Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement
Ken Chang, MSE Andrew L Beers, BA Harrison X Bai, MD James M Brown, PhD K Ina Ly, MD Xuejun Li, MD Joeky T Senders, BS Vasileios K Kavouridis, MD Alessandro Boaro, MD Chang Su, BSE ... Show more
Neuro-Oncology, noz106, https://doi.org/10.1093/neuonc/noz106
Published: 13 June 2019 Article history
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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 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).
Methods
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.
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, 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.
Conclusions
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.
Deep Learning, Glioma, Segmentation, Longitudinal response assessment, RANO
Topic: magnetic resonance imaging glioma neoplasms tumor volume fluid attenuated inversion recovery deep learning
Issue Section: Basic and Translational Investigations
Ken Chang, MSE Andrew L Beers, BA Harrison X Bai, MD James M Brown, PhD K Ina Ly, MD Xuejun Li, MD Joeky T Senders, BS Vasileios K Kavouridis, MD Alessandro Boaro, MD Chang Su, BSE ... Show more
Neuro-Oncology, noz106, https://doi.org/10.1093/neuonc/noz106
Published: 13 June 2019 Article history
pdfPDF Split View Cite
Permissions Icon Permissions
Share
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 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).
Methods
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.
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, 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.
Conclusions
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.
Deep Learning, Glioma, Segmentation, Longitudinal response assessment, RANO
Topic: magnetic resonance imaging glioma neoplasms tumor volume fluid attenuated inversion recovery deep learning
Issue Section: Basic and Translational Investigations
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