Format

Send to

Choose Destination
Neuro Oncol. 2019 Jun 13. pii: noz106. doi: 10.1093/neuonc/noz106. [Epub ahead of print]

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

Author information

1
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
2
Department of Radiology, Hospital of the University of Pennsylvania, PA, USA.
3
Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, MA, USA.
4
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan China.
5
Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, MA, USA.
6
Yale School of Medicine, New Haven, CT,USA.
7
Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.
8
Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
9
Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
10
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
11
Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
12
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
13
Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, PA, USA.
14
Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX​.
15
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
16
Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
17
Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI.
18
Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
19
Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.

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.

KEYWORDS:

Deep Learning; Glioma; Longitudinal response assessment; RANO; Segmentation

PMID:
31190077
DOI:
10.1093/neuonc/noz106

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center