Clinical trial · Observational
A Clinical Evaluation of AI Solutions Developed in the CHAIMELEON Project for Cancer: Prostate, Lung, Breast, Colon and Rectum
An in Silico External Clinical Validation of AI Solutions for Cancer Management in the CHAIMELEON Project. Applied to 4 Target Types of Cancer (Lung, Breast, Prostate and Colorectal), Collected Through the Routine Delivery of Health Care With no Enrolment Conditinos (Real World Data).
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this observational study is to see how useful an experimental viewer and AI solutions are for clinicians in their daily work. The investigators want to find out if the AI helps clinicians interpret medical images for different types of cancer. 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.
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Colon Cancer | Malignant Colon Neoplasm | CURATED_EXACT | 0.92 |
| Lung Cancer, Non-Small Cell | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
| Rectum Cancer | Rectal Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (5)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Histological subtype | Other | — | UNRESOLVED |
| invasion in rectum cancer | Other | — | UNRESOLVED |
| Life expectancy in lung cancer | Other | — | UNRESOLVED |
| Risk in prostate cancer | Other | — | UNRESOLVED |
| Staging of colon cancer | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Group 1: Evaluation with Medical expertise only
- description
- Evaluation of different medical images of people with 5 types of cancer using their own expertise.
- interventionNames
- Other: Risk in prostate cancer
- Other: Life expectancy in lung cancer
- Other: Histological subtype
- Other: Staging of colon cancer
- Other: invasion in rectum cancer
- label
- Group 2: Evaluation with the support of AI solutions
- description
- Evaluation of different medical images of people with 5 types of cancer guided by the AI solutions developed.
- interventionNames
- Other: Risk in prostate cancer
- Other: Life expectancy in lung cancer
- Other: Histological subtype
- Other: Staging of colon cancer
- Other: invasion in rectum cancer
Primary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 85 Years
Show eligibility criteria text
Inclusion Criteria: * patients with an histological confirmation of cancer diagnosis (prostate, lung, breast, colon or rectum) * 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: * patients with incomplete or low quality data (radiological, pathological or uncomplete clinical data necessary for the ground truth)
References
Publications (2)
- BACKGROUNDYilmaz 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. PMID 36408543
- DERIVEDGaliana-Bordera A, Aquerreta-Escribano J, Martinez-Girones PM, Lozano P, Ribas G, Jimenez-Gomez P, Segrelles Quilis JD, Cerda-Alberich L, Blanquer-Espert I, Marti-Bonmati L. Bridging the AI chasm in oncology: a standardized platform to enable the in silico clinical validation of AI models within the CHAIMELEON Project. Eur Radiol Exp. 2026 Jul 31;10(1):111. doi: 10.1186/s41747-026-00770-7. PMID 42536277