For the external test dataset, which included only two tumor types (high-grade glioma and low-grade glioma), the model had an accuracy of 91.95%.
"These results suggest that deep learning is a promising approach for automated classification and evaluation of brain tumors," Chakrabarty said. "The model achieved high accuracy on a heterogeneous dataset and showed excellent generalization capabilities on unseen testing data."
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Chakrabarty said the 3D deep learning model comes closer to the goal of an end-to-end, automated workflow by improving upon existing 2D approaches, which require radiologists to manually delineate, or characterize, the tumor area on an MRI scan before machine processing. The convolutional neural network eliminates the tedious and labor-intensive step of tumor segmentation prior to classification.
Dr. Sotiras, a co-developer of the model, said it can be extended to other brain tumor types or neurological disorders, potentially providing a pathway to augment much of the neuroradiology workflow.
"This network is the first step toward developing an artificial intelligence-augmented radiology workflow that can support image interpretation by providing quantitative information and statistics," Chakrabarty added.
"MRI-based Identification and Classification of Major Intracranial Tumor Types Using a 3D Convolutional Neural Network: A Retrospective Multi-Institutional Analysis." Collaborating with Satrajit Chakrabarty and Drs. Sotiras and Marcus were Mikhail Milchenko, Ph.D., Pamela LaMontagne, Ph.D., and Michael Hileman, B.S.
Radiology: Artificial Intelligence is edited by Charles E. Kahn Jr., M.D., M.S., Perelman School of Medicine at the University of Pennsylvania, and owned and published by the Radiological Society of North America, Inc. (https://pubs.rsna.org/journal/ai)
About RSNA
RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois.
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