Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
220 participants
OBSERVATIONAL
2025-03-25
2025-05-14
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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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.
Radiomic Analysis of Ultrasound Images
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).
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.
Radiomic Analysis of Ultrasound Images
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).
Interventions
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Radiomic Analysis of Ultrasound Images
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).
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
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)
Exclusion Criteria
Only printed ultrasound images available
Ultrasound images with poor tumor visualization or with text/markers obscuring the tumor
18 Years
FEMALE
No
Sponsors
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Fondazione Policlinico Universitario Agostino Gemelli IRCCS
OTHER
Responsible Party
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Principal Investigators
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Camilla Culcasi
Role: STUDY_CHAIR
Fondazione Policlinico Universitario Agostino Gemelli
Locations
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Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Roma, , Italy
Countries
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Other Identifiers
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7443
Identifier Type: -
Identifier Source: org_study_id
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