Machine Learning Algorithms Incorporating Radiomic Ultrasound Features
A legfontosabb adatok
- Hol
- Roma (Olaszország)A helyszín megnézése
AI-kivonat: kiket keres a vizsgálat
A vizsgálat saját részvételi feltételeiből. Gép olvasta ki az oldalon álló regiszterszövegből, ezért tévedhet. Hogy ki vehet részt, azt a lenti regiszterszöveg és a vizsgálat csapata dönti el.
A vizsgálat olyan felnőtteket keres, akiknek az ultrahangon adnexalis (a petefészek környéki) terimét találtak, és 6 hónapon belül műtétet kaptak, vagy legalább 1 évig követték őket.
- Életkor
- 18 éves vagy idősebb
- Nem
- Csak nők
- A betegség stádiuma
- Ultrahangon látható adnexalis (a petefészek környéki) terime
- Korábbi kezelés
- Vagy műtét az ultrahangvizsgálattól számított 6 hónapon belül, vagy legalább 1 év követés
A részvétel fő kizáró okai
- Nincs elérhető digitális ultrahangfelvétel
- Nincs végső eredmény (végső szövettan vagy 1 éves követés)
- Nincs aláírt írásos beleegyező nyilatkozat
Gépi kiolvasás: 2026. november 9.
Helyszínek itt: Olaszország
- Fondazione Policlinico Universitario Agostino Gemelli IRCCSRoma, OlaszországContact: Antonia Carla Testa, ProfessorNo longer taking part
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- Mi történik, ha ki akarok lépni?
Contact the study team: see the registry record → Contacts and Locations
Oldal nyomtatása az orvosomnakA vizsgálat adatai
- Ki végzi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Azonosító
- NCT07793201
- A regiszteradat frissítve
- 2026. augusztus 28.
- Eredeti leírás
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.A teljes eredeti leírásKevesebb
Machine Learning Algorithms Incorporating Radiomic Ultrasound Features for Clinical Precision in Diagnosing Adnexal Tumors and Optimizing Patient Management: a Multicenter IOTA Study
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.
Bevonási feltételek: Patients with an adnexal mass identified at ultrasound examination who either undergo surgery within 6 months of the ultrasound or have at least 1 year of follow-up. This includes: the retrospective cohort recruited from IOTA centers, and the prospective cohort recruited from nonIOTA centers; Age ≥ 18 years old; Availability of at least one grayscale digital ultrasound image clearly depicting the adnexal mass; Signed written informed consent.
Kizáró feltételek: Patients without available digital ultrasound images; Patients without an outcome (final histology or follow up at one year); Absence of written informed consent.