OBJECTIVE: To perform a systematic review of the literature on the accuracy of prediction models in the preoperative assessment of adnexal masses. DATA SOURCES: Studies were identified through the MEDLINE and EMBASE databases from inception to March 2008. The MEDLINE search was performed using the keywords ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "model"] and ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "prediction"]. The Embase search was performed using the keywords [ovary tumor AND prediction], [ovary tumor AND Mathematical model], and [ovary tumor AND statistical model]. METHODS OF STUDY SELECTION: The search detected 1,161 publications; from the cross-references, another 116 studies were identified. Language restrictions were not applied. Eligible studies contained data on the accuracy of models predicting the risk of malignancy in ovarian masses. Models were required to combine at least two parameters. TABULATION, INTEGRATION, AND RESULTS: Two independent reviewers selected studies and extracted study characteristics, study quality, and test accuracy. There were 109 accuracy studies that met the selection criteria. Accuracy data were used to form two-by-two contingency tables of the results of the risk score compared with definitive histology. We used bivariate meta-analysis to estimate pooled sensitivities and specificities and to fit summary receiver operating characteristic curves.Studies included in our analysis reported on 83 different prediction models. The model developed by Sassone was the most evaluated prediction model. All models has acceptable sensitivity and specificity. However, the Risk of Malignancy Index I and the Risk of Malignancy Index II, which use the product of the serum CA 125 level, an ultrasound scan result, and the menopausal state, were the best predictors. When 200 was used as the cutoff level, the pooled estimate for sensitivity was 78% for a specificity of 87%. CONCLUSION: Based on our review, the Risk of Malignancy Index should be the prediction model of choice in the preoperative assessment of the adnexal mass.
OBJECTIVE: To perform a systematic review of the literature on the accuracy of prediction models in the preoperative assessment of adnexal masses. DATA SOURCES: Studies were identified through the MEDLINE and EMBASE databases from inception to March 2008. The MEDLINE search was performed using the keywords ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "model"] and ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "prediction"]. The Embase search was performed using the keywords [ovary tumor AND prediction], [ovary tumor AND Mathematical model], and [ovary tumor AND statistical model]. METHODS OF STUDY SELECTION: The search detected 1,161 publications; from the cross-references, another 116 studies were identified. Language restrictions were not applied. Eligible studies contained data on the accuracy of models predicting the risk of malignancy in ovarian masses. Models were required to combine at least two parameters. TABULATION, INTEGRATION, AND RESULTS: Two independent reviewers selected studies and extracted study characteristics, study quality, and test accuracy. There were 109 accuracy studies that met the selection criteria. Accuracy data were used to form two-by-two contingency tables of the results of the risk score compared with definitive histology. We used bivariate meta-analysis to estimate pooled sensitivities and specificities and to fit summary receiver operating characteristic curves.Studies included in our analysis reported on 83 different prediction models. The model developed by Sassone was the most evaluated prediction model. All models has acceptable sensitivity and specificity. However, the Risk of Malignancy Index I and the Risk of Malignancy Index II, which use the product of the serum CA 125 level, an ultrasound scan result, and the menopausal state, were the best predictors. When 200 was used as the cutoff level, the pooled estimate for sensitivity was 78% for a specificity of 87%. CONCLUSION: Based on our review, the Risk of Malignancy Index should be the prediction model of choice in the preoperative assessment of the adnexal mass.
Authors: Deborah Levine; Douglas L Brown; Rochelle F Andreotti; Beryl Benacerraf; Carol B Benson; Wendy R Brewster; Beverly Coleman; Paul Depriest; Peter M Doubilet; Steven R Goldstein; Ulrike M Hamper; Jonathan L Hecht; Mindy Horrow; Hye-Chun Hur; Mary Marnach; Maitray D Patel; Lawrence D Platt; Elizabeth Puscheck; Rebecca Smith-Bindman Journal: Radiology Date: 2010-05-26 Impact factor: 11.105
Authors: Ben Van Calster; Lil Valentin; Caroline Van Holsbeke; Antonia C Testa; Tom Bourne; Sabine Van Huffel; Dirk Timmerman Journal: BMC Med Res Methodol Date: 2010-10-20 Impact factor: 4.615
Authors: Elizabeth Lokich; Marguerite Palisoul; Nicole Romano; M Craig Miller; Katina Robison; Ashley Stuckey; Paul DiSilvestro; Cara Mathews; C O Granai; Geralyn Lambert-Messerlian; Richard G Moore Journal: Gynecol Oncol Date: 2015-09-11 Impact factor: 5.482