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Machine Learning Algorithms Incorporating Radiomic Ultrasound Features

NCT07793201 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 12000

Last updated 2026-08-28

No results posted yet for this study

Summary

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.

Conditions

  • Adnexal Masses

Sponsors & Collaborators

  • Fondazione Policlinico Universitario Agostino Gemelli IRCCS

    lead OTHER

Principal Investigators

  • Antonia Carla Testa · Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Eligibility

Min Age
18 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-30
Primary Completion
2028-01-31
Completion
2029-09-30

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07793201 on ClinicalTrials.gov