Adnexal Masses

Adnexal masses represent a frequent clinical finding and their preoperative characterization remains challenging. Accurate discrimination between benign and malignant adnexal masses is essential to optimize patient management, avoid unnecessary surgery, and ensure appropriate referral of patients with suspected malignancy to specialized centers. This multicenter international observational study aims to evaluate the diagnostic performance and clinical utility of ultrasound-based machine learning (ML) models incorporating radiomic features for the characterization of adnexal masses. The study will develop and validate artificial intelligence (AI)-based models using ultrasound imaging data to support the preoperative classification of adnexal masses. The primary objective of the study is to evaluate the ability of ultrasound-based ML models to distinguish between benign and malignant adnexal masses. Secondary objectives include the evaluation of additional AI-based classification models among masses identified as malignant, including the discrimination between borderline tumors, primary invasive malignancies, and metastatic lesions. Furthermore, the study will assess the ability of AI models to differentiate primary epithelial ovarian carcinoma from non-epithelial ovarian malignancies among cases classified as primary ovarian cancer. The clinical utility of the developed models will be assessed using decision curve analysis. In addition, a retrospective post hoc evaluation will be performed in an independent prospective external validation cohort to explore the potential clinical impact of an AI-based preoperative model for the management of adnexal masses. This evaluation will compare AI model outputs with actual clinical decisions made during routine care, without influencing patient management or altering the diagnostic and therapeutic pathway. The post hoc clinical impact analysis will assess diagnostic concordance between AI predictions and clinicians' preoperative assessments, the potential proportion of avoidable surgical procedures according to AI model predictions, surgical and follow-up complications, and cost-effectiveness through comparison of healthcare resource utilization between standard clinical management and a reconstructed AI-supported scenario. Patient-reported outcomes will also be evaluated, including patient satisfaction regarding diagnostic communication, clarity of information provided, and perceived quality of care within the standard clinical management pathway. Overall, this study aims to investigate whether ultrasound-based AI models integrating radiomic features can improve the characterization of adnexal masses and provide clinically useful tools to support personalized and efficient patient management.

Adnexal masses are growths that can form in the ovaries or fallopian tubes for different reasons, such as hormonal changes, infection, or cancer. These masses may cause pelvic discomfort, pain, constipation, or no symptoms at all. When adnexal masses are found on scans, they are described in a certain way to indicate if they could represent early-stage cancer, and the word "complex" is used to refer to these masses. Surgery is often recommended, where the mass is removed and examined under the microscope during surgery in a process called (frozen section analysis); to determine its true nature. It is still difficult to confirm cancer before surgery, and many of these masses turn out to be benign (not cancerous) or borderline (slow-growing tumours). Currently, doctors use open surgery with a cut from at least the belly button to the pubic bone to remove these masses. Patients with a cancer diagnosis will then have more surgical steps including assessment and sampling of various areas inside the abdomen (known as staging surgery) to see how far the cancer has spread. Recovery after open surgery can be long and painful, with a slow return to normal daily activities. The trial investigators know from practice that robotic surgery has replaced open surgery for most benign adnexal diseases and other types of women's cancers, such as womb cancer. Recovery is quicker, with less pain and blood loss, allowing for a faster return to daily activities. This study, MIRRORS-FROZEN (pilot), compares robotic versus the standard open surgery in managing women with complex adnexal masses of eight centimetres or less. The hope is to decrease the need for open surgery in patients with benign or borderline disease and to assess if robotic surgery has similar, worse, or better outcomes for patients with cancer. MIRRORS-FROZEN is funded by Intuitive Foundation and GRACE Charity. The investigators will establish the feasibility of conducting a large multicentre randomized controlled trial in the future comparing certain cancer outcomes between robotic and open surgery.