Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics

Abstract

Purpose: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.

Methods: A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).

Results: The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set.

Conclusion: Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.

Keywords: Imaging analysis; Medical imaging; Neoplasm progression; Renal cancer.

Fig. 1

An illustration of patient inclusion and exclusion criteria. Abbreviations: ccRCC—clear cell renal cell carcinoma, T1C—T1-contrast-enhanced sequence, T2W—T2-weighted sequence, SSIGN score—tumor stage, size, grade, and necrosis score

Fig. 2

An illustration of radiomics pipeline

Fig. 3

Heatmaps depicting the performance (in area under receiver operating characteristic curve) of feature selection and classification methods in fivefold cross-validation on the training set. a 10 radiomic features were selected, b 20 radiomic features were selected, c 50 radiomic features were selected, and d 100 radiomic features were selected

Fig. 4

a Area under the receiver operating characteristic curve and b area under the Precision-Recall curve of the manually optimized radiomic and Tree-Based Pipeline Optimization Tool (TPOT) models on the test set