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Buda M, AlBadawy EA, Saha A, Mazurowski MA. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiol Artif Intell 2020; 2:e180050. [PMID: 33937809 DOI: 10.1148/ryai.2019180050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 08/06/2019] [Accepted: 08/30/2019] [Indexed: 11/11/2022]
Abstract
PURPOSE To employ deep learning to predict genomic subtypes of lower-grade glioma (LLG) tumors based on their appearance at MRI. MATERIALS AND METHODS Imaging data from The Cancer Imaging Archive and genomic data from The Cancer Genome Atlas from 110 patients from five institutions with lower-grade gliomas (World Health Organization grade II and III) were used in this study. A convolutional neural network was trained to predict tumor genomic subtype based on the MRI of the tumor. Two different deep learning approaches were tested: training from random initialization and transfer learning. Deep learning models were pretrained on glioblastoma MRI, instead of natural images, to determine if performance was improved for the detection of LGGs. The models were evaluated using area under the receiver operating characteristic curve (AUC) with cross-validation. Imaging data and annotations used in this study are publicly available. RESULTS The best performing model was based on transfer learning from glioblastoma MRI. It achieved AUC of 0.730 (95% confidence interval [CI]: 0.605, 0.844) for discriminating cluster-of-clusters 2 from others. For the same task, a network trained from scratch achieved an AUC of 0.680 (95% CI: 0.538, 0.811), whereas a model pretrained on natural images achieved an AUC of 0.640 (95% CI: 0.521, 0.763). CONCLUSION These findings show the potential of utilizing deep learning to identify relationships between cancer imaging and cancer genomics in LGGs. However, more accurate models are needed to justify clinical use of such tools, which might be obtained using substantially larger training datasets.Supplemental material is available for this article.© RSNA, 2020.
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Affiliation(s)
- Mateusz Buda
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Suite 302, Durham, NC 27705 (M.B., E.A.A., A.S., M.A.M.); Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.); and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (M.A.M.)
| | - Ehab A AlBadawy
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Suite 302, Durham, NC 27705 (M.B., E.A.A., A.S., M.A.M.); Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.); and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (M.A.M.)
| | - Ashirbani Saha
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Suite 302, Durham, NC 27705 (M.B., E.A.A., A.S., M.A.M.); Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.); and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (M.A.M.)
| | - Maciej A Mazurowski
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Suite 302, Durham, NC 27705 (M.B., E.A.A., A.S., M.A.M.); Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.); and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (M.A.M.)
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AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys 2018; 45:1150-1158. [PMID: 29356028 DOI: 10.1002/mp.12752] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 12/13/2017] [Accepted: 12/14/2017] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND PURPOSE Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs. METHODS We selected 44 glioblastoma (GBM) patients from two institutions in The Cancer Imaging Archive dataset. The images were manually annotated by outlining each tumor component to form ground truth. To automatically segment the tumors in each patient, we trained three CNNs: (a) one using data for patients from the same institution as the test data, (b) one using data for the patients from the other institution and (c) one using data for the patients from both of the institutions. The performance of the trained models was evaluated using Dice similarity coefficients as well as Average Hausdorff Distance between the ground truth and automatic segmentations. The 10-fold cross-validation scheme was used to compare the performance of different approaches. RESULTS Performance of the model significantly decreased (P < 0.0001) when it was trained on data from a different institution (dice coefficients: 0.68 ± 0.19 and 0.59 ± 0.19) as compared to training with data from the same institution (dice coefficients: 0.72 ± 0.17 and 0.76 ± 0.12). This trend persisted for segmentation of the entire tumor as well as its individual components. CONCLUSIONS There is a very strong effect of selecting data for training on performance of CNNs in a multi-institutional setting. Determination of the reasons behind this effect requires additional comprehensive investigation.
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Affiliation(s)
- Ehab A AlBadawy
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ashirbani Saha
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Maciej A Mazurowski
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.,Duke University Medical Physics Program, Durham, NC, USA
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