Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images
BackgroundPredicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.ObjectiveTo explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.899897/full |
| _version_ | 1818235733256699904 |
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| author | Jing Qian Ling Yang Su Hu Siqian Gu Juan Ye Zhenkai Li Hongdi Du Hailin Shen |
| author_facet | Jing Qian Ling Yang Su Hu Siqian Gu Juan Ye Zhenkai Li Hongdi Du Hailin Shen |
| author_sort | Jing Qian |
| collection | DOAJ |
| description | BackgroundPredicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.ObjectiveTo explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.MethodsPatients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.ResultsOf the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.ConclusionCombining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance. |
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| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-12T11:58:39Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-3f3a15a8112340abb35eae568eb94bd12022-12-22T00:25:08ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.899897899897Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT ImagesJing Qian0Ling Yang1Su Hu2Siqian Gu3Juan Ye4Zhenkai Li5Hongdi Du6Hailin Shen7Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, ChinaDepartment of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, ChinaDepartment of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, ChinaDepartment of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, ChinaBackgroundPredicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.ObjectiveTo explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.MethodsPatients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.ResultsOf the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.ConclusionCombining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.https://www.frontiersin.org/articles/10.3389/fonc.2022.899897/fullbladder cancerrecurrencemultiphase CT imagesradiomicsretrospective studies |
| spellingShingle | Jing Qian Ling Yang Su Hu Siqian Gu Juan Ye Zhenkai Li Hongdi Du Hailin Shen Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images Frontiers in Oncology bladder cancer recurrence multiphase CT images radiomics retrospective studies |
| title | Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images |
| title_full | Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images |
| title_fullStr | Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images |
| title_full_unstemmed | Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images |
| title_short | Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images |
| title_sort | feasibility study on predicting recurrence risk of bladder cancer based on radiomics features of multiphase ct images |
| topic | bladder cancer recurrence multiphase CT images radiomics retrospective studies |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.899897/full |
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