Development of a clinical prediction model for assessment of malignancy risk in Bosniak III renal lesions

Urology. 2013 Sep;82(3):630-5. doi: 10.1016/j.urology.2013.05.016. Epub 2013 Jul 19.

Abstract

Objective: To identify independent predictors of malignancy in Bosniak III (BIII) renal lesions and to build a prediction model based on readily identifiable clinical variables.

Methods: In this institutional review board-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study, radiology, and hospital information systems containing data from January 1, 1994, to August 31, 2009, were queried for adult patients (age >18 years) with surgically excised BIII lesions. Clinical variables and results of histopathology were noted. Univariate and multiple-variable logistic regression analyses were performed to identify potential predictors and to build a prediction model. Cross-validation was used to assess generalizability of the model's performance, as characterized by concordance (c) index.

Results: Of the 107 lesions in 101 patients, 59 were malignant and 48 benign. On univariate analyses, the strongest potential predictors of malignancy were African American race (P = .043), history of renal cell carcinoma (RCC; P = .026), coexisting BIII lesions (P = .032), coexisting Bosniak IV (BIV) lesions (P = .104), body mass index (BMI; P = .078), and lesion size (P <.001). A model with lesion size (odds ratio [OR] = 0.69; 95% confidence interval [CI] 0.58-0.82), history of RCC (9.02; CI 0.99-82.15), and BMI (OR 1.1; 95% CI 0.99-1.19) offered the best performance with a c-index after cross-validation of 0.719. Using an estimated probability of malignancy of >80%, the positive predictive value of the model is 92% (CI 78%-100%).

Conclusion: Clinical risk factors offer modest but definite predictive ability for malignancy in BIII lesions. In particular, a prediction model encompassing lesion size, BMI, and history of RCC seems promising. Further refinements with possible inclusion of imaging biomarkers and validation on an independent dataset are desirable.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Carcinoma, Renal Cell / etiology
  • Carcinoma, Renal Cell / pathology*
  • Carcinoma, Renal Cell / surgery
  • Decision Support Techniques*
  • Female
  • Humans
  • Kidney Diseases, Cystic / classification*
  • Kidney Diseases, Cystic / complications
  • Kidney Diseases, Cystic / pathology*
  • Kidney Neoplasms / etiology
  • Kidney Neoplasms / pathology*
  • Kidney Neoplasms / surgery
  • Logistic Models
  • Male
  • Medical History Taking
  • Middle Aged
  • Multivariate Analysis
  • Predictive Value of Tests
  • Preoperative Period
  • Retrospective Studies
  • Risk Assessment / methods