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

Ken Chang, Andrew L. Beers, Harrison X. Bai, James M. Brown, K. Ina Ly, Xuejun Li, Joeky T. Senders, Vasileios K. Kavouridis, Alessandro Boaro, Chang Su, Wenya Linda Bi, Otto Rapalino, Weihua Liao, Qin Shen, Hao Zhou, Bo Xiao, Yinyan Wang, Paul J. Zhang, Marco C. Pinho, Patrick Y. WenTracy T. Batchelor, Jerrold L. Boxerman, Omar Arnaout, Bruce R. Rosen, Elizabeth R. Gerstner, Li Yang, Raymond Y. Huang, Jayashree Kalpathy-Cramer

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1412-1422
Number of pages11
JournalNeuro-oncology
Volume21
Issue number11
DOIs
StatePublished - Nov 4 2019

Keywords

  • RANO
  • deep learning
  • glioma
  • longitudinal response assessment
  • segmentation

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

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