Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging

Abstract

IMPORTANCEDeep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1-and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy inbrain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan

Multiparametric MRI

“…Furthermore, Zhu et al 159 compared the performances of 1 multi-input and 2 single-input models based on DWI and DCE MRI in differentiating benign from malignant breast lesions and showed that the combination of DCE with DWI was superior to using a single sequence. Previous studies combined the segmentation and diagnosis of tumors in organs such as the prostate, 133 breast, 159 and brain [160][161][162] using deep learning based on multiparametric MRI. Such "1-stop" deep learning algorithms may be widely used in the near future as a computer-aided diagnosis tool to assist clinicians and radiologists by speeding up segmentation, training residents, and providing a preliminary diagnosis.…”

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

“…However, AI can help augment the ability of a less experienced or nonsubspecialty-trained radiologist in providing adequate differential considerations. A large retrospective study evaluating an AI algorithm trained to classify 18 different types of brain tumors based on MRI demonstrated a 12.0% increase in accuracy with AI assistance even for subspecialty-trained neuroradiologists and demonstrated generalizability to external data [14 ▪▪ ]. Shin et al explored the use of deep learning applied to MRI specifically in the emergency neuroradiology setting, demonstrating high performance in discriminating between tumor and nontumorous conditions, as well as providing a referral and management recommendation [15 ▪ ].…”

Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches

“…Several studies have demonstrated promising results in organ segmentation ( Akkus et al, 2017 ; Livne et al, 2019 ; Hssayeni et al, 2020 ; Meddeb et al, 2021 ) and disease classification ( Artzi et al, 2019 ; Burduja et al, 2020 ; Li et al, 2020 ; Meddeb et al, 2022 ; Nishio et al, 2022 ). In neuro-imaging, deep learning models have been successfully applied to intracranial hemorrhage detection and segmentation using CT images ( Xu et al, 2021 ), as well as brain tumor classification using magnetic resonance imaging (MRI) ( Gao et al, 2022 ).…”