A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions

Abstract

Background: There is lack of discrimination as to traditional imaging diagnostic methods of cystic renal lesions (CRLs). This study aimed to evaluate the value of machine learning models based on clinical data and contrast-enhanced computed tomography (CECT) radiomics features in the differential diagnosis of benign and malignant CRL.

Methods: There were 192 patients with CRL (Bosniak class ≥ II) enrolled through histopathological examination, including 144 benign cystic renal lesions (BCRLs) and 48 malignant cystic renal lesions (MCRLs). Radiomics features were extracted from CECT images taken during the medullary phase. Using the light gradient boosting machine (LightGBM) algorithm, the clinical, radiomics and combined models were constructed. A comprehensive nomogram was developed by integrating the radiomics score (Rad-score) with independent clinical factors. Receiver operating characteristic (ROC) curves were plotted. The corresponding area under the curve (AUC) value was worked out to quantify the discrimination performance of the three models in training and validation cohorts. Calibration curves were worked out to assess the accuracy of the probability values predicted by the models. Decision curve analysis (DCA) was worked out to assess the performance of models at different thresholds.

Results: Maximum diameter and Bosniak class were independent risk factors of patients with MCRL in the clinical model. Twenty-one radiomics features were extracted to work out a Rad-score. The performance of the clinical model in the training cohort was AUC =0.948, 95% confidence interval (CI): 0.917-0.980, and the performance in the validation cohort was AUC =0.936, 95% CI: 0.859-1.000 (P<0.05). The performance of the radiomics model in the training cohort was AUC =0.990, 95% CI: 0.979-1.000, and the performance in the validation cohort was AUC =0.959, 95% CI: 0.903-1.000 (P<0.05). Compared with the above models, the combined radiomics nomogram had an AUC of 0.989 (95% CI: 0.977-1.000) in the training cohort and an AUC of 0.962 (95% CI: 0.905-1.000) in the validation cohort (P<0.05), showing the best diagnostic efficacy.

Conclusions: The radiomics nomogram integrating clinical independent risk factors and radiomics signature improved the diagnostic accuracy in differentiating between BCRL and MCRL, which can provide a reference for clinical decision-making and help clinicians develop individualized treatment strategies for patients.

Keywords: Bosniak classification; computed tomography (CT); cystic tumor; radiomics; renal cyst.

Figure 1

Flowchart demonstrating how the study cohort of 192 cystic renal lesions was derived and assigned into two groups. CECT, contrast-enhanced computed tomography; BCRLs, benign cystic renal lesions; MCRLs, malignant cystic renal lesions.

Figure 2

Analysis workflow of radiomics. (A) Specific ROIs are identified and segmented from the images. (B) Quantitative information is extracted from the ROIs, such as geometry, intensity and texture. (C) The most relevant and informative features are selected to reduce dimensionality and improve the model’s performance. (D) A model is constructed using the selected features and machine learning methods for tasks such as classification, prediction or other analytical objectives. ROI, region of interest; glcm, gray-level co-occurrence matrix; gldm, gray-level dependence matrix; glrlm, gray-level run length matrix; glszm, gray-level size zone matrix; ngtdm, normalized gray-level transition matrix; MSE, mean standard error; AUC, area under the curve; CI, confidence interval; DCA, decision curve analysis.

Figure 3

Utilizing the LASSO regression model for the selection of pertinent radiomics features. (A) The minimum criterion was employed to determine the optimal tuning parameter “λ”. (B) The coefficient profile plot was generated based on the chosen log “λ” value. (C) A total of 21 selected features are presented alongside their respective non-zero coefficients. MSE, mean standard error; gldm, gray-level dependence matrix; glrlm, gray-level run length matrix; glszm, gray-level size zone matrix; glcm, gray-level co-occurrence matrix; MCC, Matthews correlation coefficient; ngtdm, normalized gray-level transition matrix; 3D, three-dimensional; Idn, inverse difference normalized; LASSO, least absolute shrinkage and selection operator.

Figure 4

Results of the LightGBM models. (A,B) ROC curves, (C,D) calibration curves and (E,F) DCA of the three models in both the training and validation cohorts. AUC, area under the curve; CI, confidence interval; LightGBM, light gradient boosting machine; ROC, receiver operating characteristic; DCA, decision curve analysis.

Figure 5

The radiomics nomogram based on the combined model.