A Clinical Evaluation of AI Solutions Developed in the CHAIMELEON Project for Cancer: Prostate, Lung, Breast, Colon and Rectum
NCT ID: NCT06950996
Last Updated: 2025-04-30
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
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
COMPLETED
300 participants
OBSERVATIONAL
2024-09-01
2024-11-01
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
The AI solutions aim to:
* Classify whether prostate cancer is low or high risk
* Classify the histological subtype in breast cancer
* Estimate the life expectancy of patients with lung cancer
* Determine the size of colon cancer, lymph node involvement and the possibility of metastasis..
* Assess the invasion of sorrounding tissues in the case of rectum cancer. The study will involve clinicians from various centres who will review a set of cases not previously analysed by the AI. Clinicians will do this in two phases: first using only their own expertise and then with the help of the AI solutions.
The technical team want to see if the AI solutions assist clinicians and could become useful in the everyday clinical practice. Clinicians will complete a survey to share their feedback on the usability of the platform and how helpful the AI solutions are.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
NCT06168864
Artificial Intelligence-Based Computer-Aided Diagnosis of Prostate Cancer
NCT05513638
Investigation of Impact of AI on Prostate Cancer Workflow
NCT07084779
Acceptability of Artificial Intelligence in the Diagnosis of Prostate Cancer
NCT07074405
Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
NCT06253065
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
An 10% improvement in cancer risk classification was observed when clinicians had access to an AI tool solution (Yilmaz et al.,). In addition, the authors reported that expert readers had an accuracy rate of 81% compared to 69% for novice readers when determining the Gleason score of lesions (a medical term used in pathology to classify the aggressiveness of cells in a tumour). The authors also assumed an 80% correlation between paired observations.
As a result, at least 60 new cases would be needed to evaluate the performance of the AI tool.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
RETROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Group 1: Evaluation with Medical expertise only
Evaluation of different medical images of people with 5 types of cancer using their own expertise.
Risk in prostate cancer
the prediction involves the classification of the prostate cancer according to the level of prostatic antigen (PSA), the biopsy classification of the aggressiveness of the tumour, and also the localisation of the tumour
Life expectancy in lung cancer
Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information.
Histological subtype
An assessment by pathology of the subtype of breast tumour
Staging of colon cancer
classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region
invasion in rectum cancer
assess whether vascular extramural o mesorectal fascia has been invaded in the tumour using magnetic resonance medical images taken at diagnosis in the pelvic region
Group 2: Evaluation with the support of AI solutions
Evaluation of different medical images of people with 5 types of cancer guided by the AI solutions developed.
Risk in prostate cancer
the prediction involves the classification of the prostate cancer according to the level of prostatic antigen (PSA), the biopsy classification of the aggressiveness of the tumour, and also the localisation of the tumour
Life expectancy in lung cancer
Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information.
Histological subtype
An assessment by pathology of the subtype of breast tumour
Staging of colon cancer
classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region
invasion in rectum cancer
assess whether vascular extramural o mesorectal fascia has been invaded in the tumour using magnetic resonance medical images taken at diagnosis in the pelvic region
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Risk in prostate cancer
the prediction involves the classification of the prostate cancer according to the level of prostatic antigen (PSA), the biopsy classification of the aggressiveness of the tumour, and also the localisation of the tumour
Life expectancy in lung cancer
Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information.
Histological subtype
An assessment by pathology of the subtype of breast tumour
Staging of colon cancer
classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region
invasion in rectum cancer
assess whether vascular extramural o mesorectal fascia has been invaded in the tumour using magnetic resonance medical images taken at diagnosis in the pelvic region
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* availability of radiological images (MR for prostate and rectum, CT for lung and colon or mammographys for breast).
* enough follow up (12 months for prostate, breast and rectum), 18 months for lung, and 24 months for colon.
Exclusion Criteria
18 Years
85 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
University of Pisa
OTHER
University Hospital Rijeka
OTHER
University of Messina
OTHER
Istanbul Medipol University Hospital
OTHER
Centro Hospitalar do Porto
OTHER
Hospitales Universitarios Virgen del Rocío
OTHER
IRCCS Policlinico S. Donato
OTHER
National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
OTHER
Charite University, Berlin, Germany
OTHER
Osakidetza
OTHER
Le Collège des Enseignants de Radiologie de France
OTHER
Instituto de Investigacion Sanitaria La Fe
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Hospital Universitario y Politécnico la Fe
Valencia, , Spain
Countries
Review the countries where the study has at least one active or historical site.
References
Explore related publications, articles, or registry entries linked to this study.
Yilmaz EC, Turkbey B. The added value of a deep learning-based computer-aided detection system on prostate cancer detection among readers with varying level of multiparametric MRI expertise. Chin Clin Oncol. 2022 Dec;11(6):42. doi: 10.21037/cco-22-104. Epub 2022 Nov 15. No abstract available.
Related Links
Access external resources that provide additional context or updates about the study.
Accelerating the lab to market transition of AI tools for cancer management is an European project that contemplates the evaluation of the in silico clinical validation
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
952172
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
CHAIMELEON insilico validation
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.