MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier
ObjectiveTo develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.Materials and MethodsWe retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 h...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.708655/full |
| _version_ | 1818887022819934208 |
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| author | Xin-Yuan Chen Yu Zhang Yu Zhang Yu-Xing Chen Zi-Qiang Huang Xiao-Yue Xia Yi-Xin Yan Mo-Ping Xu Wen Chen Xian-long Wang Qun-Lin Chen |
| author_facet | Xin-Yuan Chen Yu Zhang Yu Zhang Yu-Xing Chen Zi-Qiang Huang Xiao-Yue Xia Yi-Xin Yan Mo-Ping Xu Wen Chen Xian-long Wang Qun-Lin Chen |
| author_sort | Xin-Yuan Chen |
| collection | DOAJ |
| description | ObjectiveTo develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.Materials and MethodsWe retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set.ResultsThe ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively.ConclusionsA machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images. |
| first_indexed | 2024-12-19T16:30:38Z |
| format | Article |
| id | doaj.art-1729c635fe8f4f298569e5735cef3812 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-19T16:30:38Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-1729c635fe8f4f298569e5735cef38122022-12-21T20:14:13ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-10-011110.3389/fonc.2021.708655708655MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning ClassifierXin-Yuan Chen0Yu Zhang1Yu Zhang2Yu-Xing Chen3Zi-Qiang Huang4Xiao-Yue Xia5Yi-Xin Yan6Mo-Ping Xu7Wen Chen8Xian-long Wang9Qun-Lin Chen10Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaObjectiveTo develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.Materials and MethodsWe retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set.ResultsThe ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively.ConclusionsA machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.https://www.frontiersin.org/articles/10.3389/fonc.2021.708655/fullmachine learningmagnetic resonance imagingtexture analysisclear cell renal cell carcinomamulti-layer perceptron algorithm |
| spellingShingle | Xin-Yuan Chen Yu Zhang Yu Zhang Yu-Xing Chen Zi-Qiang Huang Xiao-Yue Xia Yi-Xin Yan Mo-Ping Xu Wen Chen Xian-long Wang Qun-Lin Chen MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier Frontiers in Oncology machine learning magnetic resonance imaging texture analysis clear cell renal cell carcinoma multi-layer perceptron algorithm |
| title | MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier |
| title_full | MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier |
| title_fullStr | MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier |
| title_full_unstemmed | MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier |
| title_short | MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier |
| title_sort | mri based grading of clear cell renal cell carcinoma using a machine learning classifier |
| topic | machine learning magnetic resonance imaging texture analysis clear cell renal cell carcinoma multi-layer perceptron algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.708655/full |
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