ArticleWorld journal of urology2018
Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat.
Article in World journal of urology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Adherent perinephric fat affects perioperative outcomes after partial nephrectomy: a systematic review and meta-analysis.International journal of clinical oncology · 2021Pooled it
- CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- Male Gender Is a Contributing Risk Factor to Predict Sticky Fat in Laparoscopic Partial Nephrectomy.Life (Basel, Switzerland) · 2025Article
- Development and validation of a predictive model for adherent perirenal fat based on CT radiomics and deep learning.World journal of urology · 2025Article
- Evaluation of factors predicting adherent perinephric fat in laparoscopic retroperitoenal partial nephrectomy.BMC urology · 2025Article
- Article
- Comparison of the results of laparoscopic retroperitoneal and transperitoneal partial nephrectomy in anterior kidney tumors: a two-center matched-pair analysis.World journal of urology · 2025Article
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
- A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis.BMC urology · 2022Article
- Automatic brain segmentation in preterm infants with post-hemorrhagic hydrocephalus using 3D Bayesian U-Net.Human brain mapping · 2022Article
- Clinical Implication of Adherent Perinephric Fat in Robot-Assisted Partial Nephrectomy: Validation With Video Review.Frontiers in surgery · 2022Article
- Susceptibility weighted imaging (SWI) for evaluating renal dysfunction in type 2 diabetes mellitus: a preliminary study using SWI parameters and SWI-based texture features.Annals of translational medicine · 2020Article
- Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT.Translational andrology and urology · 2019Article
- Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes.Advances in urology · 2019Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the performance of computed tomography (CT) texture analysis to predict the presence of adherent perinephric fat (APF). MATERIALS AND
methodsSeventy 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.
resultsAPF 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).
conclusionResults 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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