RESUMO
OBJECTIVES: Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS: We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS: In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION: Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.
RESUMO
The Risk of Malignancy Index (RMI) is commonly used to diagnose adnexal masses. The aim of the present study was to determine the cost-effectiveness of the RMI compared with subjective assessment (SA) by an expert and the following novel ultrasound models: Cost-effectiveness and budget impact analyses were performed from a societal perspective. A decision tree was constructed, and short-term costs and effects were examined in women with adnexal masses. Sensitivity, specificity and the costs of diagnostic strategies were incorporated. Incremental cost-effectiveness ratios were expressed as costs/additional percentage of correctly diagnosed patients. Probabilistic and deterministic sensitivity analyses were performed. Effectiveness was highest for SA (90.7% [95% confidence interval = 77.3-100]), with a cost saving of 5.0% (-398 per patient [-1403 to 549]) compared with the RMI. The costs of SR + SA were the lowest (7180 [6072-8436]), resulting in a cost saving of 9.0% (-709 per patient [-1628 to 236]) compared with the RMI, with an effectiveness of 89.6% (75.8-100). SR + SA showed the highest probability of being the most cost-effective when willingness-to-pay was <350 per additional percentage of correctly diagnosed patients. The RMI had low cost-effectiveness probabilities (<3%) and was inferior to SA, SR + SA and LR2. Budget impact in the Netherlands compared with that of the RMI varied between a cost saving of 4.67 million for SR + SA and additional costs of 3.83 million when implementing ADNEX (cut-off: 10%). The results were robust when tested in sensitivity analyses. Although SA is the best strategy in terms of diagnostic accuracy, SR + SA might be preferred from a cost-effectiveness perspective.