Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging

By Ken Chang, H. Bai, Hao Zhou, Chang Su, W. Bi, Ena Agbodza, V. Kavouridis, J. Senders, Alessandro Boaro, Andrew L Beers and 20 more. 2017

336 citations recorded by the source corpus.

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data. Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II–IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073–81. ©2017 AACR.

Subfields: Genetics, Neurology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation (2018, background): “Accurate segmentation of these subregions is considered the basis for extracting quantitative imaging features corresponding to specific anatomical regions, which when integrated using advanced computational approaches, can enable assessment of this radiographic heterogeneity, evaluation of disease ”
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas (2018, methodology): “Recently, Chang et al 34 used a 34-layer residual neural network to predict IDH status with up to 89% accuracy using MR imaging in combination with patient age.”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, background): “(7, 41-54) Figure 1 illustrates the many opportunities offered by radiomics prediction models for improved risk stratification of patients from a point-of-care perspective.”
Harnessing multimodal data integration to advance precision oncology (2021, background): “…status of glioma from magnetic resonance imaging (MRI), pathological grade of prostate cancer from MRI, epidermal growth factor receptor ( EGFR ) mutational status of lung adenocarcinoma from CT and BRCA1 or BRCA2 mutational status of breast cancer from full-field digital mammography 21–25 .”
Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, methodology): “96,127 Moreover, unsupervised deep learning methods are showing promise in discerning molecular subgroups in glioblastoma with differential prognoses.”
Deep learning with convolutional neural network in radiology (2018, neutral): “89 when age was also incorporated [49].”