External validation of Risk of Malignancy Index compared to IOTA Simple Rules

Mulder, Esmee E; Gelderblom, Malou E.; Schoot, B.C.; Vergeldt, Tineke F. M.; Nijssen, Donna L; Piek, Jurgen M.J.

doi:10.1177/0284185120933990

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Earlier published studies assessing ultrasound prediction models show reasonable sensitivity (72–77%) and specificity (85–89%) for the RMI [ 65 , 66 ]. An external validation of the IOTA ADNEX model showed a better performance, with a sensitivity of 98% (95% CI 93–100%), but with low specificity of 62% at a cutoff value for malignancy of 10% (95% CI 55%–68%) [ 5 ].…”

Section: Discussionmentioning

confidence: 97%

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 97%

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

et al. 2023

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“…The level of accuracy of RMI 3 is shown by the PPV value of 86.2%, the NPV value of 83.3% and the accuracy value of 84.9%. In the study conducted by Tingulstad modified the RMI and defined RMI 3 and they observed that at the cut-off level of 200 the sensitivity and specificity were 71% and 92% 15 .…”

Section: Discussionmentioning

confidence: 99%

Risk of Malignancy Index 3 (RMI3) Performance as a Predictor Advanced Stage Epithelial Ovarian Carcinoma used for NACT

Nurseta,

Herliawati,

Harnandari

et al. 2022

Indones J Obstet Gynecol

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Objective: To find a non-invasive method in determining preoperative NACT administration. The method used is RMI 3 diagnostic scoring where this method can be used at the beginning of the examination and the results do not require a long time. assessed whether RMI3 performance can be used as a predictor of advanced epithelial ovarian carcinoma in the interest of NACT. Methods:A cross-sectional study with samples of all patients suffering from ovarian cancer for the past 5 years, from January 2016 to January 2020 who had been diagnosed at the Gynecology Polyclinic using medical record data from the Gynecology Oncology Polyclinic and Anatomical Pathology Laboratory, RSUD dr. Saiful Anwar in the form of age, demographics, menopausal status, Ca125, ultrasound results. Data is processed using SPSS version 25.0. Results:The number of initial samples of this study was 253 women, but after being included in the inclusion and exclusion criteria, there were 106 samples. After staging by an authorized clinician, there were 48 patients with early stage and 58 patients with advanced stage. Between the results of the RMI 3 score and the histopathological results on the ROC curve, it was found that the p-value was less than 0.05 (p<0.05) with an area of 0.945 and 95% CI of 0.907 -0.982. P-value less than 0.05 indicates that the RMI3 score is very good for predicting advanced epithelial ovarian carcinoma. With a sensitivity value of 86% and a specificity of 83%, the cut of value RMI score 3 to be a predictor of advanced ovarian carcinoma is 888.3 and PPV 86.2%, NPV 83.3% and an accuracy value of 84.9%. Conclusion:RMI 3 is very good to be used as a predictor of advanced ovarian carcinoma so that it is expected to be a reference for the administration of neoadjuvant chemotherapy in primary ovarian carcinoma which is predicted to be less likely to achieve optimal cytoreduction if surgery is performed to reduce the risk of mortality, morbidity and bad prognosis.

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“…RMI is a easy, simple and advicable method to differenciate malignant and benign adnexial masses (8). RMI is a numeric value which is calculated with the combination of serum Ca125 level, menopausal state and ultrasonographic ndings and modi ed to four different versions (9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Tumor Markers and Risk of Malignancy Index (RMI) in Borderline OvarianTumors Names of

Demircivi,

Donmez,

Callioglu

et al. 2024

Preprint

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Background Borderline ovarian tumours (BOTs) are non-invasive tumours with low malignancy potential frequently observed in patients of reproductive age. Therefore, preoperative differential diagnosis is important in these patients. In our study, we tried to differentiate benign and borderline ovarian tumours preoperatively using risk of malignancy (RMI) index and tumour markers. Methods In our study, we retrospectively compared tumour markers and risk of malignancy index (RMI) of 85 patients aged between 17–84 years with postoperative benign (n:52) and borderline (n:33) ovarian cysts. Results In our study, the mean age of the benign group was significantly higher than the borderline group (p = 0.001). Ca 125 and RMI values were significantly higher in the borderline group compared to the benign group (p = 0.001 and p = 0.018). In addition, mucinous tumours had significantly larger tumour diameter than serous tumours in the borderline group (p = 0.022). Conclusion As a result of our study, since borderline ovarian tumours are seen in young patients of reproductive age, it may be suggested to use Ca125 and RMI for the differential diagnosis of benign and borderline cysts preoperatively.

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External validation of Risk of Malignancy Index compared to IOTA Simple Rules

Cited by 6 publications

References 18 publications

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

Risk of Malignancy Index 3 (RMI3) Performance as a Predictor Advanced Stage Epithelial Ovarian Carcinoma used for NACT

Comparison of Tumor Markers and Risk of Malignancy Index (RMI) in Borderline OvarianTumors Names of

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