Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning

TL;DR

This study demonstrates that CT radiomics features combined with machine learning models can effectively predict the Fuhrman grade of clear cell renal cell carcinoma preoperatively, offering a non-invasive diagnostic tool.

Contribution

The paper introduces a novel approach using radiomics and machine learning to accurately classify ccRCC grades from CT images, improving preoperative assessment.

Findings

SVM achieved the highest AUC of 0.83 in classification.

128 discretized texture features significantly correlated with tumor grade.

Machine learning models showed proficient discrimination between high and low-grade ccRCC.

Abstract

Purpose: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade. Methods and material: Seventy one ccRCC patients were included in the study. Three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) were applied on tumor volumes. In total, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association of each feature with the histological condition. In the case of multivariate analysis, the following was implemented: three feature selection including the least absolute shrinkage and selection operator (LASSO), students t-test and minimum Redundancy Maximum Relevance (mRMR) algorithms. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate the high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under receiver operating characteristic curve (AUC). Results: Univariate analysis demonstrated that among different image sets, 128 bin discretized images have statistically significant different (q-value < 0.05) texture parameters with a mean of AUC 0.74 (q-value < 0.05). The three ML-based classifier shows proficient discrimination of the high from low-grade ccRCC. The AUC was 0.78 in logistic regression, 0.62 in random forest, and 0.83 in SVM model, respectively. Conclusion: Radiomics features can be a useful and promising non-invasive method for preoperative evaluation of ccRCC Fuhrman grades. Key words: RCC, Radiomics, Machine Learning, Computed Tomography