Zine-Eddine Khene1,2, Karim Bensalah3, Axel Largent4, Shahrokh Shariat5,6,7,8, Gregory Verhoest3, Benoit Peyronnet3, Oscar Acosta4, Renaud DeCrevoisier4,9, Romain Mathieu3,4. 1. Department of Urology, Rennes University Hospital, Rennes, France. zineddine.khene@gmail.com. 2. LTSI, Inserm U1099, Université de Rennes 1, Rennes, France. zineddine.khene@gmail.com. 3. Department of Urology, Rennes University Hospital, Rennes, France. 4. LTSI, Inserm U1099, Université de Rennes 1, Rennes, France. 5. Department of Urology, Medical University Vienna, General Hospital, Vienna, Austria. 6. Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA. 7. Department of Urology, Weill Cornell Medical College, New York, NY, USA. 8. Karl Landsteiner Institute, Vienna, Austria. 9. Centre Eugene Marquis, Rennes, France.
Abstract
OBJECTIVE: To assess the performance of computed tomography (CT) texture analysis to predict the presence of adherent perinephric fat (APF). MATERIALS AND METHODS: Seventy patients with small renal tumors treated with robot-assisted partial nephrectomy were included. Patients were divided into two groups according to the presence of APF. We extracted 15 image features from unenhanced CT and contrast-enhanced CT corresponding to first-order and second-order Haralick textural features. Predictors of APF were evaluated by univariable and multivariable analysis. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) to predict APF was calculated for the independent predictors. RESULTS: APF was observed in 26 patients (37%). We identified entropy (p = 0.01), sum entropy (p = 0.02) and difference entropy (p = 0.05) as significant independent predictors of APF. In the portal phase, we identified correlation (p = 0.03), inverse difference moment (p = 0.01), sum entropy (p = 0.02), entropy (p = 0.01), difference variance (p = 0.04) and difference entropy (p = 0.02) as significant independent predictors of APF. Combining these parameters yielded to an ROC-AUC of 0.82 (95% CI 0.65-0.86). CONCLUSION: Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that helps urologist to identify APF.
OBJECTIVE: To assess the performance of computed tomography (CT) texture analysis to predict the presence of adherent perinephric fat (APF). MATERIALS AND METHODS: Seventy patients with small renal tumors treated with robot-assisted partial nephrectomy were included. Patients were divided into two groups according to the presence of APF. We extracted 15 image features from unenhanced CT and contrast-enhanced CT corresponding to first-order and second-order Haralick textural features. Predictors of APF were evaluated by univariable and multivariable analysis. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) to predict APF was calculated for the independent predictors. RESULTS: APF was observed in 26 patients (37%). We identified entropy (p = 0.01), sum entropy (p = 0.02) and difference entropy (p = 0.05) as significant independent predictors of APF. In the portal phase, we identified correlation (p = 0.03), inverse difference moment (p = 0.01), sum entropy (p = 0.02), entropy (p = 0.01), difference variance (p = 0.04) and difference entropy (p = 0.02) as significant independent predictors of APF. Combining these parameters yielded to an ROC-AUC of 0.82 (95% CI 0.65-0.86). CONCLUSION: Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that helps urologist to identify APF.
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