Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Ruizhi Gao; Hui Qin; Peng Lin; Chenjun Ma; Chengyang Li; Rong Wen; Jing Huang; Da Wan; Dongyue Wen; Yiqiong Liang; Jiang Huang; Xin Li; Xinrong Wang; Gang Chen; Yun He; Hong Yang

doi:10.3389/fonc.2021.613668

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Front Oncol. 2021 Jul 6:11:613668. doi: 10.3389/fonc.2021.613668. eCollection 2021.

Authors

Ruizhi Gao¹, Hui Qin¹, Peng Lin¹, Chenjun Ma², Chengyang Li², Rong Wen¹, Jing Huang¹, Da Wan¹, Dongyue Wen¹, Yiqiong Liang³, Jiang Huang³, Xin Li⁴, Xinrong Wang⁴, Gang Chen⁵, Yun He¹, Hong Yang¹

Affiliations

¹ Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
² Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
³ Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁴ GE Healthcare Global Research, GE, Shanghai, China.
⁵ Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Abstract

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.

Keywords: artificial intelligence; contrast-enhanced computed tomography; kidney renal clear cell carcinoma; prognosis; weighted correlation network analysis.