Clinical Validation of Radiomics Artificial Intelligence: Application to Breast Cancer Treatment Planning

NCT05070884 | Unknown

• 1 other identifier: RADIOVAL
• Study Type: observational
• Target: 5,000
• Locations: 8 countries
• Sites: 8

Brief Summary

RadioVal will develop and implement interoperable solutions for clinical deployment of the radiomics tools, including information, training, and communication packages for clinicians and patients, as well as standard operating procedures for the integration of radiomics in clinical oncology. With this study, we will clinically validate these solutions, by looking at their reliability for precise breast cancer diagnosis, treatment recommendation and prognosis estimate, treatment response, evaluation of residual disease and outcome prediction.

Conditions

Breast Cancer

Outcome Measures

Primary Outcomes (1)

• Percentage of patients non-respondents vs respondents in neoadjuvant breast cancer treatment (Estimate tumor aggressiveness)

  Proportion of patients who have complete response evaluating the target lesion according to Miller/Payne Grading system \[Ogston et al., 2003\]: 1A. Evaluation of target Tumor: G5 as pathological complete response, no tumor left; G4:more than 90% loss of tumor cells; G3: between 30-90% reduction in tumor cells; G2: loss of tumor \<30%; G1: no reduction. 1B: Evaluating the lymph nodes: A: negative; B: lymph nodes with metastasis and without changes by chemotherapy; C: lymph nodes with metastasis with evidence of partial response, D: lymph nodes with changes attributed to response without residual infiltration. 1C: Using images to evaluated radiological response: Size and diameter in millimeters of the target lesion using RM and TC or PET/CT for extension analysis (lymph nodes and metastasis).

  Baseline and after neoadjuvant treatment (4-6 months)

Study Arms (1)

non respondants of neo treatment

those patients with no response or partial response when administered with Chemotherapy prior to sugery

Other: The development of new clinical AI solutions to predict treatment response to neoadjuvant chemotherapy (NAC) in breast cancer

Interventions

The development of new clinical AI solutions to predict treatment response to neoadjuvant chemotherapy (NAC) in breast cancer OTHER

Evaluate response to neoadjuvant treatment in advanced breast cancer

non respondants of neo treatment

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: female
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients treated with chemotherapy prior to surgery

You may qualify if:

• Females ≥ 18 years up to 85 years old
• Individuals referred to hospitals for diagnosis of breast cancer
• Availability of radiological images: 2D mammography or 2D synthetic digital tomosynthesis, ultrasound, or magnetic resonance
• Availability of pathological report (surgical specimen)
• Availability of (Neoadjuvant) treatment allocation (scheme, duration, benefit)
• Availability of treatment response

You may not qualify if:

• Patient with incomplete or low-quality data (radiological, pathological or clinical)

Sponsors & Collaborators

• Instituto de Investigacion Sanitaria La Fe: lead
• Karolinska Institutet: collaborator
• Medical University of Gdansk: collaborator
• Medical School University of Zagreb: collaborator
• Medical University of Vienna: collaborator
• Hacettepe University: collaborator
• Alexander Fleming Institute: collaborator
• Ain Shams University: collaborator

Study Sites (8)

Alexander Fleming

Buenos Aires, Argentina

NOT YET RECRUITING

Medical University of Vienna

Vienna, Austria

NOT YET RECRUITING

University of Zagreb School of Medicine

Zagreb, Croatia

NOT YET RECRUITING

Ain Shams University

Cairo, Egypt

NOT YET RECRUITING

Medical University of Gdansk

Gdansk, Poland

NOT YET RECRUITING

Hospital Universitario y Politécnico La Fe de Valencia

Valencia, 46026, Spain

RECRUITING

Karolinska Institute

Stockholm, Sweden

NOT YET RECRUITING

Hacettepe University

Ankara, Turkey (Türkiye)

NOT YET RECRUITING

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Ana Penades-Blasco, M.Ec

CONTACT

+34 961245633; ana_penades@iislafe.es

Study Design

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

Study Record Dates

• First Submitted: September 20, 2021
• First Posted: October 7, 2021
• Study Start: June 1, 2023
• Primary Completion: August 1, 2025
• Study Completion: December 1, 2025
• Last Updated: February 3, 2023

Record last verified: 2022-09

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1); PL (1); AU (1); AR (1); TU (1); SE (1); CR (1); ES (1)