T1 Stage Clear Cell Renal Cell Carcinoma: A CT-Based Radiomics Nomogram to Estimate the Risk of Recurrence and Metastasis

Abstract

Objectives: To develop and validate a radiomics nomogram to improve prediction of recurrence and metastasis risk in T1 stage clear cell renal cell carcinoma (ccRCC).

Methods: This retrospective study recruited 168 consecutive patients (mean age, 53.9 years; range, 28-76 years; 43 women) with T1 ccRCC between January 2012 and June 2019, including 50 aggressive ccRCC based on synchronous metastasis or recurrence after surgery. The patients were divided into two cohorts (training and validation) at a 7:3 ratio. Radiomics features were extracted from contrast enhanced CT images. A radiomics signature was developed based on reproducible features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables (including sex, age, Fuhrman grade, hemoglobin, platelet, neutrophils, albumin, and calcium) and CT findings were combined to develop clinical factors model. Integrating radiomics signature and independent clinical factors, a radiomics nomogram was developed. Nomogram performance was determined by calibration, discrimination, and clinical usefulness.

Results: Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) of 0.86 in the training cohort and 0.85 in the validation cohort. By incorporating the sex, maximum diameter, neutrophil count, albumin count, and radiomics score, a radiomics nomogram was developed. Radiomics nomogram (AUC: training, 0.91; validation, 0.92) had higher performance than clinical factors model (AUC: training, 0.86; validation, 0.90) or radiomics signature as a means of identifying patients at high risk for recurrence and metastasis. The radiomics nomogram had higher sensitivity than clinical factors mode (McNemar's chi-squared = 4.1667, p = 0.04) and a little lower specificity than clinical factors model (McNemar's chi-squared = 3.2, p = 0.07). The nomogram showed good calibration. Decision curve analysis demonstrated the superiority of the nomogram compared with the clinical factors model in terms of clinical usefulness.

Conclusion: The CT-based radiomics nomogram could help in predicting recurrence and metastasis risk in T1 ccRCC, which might provide assistance for clinicians in tailoring precise therapy.

Keywords: clear cell renal cell carcinoma; computed tomography; neoplasm metastasis; prediction model; recurrence.

Figure 1

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

Figure 2

Manual segmentation of the tumor on the center axial slice of the clear cell renal cell carcinoma (ccRCC).

Figure 3

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. An optimal λ value of 0.022 (vertical dash line) with log(λ) = −3.836 was selected. (B) The feature coefficients varied according to log(λ). (C) The selected features with nonzero coefficients and their coefficients.

Figure 4

The distributions of the Rad-score for each patient in the (A) training and (B) validation cohorts. Blue and yellow represent non-aggressive clear cell renal cell carcinoma (ccRCC) and aggressive ccRCC, respectively.

Figure 5

Receiver operating characteristic (ROC) curves of the radiomics signature in the (A) training and (B) validation cohorts, respectively. AUC, area under the receiver operating characteristic curve.

Figure 6

Radiomics nomogram developed with receiver operating characteristic (ROC) curves and calibration curves. (A) The radiomics nomogram, combining sex, tumor maximum diameter, neutrophils, albumin, and Rad-score, developed in the training set. The nomogram calibration curves in the training (B) and validation (C) sets. Calibration curves indicate the goodness-of-fit of the model. The closer the pink line approaches the gray line, the better agreement between the predictive probabilities and the observed probabilities.

Figure 7

Comparison of receiver operating characteristic (ROC) curves between the radiomics nomogram and clinical model for the prediction of aggressive clear cell renal cell carcinoma (ccRCC) in the (A) training and (B) validation cohorts. AUC, area under the receiver operating characteristic curve.

Figure 8

Decision curve analysis for the radiomics nomogram. The y-axis shows the net benefit; x-axis shows the threshold probability. The red line and blue line represent the net benefit of the radiomics nomogram and the clinical factor model, respectively. The green line indicates the hypothesis that all patients had aggressive clear cell renal cell carcinoma (ccRCC). The black line represents the hypothesis that no patients had aggressive ccRCC. The decision curves indicate that the application of radiomics nomogram to predict aggressive ccRCC adds more benefit than treating all or none of the patients, and clinical factor model, across the full range of reasonable threshold probabilities.