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. 2022 Jan 27:11:742547.
doi: 10.3389/fonc.2021.742547. eCollection 2021.

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma

Haijie Zhang  1   2 , Fu Yin  3 , Menglin Chen  1 , Liyang Yang  1 , Anqi Qi  1 , Weiwei Cui  1 , Shanshan Yang  1 , Ge Wen  1
Affiliations

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma

Haijie Zhang et al. Front Oncol. .

Abstract

Background: Many patients experience recurrence of renal cell carcinoma (RCC) after radical and partial nephrectomy. Radiomics nomogram is a newly used noninvasive tool that could predict tumor phenotypes.

Objective: To investigate Radiomics Features (RFs) associated with progression-free survival (PFS) of RCC, assessing its incremental value over clinical factors, and to develop a visual nomogram in order to provide reference for individualized treatment.

Methods: The RFs and clinicopathological data of 175 patients (125 in the training set and 50 in the validation set) with clear cell RCC (ccRCC) were retrospectively analyzed. In the training set, RFs were extracted from multiphase enhanced CT tumor volume and selected using the stability LASSO feature selection algorithm. A radiomics nomogram final model was developed that incorporated the RFs weighted sum and selected clinical predictors based on the multivariate Cox proportional hazard regression. The performances of a clinical variables-only model, RFs-only model, and the final model were compared by receiver operator characteristic (ROC) analysis and DeLong test. Nomogram performance was determined and validated with respect to its discrimination, calibration, reclassification, and clinical usefulness.

Results: The radiomics nomogram included age, clinical stage, KPS score, and RFs weighted sum, which consisted of 6 selected RFs. The final model showed good discrimination, with a C-index of 0.836 and 0.706 in training and validation, and good calibration. In the training set, the C-index of the final model was significantly larger than the clinical-only model (DeLong test, p = 0.008). From the clinical variables-only model to the final model, the reclassification of net reclassification improvement was 18.03%, and the integrated discrimination improvement was 19.08%. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram.

Conclusion: The CT-based RF is an improvement factor for clinical variables-only model. The radiomics nomogram provides individualized risk assessment of postoperative PFS for patients with RCC.

Keywords: CT; Radiomics; artificial intelligence; predict model; progression-free survival (PFS); renal cell carcinoma (RCC).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Image segmentation and feature extraction, selection schematic diagram.
Figure 2
Figure 2
Surv_cutpoint function and survival analysis of PFS in the training set. (A) RFs-weighted sum. (B) Age.
Figure 3
Figure 3
Kaplan–Meier survival analysis of PFS in the training set. (A) Clinical stage. (B) KPS score.
Figure 4
Figure 4
ROC results of the final model of the training set (A) and validation set (B).
Figure 5
Figure 5
A nomogram for PFS was established that included age, clinical stage, KPS score, and RFs-weighted sum.
Figure 6
Figure 6
ROC results of the clinical variables-only model, RFs only model, and final model of the training set (A) and validation set (B).
Figure 7
Figure 7
Decision curve analysis results of the clinical variables-only model, RFs only model, and final model.
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