Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

Abstract

Background: Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.

Methods: This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.

Results: Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusions: The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

Keywords: clear cell renal cell carcinoma; computed tomography; metastasis; nomogram; radiomics.

Figure 1

Recruitment pathway for patients in this study. ccRCC, clear cell renal cell carcinoma.

Figure 2

Radiomics feature selection by using the least absolute shrinkage and selection operator (LASSO) logistic regression. (A) Selection of the tuning parameter (λ) in the LASSO model via 10-fold cross-validation based on minimum criteria. Binomial deviances from the LASSO regression cross-validation model are plotted as a function of log(λ). The y-axis shows binomial deviances and the lower x-axis the log(λ). Numbers along the upper x-axis indicate the average number of predictors. Red dots indicate average deviance values for each model with a given λ, and vertical bars through the red dots indicate the upper and lower values of the deviances. The vertical black lines define the optimal values of λ, where the model provides its best fit to the data. An optimal λ value of 0.007 with log(λ) =-4.962 was selected. (B) The coefficients have been plotted vs. log(λ). (C) The 10 features with nonzero coefficients are shown in the plot.

Figure 3

Radiomics nomogram and calibration curves. (A) The radiomics nomogram, combining Cli-score and Rad-score, developed in the training set. Calibration curves for the radiomics nomogram in the training (B), internal validation (C), and external validation (D) cohorts. Calibration curves indicate the goodness-of-fit of the nomogram. The 45° gray line represents the ideal prediction, and the pink line represents the predictive performance of the nomogram. The closer the pink line approaches the ideal prediction line, the better the predictive efficacy of the nomogram is.

Figure 4

The distribution of Nomo-score with regard to SDM status in the training (A), internal validation (B) and external validation (C) cohorts.

Figure 5

Diagnostic performance of the clinical factors model, radiomics signature, and radiomics nomogram was assessed and compared through ROC curves in the training (A), internal validation (B) and external validation (C) cohorts. ROC = receiver operating characteristics; AUC = area under the receiver operating characteristic curve.

Figure 6

Decision curve analysis for the three models in the training (A), internal validation (B) and external validation (C) cohorts. The y-axis shows the net benefit; x-axis shows the threshold probability. The red, orange, and green line represent net benefit of the radiomics nomogram, clinical factors model, and radiomics signature, respectively.