Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis

Eur J Cancer. 2016 May:58:17-29. doi: 10.1016/j.ejca.2016.01.007. Epub 2016 Feb 27.


Introduction: Many national guidelines concerning the management of ovarian cancer currently advocate the risk of malignancy index (RMI) to characterise ovarian pathology. However, other methods, such as subjective assessment, International Ovarian Tumour Analysis (IOTA) simple ultrasound-based rules (simple rules) and IOTA logistic regression model 2 (LR2) seem to be superior to the RMI. Our objective was to compare the diagnostic accuracy of subjective assessment, simple rules, LR2 and RMI for differentiating benign from malignant adnexal masses prior to surgery.

Materials and methods: MEDLINE, EMBASE and CENTRAL were searched (January 1990-August 2015). Eligibility criteria were prospective diagnostic studies designed to preoperatively predict ovarian cancer in women with an adnexal mass.

Results: We analysed 47 articles, enrolling 19,674 adnexal tumours; 13,953 (70.9%) benign and 5721 (29.1%) malignant. Subjective assessment by experts performed best with a pooled sensitivity of 0.93 (95% confidence interval [CI] 0.92-0.95) and specificity of 0.89 (95% CI 0.86-0.92). Simple rules (classifying inconclusives as malignant) (sensitivity 0.93 [95% CI 0.91-0.95] and specificity 0.80 [95% CI 0.77-0.82]) and LR2 (sensitivity 0.93 [95% CI 0.89-0.95] and specificity 0.84 [95% CI 0.78-0.89]) outperformed RMI (sensitivity 0.75 [95% CI 0.72-0.79], specificity 0.92 [95% CI 0.88-0.94]). A two-step strategy using simple rules, when inconclusive added by subjective assessment, matched test performance of subjective assessment by expert examiners (sensitivity 0.91 [95% CI 0.89-0.93] and specificity 0.91 [95% CI 0.87-0.94]).

Conclusions: A two-step strategy of simple rules with subjective assessment for inconclusive tumours yielded best results and matched test performance of expert ultrasound examiners. The LR2 model can be used as an alternative if an expert is not available.

Keywords: Meta-analysis; Ovarian cancer; Ovarian neoplasms; Sensitivity and specificity; Systematic review; Ultrasonography.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Review
  • Systematic Review

MeSH terms

  • Decision Support Techniques*
  • Diagnosis, Differential
  • Female
  • Humans
  • Logistic Models
  • Models, Biological*
  • Ovarian Neoplasms / diagnostic imaging*
  • Ovarian Neoplasms / surgery
  • Predictive Value of Tests
  • Prognosis
  • Reproducibility of Results
  • Ultrasonography, Doppler, Color*