CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma
BackgroundClear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bia...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.961779/full |
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| author | Meiyi Yang Meiyi Yang Xiaopeng He Lifeng Xu Minghui Liu Jiali Deng Xuan Cheng Yi Wei Qian Li Shang Wan Feng Zhang Lei Wu Xiaomin Wang Bin Song Ming Liu |
| author_facet | Meiyi Yang Meiyi Yang Xiaopeng He Lifeng Xu Minghui Liu Jiali Deng Xuan Cheng Yi Wei Qian Li Shang Wan Feng Zhang Lei Wu Xiaomin Wang Bin Song Ming Liu |
| author_sort | Meiyi Yang |
| collection | DOAJ |
| description | BackgroundClear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult.AimWe proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading.MethodsWe integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model.ResultsThe mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%).ConclusionA transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas). |
| first_indexed | 2024-04-12T04:24:56Z |
| format | Article |
| id | doaj.art-cd4ef0a731f04f32945565ff65e5167f |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T04:24:56Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-cd4ef0a731f04f32945565ff65e5167f2022-12-22T03:48:08ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-09-011210.3389/fonc.2022.961779961779CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinomaMeiyi Yang0Meiyi Yang1Xiaopeng He2Lifeng Xu3Minghui Liu4Jiali Deng5Xuan Cheng6Yi Wei7Qian Li8Shang Wan9Feng Zhang10Lei Wu11Xiaomin Wang12Bin Song13Ming Liu14Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, ChinaQuzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaQuzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaQuzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, ChinaBackgroundClear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult.AimWe proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading.MethodsWe integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model.ResultsThe mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%).ConclusionA transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).https://www.frontiersin.org/articles/10.3389/fonc.2022.961779/fulltumor gradingensemble learningclear cell renal cell carcinomatransformer networkdeep learning |
| spellingShingle | Meiyi Yang Meiyi Yang Xiaopeng He Lifeng Xu Minghui Liu Jiali Deng Xuan Cheng Yi Wei Qian Li Shang Wan Feng Zhang Lei Wu Xiaomin Wang Bin Song Ming Liu CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma Frontiers in Oncology tumor grading ensemble learning clear cell renal cell carcinoma transformer network deep learning |
| title | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma |
| title_full | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma |
| title_fullStr | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma |
| title_full_unstemmed | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma |
| title_short | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma |
| title_sort | ct based transformer model for non invasively predicting the fuhrman nuclear grade of clear cell renal cell carcinoma |
| topic | tumor grading ensemble learning clear cell renal cell carcinoma transformer network deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.961779/full |
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