Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-10

AUTHORS

Zine‐Eddine Khene, Karim Bensalah, Axel Largent, Shahrokh Shariat, Gregory Verhoest, Benoit Peyronnet, Oscar Acosta, Renaud DeCrevoisier, Romain Mathieu

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. More... »

PAGES

1635-1642

References to SciGraph publications

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URI

http://scigraph.springernature.com/pub.10.1007/s00345-018-2292-9

DOI

http://dx.doi.org/10.1007/s00345-018-2292-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103475532

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29675631


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47 schema:description 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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