Making ovarian cancer diagnostic models useful for clinical practice: regulatory issues, multinomial outcomes, differential verification, and deep learning assistance

Music City Center 

When patients present with an ovarian tumor, it is important to provide optimal management. Benign tumors can often be managed conservatively, whereas malignant tumors require surgery that depends on the type of malignancy. A 2014 systematic review identified 116 models, based on based on score systems or binary risk models. The International Ovarian Tumor Analysis consortium developed the first multinomial model (ADNEX) that differentiates between 4 types of malignancy based on clinical and ultrasound data. A 2024 systematic review of ADNEX identified 47 validation studies reporting on 17,000 tumors. The meta-analysis resulted in an AUROC of 0.93 (95% prediction interval 0.85-0.98). ADNEX has been incorporated in ultrasound machines from several manufacturers, implemented in iOS and Android apps and as an online calculator. Currently, the apps and calculator are down due to absence of a CE label, which is near impossible to obtain from a purely academic point of view. Most studies only include women that were operated, such that histology can be used as a reference standard. This may have resulted in selection bias. A next objective is therefore to update ADNEX to the full population of women with ovarian tumors. Finally, to avoid subjectivity in the measurement of ultrasound predictors, we have developed a prototype ADNEX-AI model where ultrasound features are measured using deep learning.

Clinical prediction models

Implementation

Deep learning