Cargando…
Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma
PURPOSE: The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). METHODS: A total of 136 and 78 KI...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290524/ https://www.ncbi.nlm.nih.gov/pubmed/34295804 http://dx.doi.org/10.3389/fonc.2021.613668 |
_version_ | 1783724510337302528 |
---|---|
author | Gao, Ruizhi Qin, Hui Lin, Peng Ma, Chenjun Li, Chengyang Wen, Rong Huang, Jing Wan, Da Wen, Dongyue Liang, Yiqiong Huang, Jiang Li, Xin Wang, Xinrong Chen, Gang He, Yun Yang, Hong |
author_facet | Gao, Ruizhi Qin, Hui Lin, Peng Ma, Chenjun Li, Chengyang Wen, Rong Huang, Jing Wan, Da Wen, Dongyue Liang, Yiqiong Huang, Jiang Li, Xin Wang, Xinrong Chen, Gang He, Yun Yang, Hong |
author_sort | Gao, Ruizhi |
collection | PubMed |
description | PURPOSE: The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). METHODS: A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis. RESULTS: A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation. CONCLUSIONS: The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients. |
format | Online Article Text |
id | pubmed-8290524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82905242021-07-21 Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma Gao, Ruizhi Qin, Hui Lin, Peng Ma, Chenjun Li, Chengyang Wen, Rong Huang, Jing Wan, Da Wen, Dongyue Liang, Yiqiong Huang, Jiang Li, Xin Wang, Xinrong Chen, Gang He, Yun Yang, Hong Front Oncol Oncology PURPOSE: The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). METHODS: A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis. RESULTS: A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation. CONCLUSIONS: The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients. Frontiers Media S.A. 2021-07-06 /pmc/articles/PMC8290524/ /pubmed/34295804 http://dx.doi.org/10.3389/fonc.2021.613668 Text en Copyright © 2021 Gao, Qin, Lin, Ma, Li, Wen, Huang, Wan, Wen, Liang, Huang, Li, Wang, Chen, He and Yang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Gao, Ruizhi Qin, Hui Lin, Peng Ma, Chenjun Li, Chengyang Wen, Rong Huang, Jing Wan, Da Wen, Dongyue Liang, Yiqiong Huang, Jiang Li, Xin Wang, Xinrong Chen, Gang He, Yun Yang, Hong Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title | Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title_full | Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title_fullStr | Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title_full_unstemmed | Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title_short | Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma |
title_sort | development and validation of a radiomic nomogram for predicting the prognosis of kidney renal clear cell carcinoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290524/ https://www.ncbi.nlm.nih.gov/pubmed/34295804 http://dx.doi.org/10.3389/fonc.2021.613668 |
work_keys_str_mv | AT gaoruizhi developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT qinhui developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT linpeng developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT machenjun developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT lichengyang developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT wenrong developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT huangjing developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT wanda developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT wendongyue developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT liangyiqiong developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT huangjiang developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT lixin developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT wangxinrong developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT chengang developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT heyun developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma AT yanghong developmentandvalidationofaradiomicnomogramforpredictingtheprognosisofkidneyrenalclearcellcarcinoma |