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.
Study Type
OBSERVATIONAL
Enrollment
220
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).
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Roma, Italy
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.
Time frame: Up to 12 months after primary treatment.
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