A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC).Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patie...
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Frontiers Media S.A.
2020-05-01
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Series: | Frontiers in Oncology |
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Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.00592/full |
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doaj-9f508bf2eb534082b93ccf4860692a0d |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Jiang Yi Jiang Wuchao Li Wuchao Li Chencui Huang Chong Tian Chong Tian Qi Chen Xianchun Zeng Xianchun Zeng Yin Cao Yi Chen Yintong Yang Heng Liu Yonghua Bo Chenggong Luo Yiming Li Tijiang Zhang Rongping Wang Rongping Wang Rongping Wang |
spellingShingle |
Yi Jiang Yi Jiang Wuchao Li Wuchao Li Chencui Huang Chong Tian Chong Tian Qi Chen Xianchun Zeng Xianchun Zeng Yin Cao Yi Chen Yintong Yang Heng Liu Yonghua Bo Chenggong Luo Yiming Li Tijiang Zhang Rongping Wang Rongping Wang Rongping Wang A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma Frontiers in Oncology clear cell renal cell carcinoma tumor necrosis computed tomography radiomics prediction model |
author_facet |
Yi Jiang Yi Jiang Wuchao Li Wuchao Li Chencui Huang Chong Tian Chong Tian Qi Chen Xianchun Zeng Xianchun Zeng Yin Cao Yi Chen Yintong Yang Heng Liu Yonghua Bo Chenggong Luo Yiming Li Tijiang Zhang Rongping Wang Rongping Wang Rongping Wang |
author_sort |
Yi Jiang |
title |
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma |
title_short |
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma |
title_full |
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma |
title_fullStr |
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma |
title_full_unstemmed |
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma |
title_sort |
computed tomography-based radiomics nomogram to preoperatively predict tumor necrosis in patients with clear cell renal cell carcinoma |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2020-05-01 |
description |
Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC).Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis.Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87–0.96]) and validation (AUC 0.87 [95% CI 0.79–0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models.Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC. |
topic |
clear cell renal cell carcinoma tumor necrosis computed tomography radiomics prediction model |
url |
https://www.frontiersin.org/article/10.3389/fonc.2020.00592/full |
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doaj-9f508bf2eb534082b93ccf4860692a0d2020-11-25T03:26:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-05-011010.3389/fonc.2020.00592533222A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell CarcinomaYi Jiang0Yi Jiang1Wuchao Li2Wuchao Li3Chencui Huang4Chong Tian5Chong Tian6Qi Chen7Xianchun Zeng8Xianchun Zeng9Yin Cao10Yi Chen11Yintong Yang12Heng Liu13Yonghua Bo14Chenggong Luo15Yiming Li16Tijiang Zhang17Rongping Wang18Rongping Wang19Rongping Wang20Medical College, Guizhou University, Guiyang, ChinaDepartment of Medical Records and Statistics, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Radiology, Guizhou Provincial People's Hospital, Guiyang, ChinaGuizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, ChinaResearch Collaboration Department, R&D Center, Beijing Deepwise & League of PHD Technology Co.LTD, Beijing, ChinaDepartment of Radiology, Guizhou Provincial People's Hospital, Guiyang, ChinaGuizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Medical Records and Statistics, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Radiology, Guizhou Provincial People's Hospital, Guiyang, ChinaGuizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Pathology, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Pathology, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Pathology, Guizhou Provincial People's Hospital, Guiyang, ChinaDepartment of Radiology, Affiliated hospital of Zunyi Medical University, Zunyi, ChinaDepartment of Pathology, Affiliated hospital of Zunyi Medical University, Zunyi, ChinaDepartment of Urinary Surgery, Guizhou Provincial People's Hospital, Guiyang, ChinaResearch Collaboration Department, R&D Center, Beijing Deepwise & League of PHD Technology Co.LTD, Beijing, ChinaDepartment of Radiology, Affiliated hospital of Zunyi Medical University, Zunyi, ChinaMedical College, Guizhou University, Guiyang, ChinaDepartment of Radiology, Guizhou Provincial People's Hospital, Guiyang, ChinaGuizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, ChinaObjective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC).Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis.Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87–0.96]) and validation (AUC 0.87 [95% CI 0.79–0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models.Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.https://www.frontiersin.org/article/10.3389/fonc.2020.00592/fullclear cell renal cell carcinomatumor necrosiscomputed tomographyradiomicsprediction model |