Dataset Information

Patient and nonradiographic tumor characteristics predicting lipid-poor angiomyolipoma in small renal masses: Introducing the BEARS index.

ABSTRACT:

Purpose

To create a simple model using clinical variables for predicting lipid-poor angiomyolipoma (AML) in patients with small renal masses presumed to be renal cell carcinoma (RCC) from preoperative imaging.

Materials and methods

A series of patients undergoing partial nephrectomy (PN) for renal masses ?4 cm was identified using a prospectively maintained database. Patients were excluded if standard preoperative imaging was not consistent with RCC. Chi square and Mann-Whitney U analyses were used to evaluate differences in characteristics between patients with AML and other types of pathology. A logistic regression model was constructed for multivariable analysis of predictors of lipid-poor AML.

Results

A total of 730 patients were identified that underwent PN for renal masses ?4 cm between 2007-2015, including 35 with lipid-poor AML and 620 with RCC. In multivariable analysis, the following features predicted AML: female sex (odds ratio, 6.89; 95% confidence interval, 2.35-20.92; p<0.001), age <56 years (2.84; 1.21-6.66; p=0.02), and tumor size <2 cm (5.87; 2.70-12.77; p<0.001). Sex, age, and tumor size were used to construct the BEnign Angiomyolipoma Renal Susceptibility (BEARS) index with the following point values for each particular risk factor: female sex (2 points), age <56 years (1 point), and tumor size <2 cm (2 points). Within the study population, the BEARS index distinguished AML from malignant lesions with an area under the curve of 0.84.

Conclusions

Young female patients with small tumors are at risk for having lipid-poor AML despite preoperative imaging consistent with RCC. Identification of these patients may reduce the incidence of unnecessary PN for benign renal lesions.

SUBMITTER: Bauman TM

PROVIDER: S-EPMC5494346 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Patient and nonradiographic tumor characteristics predicting lipid-poor angiomyolipoma in small renal masses: Introducing the BEARS index.

Bauman Tyler M TM Potretzke Aaron M AM Wright Alec J AJ Vetter Joel M JM Potretzke Theodora A TA Figenshau R Sherburne RS

Investigative and clinical urology 20170627 4

<h4>Purpose</h4>To create a simple model using clinical variables for predicting lipid-poor angiomyolipoma (AML) in patients with small renal masses presumed to be renal cell carcinoma (RCC) from preoperative imaging.<h4>Materials and methods</h4>A series of patients undergoing partial nephrectomy (PN) for renal masses ≤4 cm was identified using a prospectively maintained database. Patients were excluded if standard preoperative imaging was not consistent with RCC. Chi square and Mann-Whitney U ...[more]

PMID: 28681032

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Project description:BackgroundThe presence of peripheral inflammatory cells has been linked to the prognosis of cancer. This study aims to investigate the distinct roles of absolute neutrophil count (ANC) and absolute monocyte count (AMC) in differentiating renal cell carcinoma (RCC) from renal angiomyolipoma (RAML), as well as their prognostic significance in RCC.MethodsWe conducted a comprehensive analysis of peripheral immune cell data, clinicopathological data, and tumor characteristics in patients diagnosed with RCC or RAML from January 2015 to December 2021. Receiver operating characteristic (ROC) curves, as well as univariate and multivariate analyses, were employed to assess the diagnostic utility of AMC and ANC in differentiating between RCC and RAML. Kaplan-Meier curve analysis was used to study the survival of RCC patients with different AMC and ANC. The prognostic value of AMC and ANC in RCC was investigated using COX univariate and multivariate analysis. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for bioinformatic correlation analysis.ResultsA total of 1120 eligible patients were included in the study. The mean preoperative AMC and ANC in patients with RCC were found to be significantly higher compared to those in patients with RAML (P = 0.001 and P < 0.001, respectively). High preoperative AMC and ANC significantly correlated with smoking history, tumor length, gross hematuria, and high T Stage, N stage, and pathological grade. In multivariate analyses, an ANC> 3.205 *10^9/L was identified to be independently associated with the presence of RCC (HR = 1.618, P = 0.008). High AMC and ANC were significantly associated with reduced OS and PFS (P < 0.05), and ANC may be an independent prognostic factor. Public database analysis showed that signature genes of tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) were highly expressed in ccRCC.ConclusionsElevated preoperative ANC and AMC can distinguish RCC from RAML and predict poor prognosis in patients with RCC. Furthermore, the signature genes of TAMs and TANs exhibit high expression levels in clear cell RCC.

Project description:PurposeDifferentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs.MethodsWe conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034).ResultsOur literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan.ConclusionThis study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.

Dataset Information

Patient and nonradiographic tumor characteristics predicting lipid-poor angiomyolipoma in small renal masses: Introducing the BEARS index.

Purpose

Materials and methods

Results

Conclusions

Publications

Patient and nonradiographic tumor characteristics predicting lipid-poor angiomyolipoma in small renal masses: Introducing the BEARS index.

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