US Radiomics in Advanced Cervical Cancer

NCT06984289 | Completed DMC

• 1 other identifier: 7443
• Study Type: observational
• Target: 220
• Locations: 1 country
• Sites: 1

Brief Summary

This is a retrospective, multicenter observational study aimed at evaluating the role of ultrasound-based radiomics in patients with locally advanced cervical cancer (LACC). The study will analyze pre-treatment ultrasound images to identify radiomic features that may predict treatment response and disease recurrence. A total of 220 patients treated with exclusive chemoradiotherapy or neoadjuvant chemoradiotherapy followed by radical surgery between 2011 and 2024 will be included. Using clinical and imaging data, machine learning models will be developed to distinguish between responders and non-responders, and to identify patients at higher risk of relapse. The goal is to improve personalized care in LACC by integrating radiomic analysis into treatment planning and follow-up strategies.

Conditions

Uterine Cervical Neoplasms; Locally Advanced Cervical Cancer; Radiomics; Ultrasound Imaging; Machine Learning; Chemoradiotherapy

Keywords

Locally Advanced Cervical Cancer; Cervical Cancer; Uterine Cervical Neoplasms; Radiomics; Ultrasound; Ultrasound Imaging; Machine Learning; Gynecologic Oncology

Outcome Measures

Primary Outcomes (1)

• Performance of ultrasound-based radiomic models in predicting treatment response in patients with locally advanced cervical cancer.

  Evaluation of the diagnostic accuracy (AUC, sensitivity, specificity, F1-score) of radiomic models based on pre-treatment ultrasound images in distinguishing responder vs. non-responder patients to primary treatment (exclusive chemoradiotherapy or neoadjuvant chemoradiotherapy followed by radical surgery). Models will be developed and validated using retrospective data.

  Up to 12 months after primary treatment.

Study Arms (2)

Responder Group

Patients with locally advanced cervical cancer who responded to primary treatment (either exclusive chemoradiotherapy or neoadjuvant chemoradiotherapy followed by radical surgery), as determined by clinical and/or histological assessment.

Other: Radiomic Analysis of Ultrasound Images

Non-Responder Group

Patients with locally advanced cervical cancer who did not respond to primary treatment (either exclusive chemoradiotherapy or neoadjuvant chemoradiotherapy followed by radical surgery), based on residual disease findings or lack of clinical response.

Other: Radiomic Analysis of Ultrasound Images

Interventions

Radiomic Analysis of Ultrasound Images OTHER

Quantitative analysis of pre-treatment ultrasound images of the primary cervical tumor to extract radiomic features. These features will be used to develop and validate machine learning models for predicting treatment response and disease relapse in patients with locally advanced cervical cancer (LACC).

Also known as: Radiomics, Ultrasound-based Radiomics, Radiomic Feature Extraction

Non-Responder Group; Responder Group

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with locally advanced cervical cancer (FIGO 2018 IB3-IVA, excluding IIA1), treated between 2011 and 2024, from multiple Italian centers. All participants underwent ultrasound before treatment and have available DICOM images for radiomic analysis.

You may qualify if:

• Female patients aged ≥18 years
• Histologically confirmed diagnosis of locally advanced cervical cancer (FIGO 2018 stage IB3-IVA, excluding IIA1)
• Histologic subtypes: squamous cell carcinoma, adenocarcinoma, or adenosquamous carcinoma
• Underwent transvaginal or transrectal ultrasound prior to treatment
• At least one pre-treatment DICOM ultrasound image of the primary tumor available
• Treated with either exclusive chemoradiotherapy or neoadjuvant chemoradiotherapy followed by radical surgery
• Completed at least 12 months of follow-up after primary treatment
• Signed informed consent (or equivalent declaration)

You may not qualify if:

• Age \<18 years
• Only printed ultrasound images available
• Ultrasound images with poor tumor visualization or with text/markers obscuring the tumor

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead

Study Sites (1)

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Roma, 00168, Italy

Location

MeSH Terms

Conditions

Uterine Cervical Neoplasms

Condition Hierarchy (Ancestors)

Uterine Neoplasms; Genital Neoplasms, Female; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Uterine Cervical Diseases; Uterine Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

Study Officials

• Camilla Culcasi

  Fondazione Policlinico Universitario Agostino Gemelli

  STUDY CHAIR

Study Design

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

Study Record Dates

• First Submitted: May 14, 2025
• First Posted: May 22, 2025
• Study Start: March 25, 2025
• Primary Completion: April 30, 2025
• Study Completion: May 14, 2025
• Last Updated: May 22, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)