A Radiomic MRI Predictive Model for Response to Concomitant Chemoradiotherapy in Locally Advanced Cervical Cancer

NCT07305727 | Recruiting

• 1 other identifier: SIRIC/2024/FF01
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

Cervical cancer is the fourth most common cancer in women worldwide, with approximately 604,000 new cases in 2020.Treatment for locally advanced cervical cancer is based on a combination of radiotherapy and chemotherapy. The response to concomitant chemoradiotherapy vary from one woman to another. Predicting the response to these treatments would allow early consideration of alternative therapies for patients identified as less responsive to standard treatments. A 5-year recurrence-free survival is approximately 79% for stages IB and IIA and 59% for stages III and IVA, with approximately 36% of local failures despite chemoradiotherapy. In a few studies,the radiomic MRI approach in locally advanced cervical cancers has shown to be prognostic for locoregional recurrence or survival but these models still need to be explored and validated.The EPICOL cohort, a clinical-biological cohort of 136 patients treated with chemoradiotherapy for locally advanced cervical cancer at the Montpellier Cancer Institute or Nîmes University Hospital, will be used to develop a predictive model of response to chemoradiotherapy based on radiomic data from pelvic MRIs before and after treatment.

Conditions

Cervical Cancer by FIGO Stage 2018; Uterine Cervical Neoplasms

Keywords

cancer; cervix; survival modeling; radiomic models

Outcome Measures

Primary Outcomes (1)

• Prognostic role of a magnetic resonance imaging radiomic model on progression-free survival in patients treated for locally advanced cervical cancer.

  Time from diagnosis to death from any cause or progression (according to RECIST v1.1 criteria) at 24 months of follow-up.

  Month 24

Secondary Outcomes (3)

• Prognostic role of an magnetic resonance imaging radiomic model on overall survival in patients treated for locally advanced cervical cancer.

  Month 24

• Correlation between the radiomic magnetic imaging radiomic model and Programmed cell Death protein 1 (PD-L1) expression.

  Month 24

• Correlation between the radiomic magnetic resonance imaging model and tumor-infiltrating lymphocytes (TILs).

  Month 24

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: Human beings who possess a uterus with a cervix.
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

For this study, data from 120 patients for whom we have MRI data from the EPICOL cohort (136 patients included) will be used.

You may qualify if:

• Patients treated with exclusive radio-chemotherapy for locally advanced cervical cancer (stage Ib-IVb according to the FIGO classification).
• Patients with a minimum of 2 years of post-treatment follow-up.
• Patients for whom the initial biopsy specimen (prior to treatment) is available.
• Patients who have not expressed their opposition to participating in the study.
• Patients who are affiliated with or beneficiaries of a health insurance plan.

You may not qualify if:

• Patients under judicial protection, guardianship, or curatorship

Sponsors & Collaborators

• Centre Hospitalier Universitaire de Nīmes: lead

Study Sites (1)

Nimes University Hospital

Nîmes, Gard, 30029, France

RECRUITING

Related Publications (6)

• Pang SS, Murphy M, Markham MJ. Current Management of Locally Advanced and Metastatic Cervical Cancer in the United States. JCO Oncol Pract. 2022 Jun;18(6):417-422. doi: 10.1200/OP.21.00795. Epub 2022 Mar 14.

  PMID: 35286157 BACKGROUND

• Zola P, Fuso L, Mazzola S, Piovano E, Perotto S, Gadducci A, Galletto L, Landoni F, Maggino T, Raspagliesi F, Sartori E, Scambia G. Could follow-up different modalities play a role in asymptomatic cervical cancer relapses diagnosis? An Italian multicenter retrospective analysis. Gynecol Oncol. 2007 Oct;107(1 Suppl 1):S150-4. doi: 10.1016/j.ygyno.2007.07.028. Epub 2007 Sep 14.

  PMID: 17868785 BACKGROUND

• Autorino R, Gui B, Panza G, Boldrini L, Cusumano D, Russo L, Nardangeli A, Persiani S, Campitelli M, Ferrandina G, Macchia G, Valentini V, Gambacorta MA, Manfredi R. Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy. Radiol Med. 2022 May;127(5):498-506. doi: 10.1007/s11547-022-01482-9. Epub 2022 Mar 24.

  PMID: 35325372 BACKGROUND

• Bizzarri N, Russo L, Dolciami M, Zormpas-Petridis K, Boldrini L, Querleu D, Ferrandina G, Pedone Anchora L, Gui B, Sala E, Scambia G. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int J Gynecol Cancer. 2023 Oct 2;33(10):1522-1541. doi: 10.1136/ijgc-2023-004589.

  PMID: 37714669 BACKGROUND

• Halle MK, Hodneland E, Wagner-Larsen KS, Lura NG, Fasmer KE, Berg HF, Stokowy T, Srivastava A, Forsse D, Hoivik EA, Woie K, Bertelsen BI, Krakstad C, Haldorsen IS. Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer. Sci Rep. 2024 May 17;14(1):11339. doi: 10.1038/s41598-024-61271-4.

  PMID: 38760387 BACKGROUND

• Li H, Zhu M, Jian L, Bi F, Zhang X, Fang C, Wang Y, Wang J, Wu N, Yu X. Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer. Front Oncol. 2021 Aug 16;11:706043. doi: 10.3389/fonc.2021.706043. eCollection 2021.

  PMID: 34485139 BACKGROUND

MeSH Terms

Conditions

Uterine Cervical Neoplasms; Neoplasms

Condition Hierarchy (Ancestors)

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

Central Study Contacts

Frédéric FITENI, Professor

CONTACT

+334.34.03.46.69; frederic.fiteni@chu-nimes.fr

Anissa MEGZARI

CONTACT

0466684236; drc@chu-nimes.fr

Study Design

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

Study Record Dates

• First Submitted: December 12, 2025
• First Posted: December 26, 2025
• Study Start: January 1, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: January 7, 2026

Record last verified: 2025-12

Locations

FR (1)