Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors

Abstract

Objectives. This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods. This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k -nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results. Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions. Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

“…The corticomedullary phase and nephrographic phase achieved an adequate performance (AUC = 0.767 and 0.783, respectively). 55 Similarly, Kim et al 56 assessed the predictive role of CT radiomics in 28 AML and 56 RCC, reporting an AUC of 0.89, close to those of experienced radiologists (AUC = 0.78; Table 2 ).…”

“… 64 , 71 , 77 – 79 , 105 – 108 SVMs are identified as the most used algorithms used for classification in the analyzed studies. 34 , 36 , 42 , 46 , 47 , 51 , 55 , 66 , 74 When trained, the learning process searches to differentiate between two data sets (for example, benign from malignant kidney tumors or to differentiate between certain tumor types and some of them to predict gene mutation or response to therapy). The data used to train and learn SVMs are not entirely used for this purpose.…”

“… 128 , 129 In the renal lesions, radiomics studies were used to reduce overfitting with good results. 49 – 53 , 55 , 70 , 111 …”