Abstract
BackgroundMany 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.ObjectiveTo 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.MethodsThe 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.ResultsThe 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.ConclusionThe 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.
Highlights
Renal cell carcinoma (RCC) is a malignant tumor originating from the proximal tubular epithelial system of renal parenchyma, and accounts for about 85% of all adult renal malignant tumors
The results showed that inter-group correlation coefficients (ICCs) > 0.80 between groups and within groups; it means that the image segmentation was consistent, and the remaining image segmentation was performed by Dr A
We developed a radiomics nomogram that incorporates three clinical factors and RFsweighted sum for noninvasive, individualized prediction of progression-free survival (PFS) in patients with clinical stage I–III clear cell renal cell carcinoma (ccRCC), which can enable physicians to select reasonable treatment tactics and individualized monitoring protocols to improve clinical outcomes
Summary
Renal cell carcinoma (RCC) is a malignant tumor originating from the proximal tubular epithelial system of renal parenchyma, and accounts for about 85% of all adult renal malignant tumors. The clear cell renal cell carcinoma (ccRCC) is the most common subtype accounting for about 75% of all RCC [2], and is associated with high invasion and poor prognosis [3, 4]. According to the AJCC Tumor Classification Criteria eighth Edition (2017) [5], surgery is the preferred treatment for patients with stage I–III RCC, and is associated with a 5-year survival rate of 71% to 91% [6]. If we can predict these patients with high risk of recurrence before surgery, and give them targeted treatment and close follow-up, it will be very helpful to improve the prognosis of these patients. 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
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