Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images
BackgroundBreast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRC...
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.661109/full |
| _version_ | 1818885161357410304 |
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| author | Xiaoxiao Wang Chong Zou Yi Zhang Yi Zhang Xiuqing Li Chenxi Wang Fei Ke Jie Chen Wei Wang Dian Wang Xinyu Xu Xinyu Xu Ling Xie Yifen Zhang |
| author_facet | Xiaoxiao Wang Chong Zou Yi Zhang Yi Zhang Xiuqing Li Chenxi Wang Fei Ke Jie Chen Wei Wang Dian Wang Xinyu Xu Xinyu Xu Ling Xie Yifen Zhang |
| author_sort | Xiaoxiao Wang |
| collection | DOAJ |
| description | BackgroundBreast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&E stains and the patients’ gBRCA mutation status.MethodsIn this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.ResultsA trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction.ConclusionIn this paper, the combination of pathology and molecular omics was used to establish the gBRCA mutation risk prediction model, revealing the correlation between the whole-slide histopathological images and gRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expand. The findings demonstrated that deep CNNs could be used to assist pathologists in the detection of gene mutation in breast cancer. |
| first_indexed | 2024-12-19T16:01:02Z |
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| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-19T16:01:02Z |
| publishDate | 2021-07-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-6b84615ccabd4771ab18248c23230b5b2022-12-21T20:14:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-07-011210.3389/fgene.2021.661109661109Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology ImagesXiaoxiao Wang0Chong Zou1Yi Zhang2Yi Zhang3Xiuqing Li4Chenxi Wang5Fei Ke6Jie Chen7Wei Wang8Dian Wang9Xinyu Xu10Xinyu Xu11Ling Xie12Yifen Zhang13Department of GCP Research Center, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of GCP Research Center, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Cancer Hospital, Nanjing, ChinaJiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of GCP Research Center, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of GCP Research Center, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Cancer Hospital, Nanjing, ChinaJiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Pathology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, ChinaBackgroundBreast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&E stains and the patients’ gBRCA mutation status.MethodsIn this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.ResultsA trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction.ConclusionIn this paper, the combination of pathology and molecular omics was used to establish the gBRCA mutation risk prediction model, revealing the correlation between the whole-slide histopathological images and gRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expand. The findings demonstrated that deep CNNs could be used to assist pathologists in the detection of gene mutation in breast cancer.https://www.frontiersin.org/articles/10.3389/fgene.2021.661109/fullbreast cancerBRCA genedeep learningartificial intelligencedigital pathology |
| spellingShingle | Xiaoxiao Wang Chong Zou Yi Zhang Yi Zhang Xiuqing Li Chenxi Wang Fei Ke Jie Chen Wei Wang Dian Wang Xinyu Xu Xinyu Xu Ling Xie Yifen Zhang Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images Frontiers in Genetics breast cancer BRCA gene deep learning artificial intelligence digital pathology |
| title | Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images |
| title_full | Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images |
| title_fullStr | Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images |
| title_full_unstemmed | Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images |
| title_short | Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images |
| title_sort | prediction of brca gene mutation in breast cancer based on deep learning and histopathology images |
| topic | breast cancer BRCA gene deep learning artificial intelligence digital pathology |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.661109/full |
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