Breast CancerNCT06950996
A Clinical Evaluation of AI Solutions Developed in the CHAIMELEON Project for Cancer: Prostate, Lung, Breast, Colon and Rectum
An observational study of Breast Cancer and Lung Cancer, Non-Small Cell, run by Instituto de Investigacion Sanitaria La Fe at 1 site in Spain.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Breast Cancer, Lung Cancer, Non-Small Cell, Colon Cancer
- What is being tested?
- Risk in prostate cancer, Life expectancy in lung cancer and Histological subtype
- How many people
- 300 participants
- Where
- 1 siteSpain
- Study dates
- 1 Sep 2024to 1 Nov 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with a cancer diagnosis of prostate, breast, lung and colorectal from the University and politechnic Hospital la Fe, Valencia.
Who may be able to join
- 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.
Who cannot join
- patients with incomplete or low quality data (radiological, pathological or uncomplete clinical data necessary for the ground truth)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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).
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:
Read more
- 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.
Read the detailed description
In order to conduct a robust clinical validation, the investigators have designed a study on the required sample size. The study is design to evaluate the role of an AI-assisted tool as a support for improving the daily clinical work. The investigators used an online website (https://statulator.com/SampleSize/ss2PP.html) for the calculation and use the "paired binary proportions" option. Using the case of prostate cancer, the investigators want to compare the probability of correct risk classification in prostate cancer by clinicians alone and/or guided by AI. The study will have a significance (α) = 0.05; power (β) = 80%; the analysis will be "two sided" and with equal group sizes.
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.
- Being tested
- Risk in prostate cancer, Life expectancy in lung cancer, Histological subtype, Staging of colon cancer, invasion in rectum cancer
- Keywords
- Clinical validation, AI model solutions, experimental dedicated-platform
- Medical subject headings
- Breast Neoplasms, Carcinoma, Non-Small-Cell Lung, Colonic Neoplasms, Rectal Neoplasms, Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk in prostate cancerOther
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 cancerOther
Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information.
Histological subtypeOther
An assessment by pathology of the subtype of breast tumour
Staging of colon cancerOther
classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region
invasion in rectum cancerOther
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
| Study group | Role | What they receive |
|---|---|---|
| Group 1: Evaluation with Medical expertise only | Evaluation of different medical images of people with 5 types of cancer using their own expertise. Receives: 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. Receives: Other: Risk in prostate cancer; Other: Life expectancy in lung cancer; Other: Histological subtype; Other: Staging of colon cancer; Other: invasion in rectum cancer |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 5 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Apr 2025
- Study start1 Sep 2024
- Primary completion30 Oct 2024
- Study completion1 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 300 participants (actual)
Sponsor and investigators
- Lead sponsor
- Instituto de Investigacion Sanitaria La Fe
- Sponsor type
- University, hospital or other
- Collaborators
- University of Pisa, University Hospital Rijeka, University of Messina, Istanbul Medipol University Hospital, Centro Hospitalar do Porto, Hospitales Universitarios Virgen del Rocío, IRCCS Policlinico S. Donato, National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos, Charite University, Berlin, Germany, Osakidetza, Le Collège des Enseignants de Radiologie de France
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06950996
- Sponsor's protocol ID
- CHAIMELEON insilico validation
- Other IDs
- 952172
- Status last verified
- Sep 2024
- First submitted
- 11 Apr 2025
- Last updated
- 30 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 36408543Galiana-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.
PubMed 42536277
Study at a glance
From the registry record, last updated by the sponsor on 30 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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).
- ClinicalTrials.gov ID
- NCT06950996
- Acronym / sponsor ID
- CHAIMELEON insilico validation
- Keywords
- Clinical validation, AI model solutions, experimental dedicated-platform
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Instituto de Investigacion Sanitaria La FeUniversity, hospital or other
- Collaborators
- University of Pisa, University Hospital Rijeka, University of Messina, Istanbul Medipol University Hospital, Centro Hospitalar do Porto, Hospitales Universitarios Virgen del Rocío, IRCCS Policlinico S. Donato, National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos, Charite University, Berlin, Germany, Osakidetza, Le Collège des Enseignants de Radiologie de France
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 30 Oct 2024 (actual)
- Study completion
- 1 Nov 2024 (actual)
- First posted
- 30 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
In order to conduct a robust clinical validation, the investigators have designed a study on the required sample size. The study is design to evaluate the role of an AI-assisted tool as a support for improving the daily clinical work. The investigators used an online website (https://statulator.com/SampleSize/ss2PP.html) for the calculation and use the "paired binary proportions" option. Using the case of prostate cancer, the investigators want to compare the probability of correct risk classification in prostate cancer by clinicians alone and/or guided by AI. The study will have a significance (α) = 0.05; power (β) = 80%; the analysis will be "two sided" and with equal group sizes.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1: Evaluation with Medical expertise only | – | Other: Risk in prostate cancer, Other: Life expectancy in lung cancer, Other: Histological subtype, Other: Staging of colon cancer, Other: invasion in rectum cancerEvaluation of different medical images of people with 5 types of cancer using their own expertise. |
| Group 2: Evaluation with the support of AI solutions | – | Other: Risk in prostate cancer, Other: Life expectancy in lung cancer, Other: Histological subtype, Other: Staging of colon cancer, Other: invasion in rectum cancerEvaluation of different medical images of people with 5 types of cancer guided by the AI solutions developed. |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with a cancer diagnosis of prostate, breast, lung and colorectal from the University and politechnic Hospital la Fe, Valencia.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- patients with incomplete or low quality data (radiological, pathological or uncomplete clinical data necessary for the ground truth)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk in prostate cancerthe 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 | Other | Group 1: Evaluation with Medical expertise only, Group 2: Evaluation with the support of AI solutions |
| Life expectancy in lung cancerClinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information. | Other | Group 1: Evaluation with Medical expertise only, Group 2: Evaluation with the support of AI solutions |
| Histological subtypeAn assessment by pathology of the subtype of breast tumour | Other | Group 1: Evaluation with Medical expertise only, Group 2: Evaluation with the support of AI solutions |
| Staging of colon cancerclassify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region | Other | Group 1: Evaluation with Medical expertise only, Group 2: Evaluation with the support of AI solutions |
| invasion in rectum cancerassess whether vascular extramural o mesorectal fascia has been invaded in the tumour using magnetic resonance medical images taken at diagnosis in the pelvic region | Other | Group 1: Evaluation with Medical expertise only, Group 2: Evaluation with the support of AI solutions |
Primary outcome
Usability of experimental viewer with AI tools
Time frame: 5 months
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.
Utility of experimental medical images viewer
Time frame: 5 months
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Apr 2025.
- Background: PubMed 36408543Yilmaz 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.
- Derived from this study: PubMed 42536277Galiana-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.
- 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 validationLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer and Lung Cancer, Non-Small Cell, run by Instituto de Investigacion Sanitaria La Fe at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Usability of experimental viewer with AI tools (time frame: 5 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06950996, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Apr 2025.