A Clinical Evaluation of AI Solutions Developed in the CHAIMELEON Project for Cancer: Prostate, Lung, Breast, Colon and Rectum

NCT06950996 | Completed

• 2 other identifiers: CHAIMELEON insilico validation952172
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

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

Breast Cancer; Lung Cancer, Non-Small Cell; Colon Cancer; Rectum Cancer; Prostate Cancer

Keywords

Clinical validation; AI model solutions; experimental dedicated-platform

Outcome Measures

Primary Outcomes (2)

• Usability of experimental viewer with AI tools

  Usability of the platform was assessed at the end of each of the two study phases: a standard clinical phase (without artificial intelligence assistance) and a second phase assisted by AI models. Participants evaluated their experience using a 5-point Likert scale, where 1 indicated "strongly disagree" and 5 indicated "strongly agree," in response to statements regarding ease of use, interface clarity, system efficiency, overall satisfaction, and other aspects related to user interaction with the platform. This assessment enabled a comparison of user perceptions of the viewer's usability under both conventional clinical conditions and AI-assisted conditions. Higher scores reflect a better user experience.

  5 months

• Utility of experimental medical images viewer

  The utility of the experimental viewer was assessed by comparing clinicians' diagnostic accuracy and time spent when using the system alone versus with AI assistance. Higher accuracy and reduced interpretation time were considered indicators of greater utility. The goal was to determine whether the viewer enhances clinical decision-making, streamlines workflows, and supports better patient care. Additional data such as clinician gender, specialty, and experience were collected to enable subgroup analyses. Statistical evaluations included confusion matrices to assess diagnostic performance, and Sankey flow diagrams to visualize changes in decision-making between unaided and AI-assisted phases. These tools provided a comprehensive understanding of the viewer's practical benefit in real clinical scenarios.

  5 months

Study Arms (2)

Group 1: Evaluation with Medical expertise only

Evaluation of different medical images of people with 5 types of cancer using their own expertise.

Other: Risk in prostate cancer; Other: Life expectancy in lung cancer; Other: Histological subtype; Other: Staging of colon cancer; Other: invasion in rectum cancer

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.

Other: Risk in prostate cancer; Other: Life expectancy in lung cancer; Other: Histological subtype; Other: Staging of colon cancer; Other: invasion in rectum cancer

Interventions

Risk in prostate cancer OTHER

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

Group 1: Evaluation with Medical expertise only; Group 2: Evaluation with the support of AI solutions

Life expectancy in lung cancer OTHER

Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information.

Group 1: Evaluation with Medical expertise only; Group 2: Evaluation with the support of AI solutions

Histological subtype OTHER

An assessment by pathology of the subtype of breast tumour

Group 1: Evaluation with Medical expertise only; Group 2: Evaluation with the support of AI solutions

Staging of colon cancer OTHER

classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region

Group 1: Evaluation with Medical expertise only; Group 2: Evaluation with the support of AI solutions

invasion in rectum cancer OTHER

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 1: Evaluation with Medical expertise only; Group 2: Evaluation with the support of AI solutions

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with a cancer diagnosis of prostate, breast, lung and colorectal from the University and politechnic Hospital la Fe, Valencia.

You may qualify if:

• 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.

You may not qualify if:

• patients with incomplete or low quality data (radiological, pathological or uncomplete clinical data necessary for the ground truth)

Sponsors & Collaborators

• Instituto de Investigacion Sanitaria La Fe: lead
• University of Pisa: collaborator
• University Hospital Rijeka: collaborator
• University of Messina: collaborator
• Istanbul Medipol University Hospital: collaborator
• Centro Hospitalar do Porto: collaborator
• Hospitales Universitarios Virgen del Rocío: collaborator
• IRCCS Policlinico S. Donato: collaborator
• National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos: collaborator
• Charite University, Berlin, Germany: collaborator
• Osakidetza: collaborator
• Le Collège des Enseignants de Radiologie de France: collaborator

Study Sites (1)

Hospital Universitario y Politécnico la Fe

Valencia, 46026, Spain

Location

Related Publications (1)

• 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.

  PMID: 36408543 BACKGROUND

Related Links

• 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

MeSH Terms

Conditions

Breast Neoplasms; Carcinoma, Non-Small-Cell Lung; Colonic Neoplasms; Rectal Neoplasms; Prostatic Neoplasms

Interventions

Risk; Life Expectancy

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Diseases; Respiratory Tract Diseases; Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases; Genital Neoplasms, Male; Urogenital Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Intervention Hierarchy (Ancestors)

Probability; Statistics as Topic; Epidemiologic Methods; Investigative Techniques; Mathematical Concepts; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health; Vital Statistics; Data Collection; Demography; Population Characteristics; Epidemiologic Measurements

Study Design

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

Study Record Dates

• First Submitted: April 11, 2025
• First Posted: April 30, 2025
• Study Start: September 1, 2024
• Primary Completion: October 30, 2024
• Study Completion: November 1, 2024
• Last Updated: April 30, 2025

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)