Breast NeoplasmsBEACONNCT07739121
Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations
An observational study of Breast Neoplasms and Lung Neoplasms, run by Assistance Publique - Hôpitaux de Paris at 5 sites in France.
Trial summary
BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark comparing the treatment recommendations of six frontier large language models (LLMs) with those of multidisciplinary tumour boards (RCP) in oncology treatment planning. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Breast Neoplasms, Lung Neoplasms, Urologic Neoplasms
- What is being tested?
- Multidisciplinary tumour boards and Frontier large language models
- How many people
- 100 participants
- Where
- 5 sitesFrance
- Study dates
- 1 May 2026to 31 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 100 synthetic oncology treatment-planning cases (20 per localisation) across five localisations: breast, lung, urological (prostate, bladder / upper-tract urothelial, kidney), digestive and gynaecological. Each case is a structured input specifying UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question. No human participants, no patient data and no identifiable individuals. Recommendations are produced by two independent tumour boards per localisation, eight distinct boards in total, and by six frontier LLMs queried in September and October 2026.
Who may be able to join
- Synthetic oncology case within one of the five predefined localisations (breast, lung, urological, digestive, gynaecological).
- Complete structured schema: UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question.
- A clinically answerable treatment-planning question that is mappable to the locked guideline matrix.
Who cannot join
- Case outside the five predefined localisations.
- Incomplete, internally inconsistent or ambiguous schema.
- Duplicate or near-duplicate of an existing case in the set.
- Question not resolvable by current guidelines.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
France5 sites, 5 recruiting
- Not yet recruiting
Hôpital Ambroise Paré AP-HP
Boulogne-Billancourt
Etienne Giroux Leprieur, 01 49 09 58 16 ext. +33, etienne.giroux-leprieur@aphp.fr
- Recruiting
Hopital Européen Georges Pompidou
Paris
Arnaud Méjean, MD PhD, 01 56 09 56 51 ext. +33, arnaud.mejean@aphp.fr
Anne-Sophie Bats, 01 56 09 35 67 ext. +33, anne-sophie.bats@aphp.fr
- Not yet recruiting
Hopital Tenon
Paris
Olivier Traxer, 01 56 01 65 00 ext. +33, olivier.traxer@aphp.fr
Eleonor Rivin del Campo, 01 56 01 62 10 ext. +33, eleonor.rivindelcampo@aphp.fr
- Not yet recruiting
Hôpital Cochin AP-HP
Paris
Marie Wislez, 01 56 09 34 73 ext. +33, marie.wislez@aphp.fr
Romain Coriat, 01 58 41 19 85 ext. +33, romain.coriat@aphp.fr
- Not yet recruiting
Hôpital Saint Antoine AP-HP
Paris
Thierry André, 01 49 28 23 36 ext. +33, thierry.andre@aphp.fr
About this study
Official title: Benchmarking AI for Clinical Oncology decisioNmaking (BEACON): A Prospective, Multicentre, Blinded Evaluation of Frontier Large Language Models Against Multidisciplinary Tumour Board Recommendations in Oncology Treatment Planning
BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark comparing the treatment recommendations of six frontier large language models (LLMs) with those of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to six frontier LLMs. Every recommendation, human or model, is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy with its line, work-up and biomarkers). For each domain, concordance between two recommendations is scored 2 when the same options are selected, 1 when at least one option is shared, and 0 when none is shared; the score is computed automatically from the recommendation forms, without human scoring. The primary endpoint is the mean domain score between each LLM and each tumour board, read against the same score computed between the two tumour boards of the same case, so that humans and models are measured by an identical rule and no respondent contributes to its own reference. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Where the two boards select different options, the case-domain is classified EQUIPOISE and, like every discordance, is adjudicated by an independent panel blinded to the source of the recommendation. Tumour boards record their recommendations during the meeting itself, on a dedicated platform (https://etude-beacon.fr) that reveals a case only once it is presented and time-stamps that reveal, so that no board can read a case in advance. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, scoring algorithms, verbatim prompts and raw model outputs are released for full reproducibility.
Read the detailed description
BEACON is a prospective, multicentre, blinded benchmark using automated, criteria-based scoring. It is built on three design decisions that distinguish it from the existing literature: (i) synthetic, standardised cases remove the record-completeness variability that confounds retrospective comparisons and allow the identical input to be given to every board and every model; (ii) two independent tumour boards per localisation let human-to-human agreement be measured rather than assumed, and that agreement is the level against which model performance is read; and (iii) an independent panel, blinded to the source, adjudicates every discordance and every case-domain where the two boards diverge, so that clinically equivalent options are recognised as such and dangerous ones are named.
Reference standard. For each case-domain, where the two boards select the same options their agreement is the reference; where they differ, the case-domain is classified EQUIPOISE, analysed separately and adjudicated blind.
Five decision domains. Every recommendation is decomposed into D1 Intent, D2 Surgery, D3 Radiotherapy, D4 Systemic therapy (class and line), and D5 Work-up and biomarkers before any comparison. Deliberate silence on a domain is ticked on the form as an explicit category and is scored as a recommendation, never as missing data.
Model selection. Models are chosen by a rule fixed before the first call: for each organisation, the most capable text model its API exposes on the freeze date, whether stable or in preview.
Data collection. Tumour boards record their recommendations during the meeting itself, on a dedicated platform (https://etude-beacon.fr). Each board receives a single access link, with no account to create. Outside a session the platform shows case numbers only; a case is revealed when the board presents it, and that moment is time-stamped. The recommendation form must be saved before the session closes, failing which the case cannot be opened again. Case text cannot be selected or copied and carries a watermark with the board name and the time of display. At closure the coordinator attests that the rules of the study were followed during the session. Session opening times, case reveal times, intervals to submission and attestations are exported with the data.
- Conditions
- Breast Neoplasms, Lung Neoplasms, Urologic Neoplasms, Prostatic Neoplasms, Urinary Bladder Neoplasms, Kidney Neoplasms
- Being tested
- Multidisciplinary tumour boards, Frontier large language models
- Keywords
- Large Language Models, Artificial Intelligence, Clinical Decision support, Decision Support Systems, Clinical, Multidisciplinary tumour board (RCP), Patient Care Team, Medical oncology, Neoplasms, Benchmark, Benchmarking, Concordance, Weighted kappa, Synthetic data, Patient safety, Reproductibility
- Medical subject headings
- Breast Neoplasms, Lung Neoplasms, Urologic Neoplasms, Prostatic Neoplasms, Urinary Bladder Neoplasms, Kidney Neoplasms, Digestive System Neoplasms, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multidisciplinary tumour boardsOther
Two independent tumour boards per localisation, eight distinct boards in total, issue a categorical recommendation for every synthetic case during the board meeting itself, on a dedicated platform (https://etude-beacon.fr) that reveals a case only once it is presented and time-stamps that reveal. A case opened during a session must be completed before that session closes, and each board presents all of its cases within one month of its first session. Where both boards select the same options, their agreement defines the reference; where they differ, the case-domain is classified EQUIPOISE and analysed separately.
Frontier large language modelsOther
Six frontier LLMs receive the identical structured input for every case, under locked and hashed prompts, versions and settings: GPT-6 Astra (gpt-6-astra, OpenAI), Claude Fable 5.1 (claude-fable-5-1, Anthropic), Gemini 3.1 Pro (gemini-3.1-pro-preview, Google DeepMind), Muse Spark 1.3 (muse-spark-1.3, Meta), Mistral Large 3 (mistral-large-2512, Mistral AI, open weights) and Kimi K3 (kimi-k3, Moonshot AI, open weights). Each case is submitted five times per model in independent sessions at temperature 1.0, with one additional pass at temperature 0 as a sensitivity analysis, except for Kimi K3 whose provider fixes temperature at 1.0 and rejects the parameter. No tools, no web access, no retrieval, and no context carried from one call to the next.
| Study group | Role | What they receive |
|---|---|---|
| Breast cancers | Not described Receives: Other: Multidisciplinary tumour boards; Other: Frontier large language models | |
| Lung cancers | Not described Receives: Other: Multidisciplinary tumour boards; Other: Frontier large language models | |
| Urological cancers | Not described Receives: Other: Multidisciplinary tumour boards; Other: Frontier large language models | |
| Digestive cancers | Not described Receives: Other: Multidisciplinary tumour boards; Other: Frontier large language models | |
| Gynaecological cancers | Not described Receives: Other: Multidisciplinary tumour boards; Other: Frontier large language models |
What is being measured
The primary outcome is the main question the study is designed to answer.
Domain-level concordance between LLM recommendations and tumour board recommendations
For each of the five decision domains, concordance between a model recommendation and a tumour board recommendation is scored 2 when the same options are selected (shared deliberate silence included), 1 when at least one option is shared, and 0 when none is shared. The measure is the mean domain score, reported with the proportion of domains scored 2, and is read against the same measure computed between the two tumour boards of the same case.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Proportion of recommendations carrying serious harm potential (Tier 2)
Each recommendation, from a model or from a tumour board, is rated on a three-tier harm scale by an independent panel blinded to its source, two raters per form and a third in case of disagreement. The measure is the proportion of recommendations rated Tier 2, that is serious harm or loss of chance.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Secondary outcomes (9)
Inter-tumour board domain-level concordance (human baseline)
The same 0/1/2 domain score computed between the two independent tumour boards of each case. It sets the human level against which the primary endpoint is read.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Equipoise rate
Proportion of case-domains where the two tumour boards select different options.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Chance-corrected agreement (Gwet's AC1)
Agreement on recommendation categories corrected for chance, computed between the two tumour boards and between each model and each board. AC1 is used in preference to Cohen's kappa because the distribution of recommendations is markedly unbalanced, a situation in which kappa collapses while observed agreement stays high.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Agreement with at least one tumour board
Proportion of case-domains where the model's selection matches the selection of at least one of the two boards, including case-domains classified EQUIPOISE.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Completeness
Proportion of required domains addressed (LLM and tumour boards).
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Missingness
Proportion of critical omissions (LLM and tumour boards)
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Intensity bias
Proportion of recommendation corresponding to over- or under-treatment
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Intra-model consistency
Agreement between the five independent sessions of the same model on the same case.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Proportion of unusable model responses
Proportion of calls returning a refusal, an unparsable response, or a response violating the coherence rules that the platform applies to tumour boards. Those rules are not applied to the models: their violations are measured, not corrected.
Measured: Assessed once at central scoring, after data collection (estimated January 2027)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Jul 2026
- Study start1 May 2026
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast NeoplasmsStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07739121
- Sponsor's protocol ID
- APHP261032
- Acronym
- BEACON
- Status last verified
- Jul 2026
- First submitted
- 28 Jul 2026
- Last updated
- 7 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All 100 synthetic cases, the recommendation form, the verbatim prompts with their SHA-256 hashes, the raw model outputs including refusals and unparsable responses, the pseudonymised tumour board forms, the adjudication ratings and the analysis code are deposited under an open licence with the publication. No individual participant data in the usual sense exists: the cases are synthetic and the only human data collected concerns professionals and their centres.
Study at a glance
From the registry record, last updated by the sponsor on 7 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Benchmarking AI for Clinical Oncology decisioNmaking (BEACON): A Prospective, Multicentre, Blinded Evaluation of Frontier Large Language Models Against Multidisciplinary Tumour Board Recommendations in Oncology Treatment Planning
- ClinicalTrials.gov ID
- NCT07739121
- Acronym / sponsor ID
- BEACON / APHP261032
- Conditions
- Breast Neoplasms, Lung Neoplasms, Urologic Neoplasms, Prostatic Neoplasms, Urinary Bladder Neoplasms, Kidney Neoplasms
- Keywords
- Large Language Models, Artificial Intelligence, Clinical Decision support, Decision Support Systems, Clinical, Multidisciplinary tumour board (RCP), Patient Care Team, Medical oncology, Neoplasms
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Study start
- 1 May 2026 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 31 Jul 2026 (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.
BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark comparing the treatment recommendations of six frontier large language models (LLMs) with those of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to six frontier LLMs. Every recommendation, human or model, is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy with its line, work-up and biomarkers). For each domain, concordance between two recommendations is scored 2 when the same options are selected, 1 when at least one option is shared, and 0 when none is shared; the score is computed automatically from the recommendation forms, without human scoring. The primary endpoint is the mean domain score between each LLM and each tumour board, read against the same score computed between the two tumour boards of the same case, so that humans and models are measured by an identical rule and no respondent contributes to its own reference. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Where the two boards select different options, the case-domain is classified EQUIPOISE and, like every discordance, is adjudicated by an independent panel blinded to the source of the recommendation. Tumour boards record their recommendations during the meeting itself, on a dedicated platform (https://etude-beacon.fr) that reveals a case only once it is presented and time-stamps that reveal, so that no board can read a case in advance. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, scoring algorithms, verbatim prompts and raw model outputs are released for full reproducibility.
Detailed description
BEACON is a prospective, multicentre, blinded benchmark using automated, criteria-based scoring. It is built on three design decisions that distinguish it from the existing literature: (i) synthetic, standardised cases remove the record-completeness variability that confounds retrospective comparisons and allow the identical input to be given to every board and every model; (ii) two independent tumour boards per localisation let human-to-human agreement be measured rather than assumed, and that agreement is the level against which model performance is read; and (iii) an independent panel, blinded to the source, adjudicates every discordance and every case-domain where the two boards diverge, so that clinically equivalent options are recognised as such and dangerous ones are named.
Reference standard. For each case-domain, where the two boards select the same options their agreement is the reference; where they differ, the case-domain is classified EQUIPOISE, analysed separately and adjudicated blind.
Five decision domains. Every recommendation is decomposed into D1 Intent, D2 Surgery, D3 Radiotherapy, D4 Systemic therapy (class and line), and D5 Work-up and biomarkers before any comparison. Deliberate silence on a domain is ticked on the form as an explicit category and is scored as a recommendation, never as missing data.
Model selection. Models are chosen by a rule fixed before the first call: for each organisation, the most capable text model its API exposes on the freeze date, whether stable or in preview.
Data collection. Tumour boards record their recommendations during the meeting itself, on a dedicated platform (https://etude-beacon.fr). Each board receives a single access link, with no account to create. Outside a session the platform shows case numbers only; a case is revealed when the board presents it, and that moment is time-stamped. The recommendation form must be saved before the session closes, failing which the case cannot be opened again. Case text cannot be selected or copied and carries a watermark with the board name and the time of display. At closure the coordinator attests that the rules of the study were followed during the session. Session opening times, case reveal times, intervals to submission and attestations are exported with the data.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Breast cancers | – | Other: Multidisciplinary tumour boards, Other: Frontier large language models |
| Lung cancers | – | Other: Multidisciplinary tumour boards, Other: Frontier large language models |
| Urological cancers | – | Other: Multidisciplinary tumour boards, Other: Frontier large language models |
| Digestive cancers | – | Other: Multidisciplinary tumour boards, Other: Frontier large language models |
| Gynaecological cancers | – | Other: Multidisciplinary tumour boards, Other: Frontier large language models |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 100 synthetic oncology treatment-planning cases (20 per localisation) across five localisations: breast, lung, urological (prostate, bladder / upper-tract urothelial, kidney), digestive and gynaecological. Each case is a structured input specifying UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question. No human participants, no patient data and no identifiable individuals. Recommendations are produced by two independent tumour boards per localisation, eight distinct boards in total, and by six frontier LLMs queried in September and October 2026.
Key inclusion criteria (3 listed)
- Synthetic oncology case within one of the five predefined localisations (breast, lung, urological, digestive, gynaecological).
- Complete structured schema: UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question.
- A clinically answerable treatment-planning question that is mappable to the locked guideline matrix.
Key exclusion criteria (4 listed)
- Case outside the five predefined localisations.
- Incomplete, internally inconsistent or ambiguous schema.
- Duplicate or near-duplicate of an existing case in the set.
- Question not resolvable by current guidelines.
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 |
|---|---|---|
| Multidisciplinary tumour boardsTwo independent tumour boards per localisation, eight distinct boards in total, issue a categorical recommendation for every synthetic case during the board meeting itself, on a dedicated platform (https://etude-beacon.fr) that reveals a case only once it is presented and time-stamps that reveal. A case opened during a session must be completed before that session closes, and each board presents all of its cases within one month of its first session. Where both boards select the same options, their agreement defines the reference; where they differ, the case-domain is classified EQUIPOISE and analysed separately. | Other | Breast cancers, Digestive cancers, Gynaecological cancers, Lung cancers, Urological cancers |
| Frontier large language modelsSix frontier LLMs receive the identical structured input for every case, under locked and hashed prompts, versions and settings: GPT-6 Astra (gpt-6-astra, OpenAI), Claude Fable 5.1 (claude-fable-5-1, Anthropic), Gemini 3.1 Pro (gemini-3.1-pro-preview, Google DeepMind), Muse Spark 1.3 (muse-spark-1.3, Meta), Mistral Large 3 (mistral-large-2512, Mistral AI, open weights) and Kimi K3 (kimi-k3, Moonshot AI, open weights). Each case is submitted five times per model in independent sessions at temperature 1.0, with one additional pass at temperature 0 as a sensitivity analysis, except for Kimi K3 whose provider fixes temperature at 1.0 and rejects the parameter. No tools, no web access, no retrieval, and no context carried from one call to the next. | Other | Breast cancers, Digestive cancers, Gynaecological cancers, Lung cancers, Urological cancers |
Primary outcome
Domain-level concordance between LLM recommendations and tumour board recommendations
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
For each of the five decision domains, concordance between a model recommendation and a tumour board recommendation is scored 2 when the same options are selected (shared deliberate silence included), 1 when at least one option is shared, and 0 when none is shared. The measure is the mean domain score, reported with the proportion of domains scored 2, and is read against the same measure computed between the two tumour boards of the same case.
Proportion of recommendations carrying serious harm potential (Tier 2)
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Each recommendation, from a model or from a tumour board, is rated on a three-tier harm scale by an independent panel blinded to its source, two raters per form and a third in case of disagreement. The measure is the proportion of recommendations rated Tier 2, that is serious harm or loss of chance.
9 secondary outcomes
Inter-tumour board domain-level concordance (human baseline)
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Equipoise rate
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Chance-corrected agreement (Gwet's AC1)
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Agreement with at least one tumour board
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Completeness
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Missingness
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Intensity bias
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Intra-model consistency
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Proportion of unusable model responses
Time frame: Assessed once at central scoring, after data collection (estimated January 2027)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Oct 2026.
- Data collection platform used by the participating tumour boards. Access is restricted to the participating boards; the synthetic cases are not publicly accessible before publication.Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. All 100 synthetic cases, the recommendation form, the verbatim prompts with their SHA-256 hashes, the raw model outputs including refusals and unparsable responses, the pseudonymised tumour board forms, the adjudication ratings and the analysis code are deposited under an open licence with the publication. No individual participant data in the usual sense exists: the cases are synthetic and the only human data collected concerns professionals and their centres.
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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.
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Neoplasms and Lung Neoplasms, run by Assistance Publique - Hôpitaux de Paris at 5 sites in France.
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 4 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: looking for participants now, as of the sponsor's last update on 7 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in France. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jean-Emmanuel Bibault, MD PhD (01 56 09 34 06 ext. +33, jean-emmanuel.bibault@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Domain-level concordance between LLM recommendations and tumour board recommendations (time frame: Assessed once at central scoring, after data collection (estimated January 2027)). 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?
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 7 Oct 2026.