Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Abstract

Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CTenhanced quantitative features and machine learning.

A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma

“…This hypothesis was also proven by the results of this study, in which the radiomics signature outperformed the image features model in predicting tumor necrosis in ccRCC. Consistent with previous studies, the radiomics signature consists mainly of three-dimensional texture features, and its prediction performance was significantly better than that of morphological features and first-order features (26,27). The reason is that the three-dimensional texture features can provide gross characterization of tumor heterogeneity through analysis of the distribution and relationship with gray levels of pixels or voxels in CT images (28).…”

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

“…Li et al explored the clinical value of radiomics-based approaches, by combining TA with five ML-based models, to differentiate oncocytoma from chRCC. They found that all five classifiers performed well at differentiating between the two with AUC values over 0.85 and concluded that their approach provides valuable preoperative diagnostic accuracy [29]. Other studies used DL approaches such as convolutional neural networks (CNN) to accurately classify chRCC and oncocytoma while also achieving 100% sensitivity in comparison with final pathology results [30].…”

RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management

“…In a retrospective study of 165 consecutive patients with locally advanced rectal cancer (LARC), an LR-based MR radiomics model was developed to predict the pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in order to identify candidates for optimal treatments [25]. In a study of 61 patients with renal tumors, CT radiomics models with five ML classifiers were implemented for differential diagnosis of malignant renal chromophobe cell carcinoma (chRCC) and benign renal oncocytoma (RO) from CT images [26]. MR radiomics models with SVM and LRT were developed to differentiate benign rectal adenoma and adenoma with canceration [27].…”