bai, Harrison, Brown, James M, Ly, K Ina, Li, Xuejun, Senders, Joeky, Kavouridis, Vasileois, Boaro, Alessandro, Su, Chang, Bi, Wenya Linda, Rapalino, Otto, Liao, Weihua, Shen, Qin, Zhou, Hao, Xiao, Bo, Wang, Yinyan, Zhang, Paul J, Pinho, Marco C, Wen, Patrick Y, Batchelor, Tracy T, Boxerman, Jerrold L, Arnaout, Omar, Rosen, Bruce R, Gerstner, Elizabeth R, Yang, Li, Huang, Raymond Y and Kalpathy-Cramer, Jayashree (2019) Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement. Neuro-Oncology, 21 (11). ISSN 1522-8517

Full content URL: https://doi.org/10.1093/neuonc/noz106

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

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.

Keywords:deep learning, glioblastoma, MRI
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G730 Neural Computing
A Medicine and Dentistry > A300 Clinical Medicine
G Mathematical and Computer Sciences > G760 Machine Learning
B Subjects allied to Medicine > B140 Neuroscience
Divisions:College of Science > School of Computer Science
ID Code:38024
Deposited On:05 Nov 2019 10:35

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