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

Studien-Informationen Einschlusskriterien Studien-Rationale Geprüfte Regime

Rekrutierend

NCT-Nummer:
NCT05070884

Studienbeginn:
Juni 2023

Letztes Update:
03.02.2023

Wirkstoff:
-

Indikation (Clinical Trials):
Breast Neoplasms

Geschlecht:
Frauen

Altersgruppe:
Erwachsene (18+)

Phase:
-

Sponsor:
Instituto de Investigacion Sanitaria La Fe

Collaborator:
Karolinska Institutet, Medical University of Gdansk, Medical School University of Zagreb, Medical University of Vienna, Hacettepe University Hospital, Alexander Fleming Institute, Ain Shams University,

Kontakt

Ana Penades-Blasco, M.Ec
Kontakt:
Phone: +34 961245633
E-Mail: ana_penades@iislafe.es» Kontaktdaten anzeigen

Studienlocations
(3 von 8)

Alexander Fleming
Buenos Aires
ArgentinaNoch nicht rekrutierend» Google-Maps

Medical University of Vienna
Vienna
AustriaNoch nicht rekrutierend» Google-Maps

University of Zagreb School of Medicine
Zagreb
CroatiaNoch nicht rekrutierend» Google-Maps

Ain Shams University
El Cairo
EgyptNoch nicht rekrutierend» Google-Maps

Medical University of Gdansk
Gdańsk
PolandNoch nicht rekrutierend» Google-Maps

Hospital Universitario y Politécnico La Fe de Valencia
46026 Valencia
SpainRekrutierend» Google-Maps
Ansprechpartner:
Gloria Ribas, PHD
E-Mail: gloria_ribas@iislafe.es» Ansprechpartner anzeigen

Karolinska Institute
Stockholm
SwedenNoch nicht rekrutierend» Google-Maps

Hacettepe University
Ankara
TurkeyNoch nicht rekrutierend» Google-Maps

Alle anzeigen

Studien-Informationen

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.

Ein-/Ausschlusskriterien

Inclusion Criteria:

- 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

Exclusion Criteria:

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

Studien-Rationale

Primary outcome:

1. Percentage of patients non-respondents vs respondents in neoadjuvant breast cancer treatment (Estimate tumor aggressiveness) (Time Frame - Baseline and after neoadjuvant treatment (4-6 months)):
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).

Geprüfte Regime

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

Quelle: ClinicalTrials.gov

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