Made by DATEXIS (Data Science and Text-based Information Systems) at Beuth University of Applied Sciences Berlin
Deep Learning Technology: Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers and Alexander Löser. Learning Contextualized Document Representations for Healthcare Answer Retrieval. The Web Conference 2020 (WWW'20)
Funded by The Federal Ministry for Economic Affairs and Energy; Grant: 01MD19013D, Smart-MD Project, Digital Technologies
A widely recognised method of estimating the risk of malignant ovarian cancer based on initial workup is the "risk of malignancy index" (RMI). It is recommended that women with an RMI score over 200 should be referred to a centre with experience in ovarian cancer surgery.
The RMI is calculated as follows:
There are two methods to determine the ultrasound score and menopausal score, with the resultant RMI being called RMI 1 and RMI 2, respectively, depending on what method is used:
An RMI 2 of over 200 has been estimated to have a sensitivity of 74 to 80%, a specificity of 89 to 92% and a positive predictive value of around 80% of ovarian cancer. RMI 2 is regarded as more sensitive than RMI 1.
Ovarian cysts are usually diagnosed by ultrasound, CT scan, or MRI, and correlated with clinical presentation and endocrinologic tests as appropriate.