MRI-based Identification and Classification of Major Intracranial Tumor Types by Using a 3D Convolutional Neural Network: A Retrospective Multi-institutional Analysis

Satrajit Chakrabarty; Aristeidis Sotiras; Mikhail Milchenko; Pamela LaMontagne; Michael Hileman; Daniel Marcus

doi:10.1148/ryai.2021200301

MRI-based Identification and Classification of Major Intracranial Tumor Types by Using a 3D Convolutional Neural Network: A Retrospective Multi-institutional Analysis

Radiol Artif Intell. 2021 Aug 11;3(5):e200301. doi: 10.1148/ryai.2021200301. eCollection 2021 Sep.

Authors

Satrajit Chakrabarty¹, Aristeidis Sotiras¹, Mikhail Milchenko¹, Pamela LaMontagne¹, Michael Hileman¹, Daniel Marcus¹

Affiliation

¹ Department of Electrical and Systems Engineering, Washington University in St Louis, 1 Brookings Dr, St Louis, MO 63130 (S.C.); Department of Radiology and Institute for Informatics, Washington University School of Medicine, St Louis, Mo (A.S.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (M.M., P.L., M.H., D.M.).

Abstract

Purpose: To develop an algorithm to classify postcontrast T1-weighted MRI scans by tumor classes (high-grade glioma, low-grade glioma [LGG], brain metastasis, meningioma, pituitary adenoma, and acoustic neuroma) and a healthy tissue (HLTH) class.

Materials and methods: In this retrospective study, preoperative postcontrast T1-weighted MR scans from four publicly available datasets-the Brain Tumor Image Segmentation dataset (n = 378), the LGG-1p19q dataset (n = 145), The Cancer Genome Atlas Glioblastoma Multiforme dataset (n = 141), and The Cancer Genome Atlas Low Grade Glioma dataset (n = 68)-and an internal clinical dataset (n = 1373) were used. In all, a total of 2105 images were split into a training dataset (n = 1396), an internal test set (n = 361), and an external test dataset (n = 348). A convolutional neural network was trained to classify the tumor type and to discriminate between images depicting HLTH and images depicting tumors. The performance of the model was evaluated by using cross-validation, internal testing, and external testing. Feature maps were plotted to visualize network attention. The accuracy, positive predictive value (PPV), negative predictive value, sensitivity, specificity, F1 score, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (AUPRC) were calculated.

Results: On the internal test dataset, across the seven different classes, the sensitivities, PPVs, AUCs, and AUPRCs ranged from 87% to 100%, 85% to 100%, 0.98 to 1.00, and 0.91 to 1.00, respectively. On the external data, they ranged from 91% to 97%, 73% to 99%, 0.97 to 0.98, and 0.9 to 1.0, respectively.

Conclusion: The developed model was capable of classifying postcontrast T1-weighted MRI scans of different intracranial tumor types and discriminating images depicting pathologic conditions from images depicting HLTH.Keywords MR-Imaging, CNS, Brain/Brain Stem, Diagnosis/Classification/Application Domain, Supervised Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

Keywords: Brain/Brain Stem; CNS; Convolutional Neural Network; Deep Learning Algorithms; Diagnosis/Classification/Application Domain; MR-Imaging; Machine Learning Algorithms; Supervised Learning.

2021 by the Radiological Society of North America, Inc.