The Use of Artificial Intelligence for Ultrasound Screening of Ovarian Cancer in Postmenopausal Women

NCT07660718 | Active Not Recruiting

• 1 other identifier: ASTOM
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The ASTOM study is a monocentric prospective observational pilot study conducted at the Gynecologic Oncology Unit of the IRCCS Azienda Ospedaliero-Universitaria of Bologna. This study is aimed at evaluating the application of artificial intelligence to ultrasound screening for ovarian cancer in postmenopausal women, a population at increased risk in which early diagnosis remains a major clinical challenge due to the absence of effective screening methods. Ovarian cancer accounts for a significant proportion of gynecological malignancies and is the leading cause of death among gynecologic cancers in developed countries. Although gynecologic ultrasound is currently the first-line imaging modality for the characterization of adnexal masses, its diagnostic performance is strongly dependent on operator expertise, leading to variability in interpretation and potential misclassification of lesions, particularly in complex cases such as multilocular or multilocular-solid ovarian cysts, which may represent either benign conditions such as cystadenomas or malignant lesions including primary ovarian carcinomas or metastases from gastrointestinal tumors. In this context, the ASTOM study seeks to develop an integrated predictive model combining clinical data, ultrasound imaging, and radiomic features extracted from images, with the goal of improving preoperative oncological risk stratification and supporting clinical decision-making, thereby contributing to a precision medicine approach that could reduce unnecessary surgical interventions in patients with low-risk lesions while ensuring appropriate management of high-risk cases. The study will enroll approximately 100 menopausal women aged between 18 and 90 years presenting with ultrasound evidence of multilocular or multilocular-solid ovarian cysts, either awaiting surgery or undergoing follow-up for stable adnexal masses. All participants will undergo standard clinical and ultrasound evaluations as part of routine care, with additional collection of anonymized clinical, imaging, and radiomic data for research purposes. Ultrasound images will be acquired using a dedicated machine and standardized protocols, and volumes of interest will be delineated by expert sonographers, after which radiomic features will be extracted using validated software tools and integrated into a centralized database. The predictive model will be developed using advanced machine learning techniques, including convolutional neural networks, to automatically or semi-automatically classify lesions according to their risk of malignancy and, in high-risk cases, to differentiate primary ovarian tumors from metastatic lesions, particularly those originating from the gastrointestinal tract, which often present with overlapping imaging characteristics. The primary endpoint of the study is the diagnostic performance of the integrated model in accurately stratifying ovarian lesions into different risk categories, measured through metrics such as sensitivity, specificity, accuracy, positive and negative predictive values, and area under the ROC curve, with comparison to the performance of expert sonographers. The secondary objective focuses on the model's ability to distinguish primary ovarian neoplasms from metastases, an area where current models such as the widely used ADNEX algorithm show limitations. Statistical analysis will include descriptive analysis of patient characteristics, evaluation of model performance, subgroup analyses, multivariate regression to control for confounding factors, and sensitivity analyses to assess robustness, with appropriate handling of missing data through imputation techniques. As a pilot feasibility study, no formal sample size calculation has been performed, but the estimated cohort size is based on historical patient volumes at the study center and is considered sufficient to develop and preliminarily validate the model, generating data that may inform larger future studies. The overall duration of the study is expected to be 24 months, including 12 months for patient recruitment, followed by phases of follow-up and data analysis. The ASTOM study represents an innovative attempt to integrate artificial intelligence into routine gynecological oncology practice, addressing current limitations of operator-dependent imaging interpretation and existing predictive models, with the potential to enhance diagnostic accuracy, optimize patient management pathways, and contribute to the broader implementation of data-driven precision medicine in ovarian cancer care.

Conditions

Ovarian Cancer

Outcome Measures

Primary Outcomes (1)

• The predictive performance of the integrated model in accurately discriminating between the different risk categories of ovarian lesions.

  The outcome will be assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).The achievement of the primary objective will be considered met if the integrated model demonstrates diagnostic performance equal to or greater than that achieved by an expert sonographer in the discrimination of adnexal lesions.

  18 months from enrollment

Secondary Outcomes (1)

• The model's ability to distinguish primary ovarian lesions from metastases originating from gastrointestinal tumors.

  18 months from the enrollment

Eligibility Criteria

• Age: 18 Years - 90 Years
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

About 100 postmenopausal patients with ultrasound evidence of multilocular or multilocular-solid ovarian cysts

You may qualify if:

• Age between 18 and 90 years
• Ultrasound evidence of a multilocular or multilocular-solid ovarian cyst
• Menopausal status (last menstrual period at least 12 months prior)
• Patients awaiting surgery or undergoing ultrasound follow-up with evidence of a stable adnexal mass over time
• Provision of informed consent

You may not qualify if:

• Ultrasound evidence of a unilocular cyst, unilocular-solid cyst, or solid mass
• Patient not in menopause
• No planned surgical intervention and unavailable ultrasound follow-up

Sponsors & Collaborators

• IRCCS Azienda Ospedaliero-Universitaria di Bologna: lead

Study Sites (1)

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Bologna, Bologna, 40138, Italy

Location

MeSH Terms

Conditions

Ovarian Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 16, 2026
• First Posted: June 22, 2026
• Study Start: June 12, 2026
• Primary Completion (Estimated): December 12, 2027
• Study Completion (Estimated): June 12, 2028
• Last Updated: June 22, 2026

Record last verified: 2026-06

Locations

IT (1)