Distinguishing malignant and benign renal masses with composite models and nomograms: A systematic review and meta-analysis of clinically localized renal masses suspicious for malignancy

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Abstract

Solid renal masses and cystic lesions with solid components are suspicious for renal cell carcinoma. Without an effective screening test, composite models and nomograms rely on patient and tumor characteristics to stratify the risk of benign disease versus malignant disease. To guide decisions about the use of renal mass sampling or excision, a systematic review and meta-analysis of the ability of composite models to predict the likelihood of malignancy on the basis of preoperative clinical variables was performed. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched from January 1, 1997, through May 1, 2015, according to the Preferred Reporting Items for Systematic Review and Meta-Analyses statement. Composite models necessarily included imaging results and at least 1 element from the following to be compared with surgical pathology: demographic characteristics, clinical characteristics, and blood or urine tests. Two independent reviewers screened citations and extracted data. Quality Assessment Tool for Diagnostic Accuracy Studies 2 was used to assess the risk of bias. The strength of evidence was graded with the scheme recommended by Methods Guide for Effectiveness and Comparative Effectiveness Reviews. Twenty studies (12,149 patients) were included in this review. The only significant predictors of malignancy in the composite models were tumor size (effect size, 1.33-fold increased risk per centimeter; 95% confidence interval [CI], 1.22-1.43) and male sex (effect size, 2.71; 95% CI, 2.39-3.02). The results were inconclusive or not significant for tumor characteristics, age, body mass index, and incidental presentation. In conclusion, composite models currently have a limited ability to distinguish malignant renal masses from benign renal masses, with increased tumor size and male sex associated with malignancy. Cancer 2016;122:3267–3276.

Original languageEnglish (US)
Pages (from-to)3267-3276
Number of pages10
JournalCancer
Volume122
Issue number21
DOIs
Publication statusPublished - Nov 15 2016

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Keywords

  • composite models
  • malignancy
  • nomogram
  • predictors
  • renal cell carcinoma
  • renal mass

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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