Gastric Cancer (GC)KITuKoNCT07592338
Agreement Between Large Language Model-Generated Treatment Recommendations With Guideline-Based and Tumor Board Decisions in Gastrointestinal Cancer
An observational study of Gastric Cancer (GC) and Colorectal Cancer, run by Medizinische Hochschule Brandenburg Theodor Fontane at 1 site in Germany.
Trial summary
The goal of this observational study is to learn whether a computer program can suggest cancer treatments that match expert recommendations for people with gastrointestinal cancer (cancer of the pancreas, stomach, or colon and rectum). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastric Cancer (GC), Colorectal Cancer, Pancreatic Cancer
- What is being tested?
- Treatment recommendation according to official German cancer guideline, Treatment recommendation of a LLM and Treatment recommendation of a multidisciplinary tumor board
- How many people
- 30 participants
- Where
- 1 siteGermany
- Study dates
- 1 Jan 2025to 25 Feb 2026
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult patients with gastrointestinal adenocarcinoma treated at a tertiary care academic center in the Federal State of Brandenburg, Germany. The population is derived from routine clinical practice and includes patients whose cases were evaluated in a multidisciplinary tumor board.
Who may be able to join
- Histologically confirmed pancreatic, gastric, or colorectal adenocarcinoma
- Treatment discussed in a multidisciplinary tumor board
Who cannot join
- Non-adenocarcinoma histology
Trial locations and contacts
1 site in 1 country.
Germany1 site
University Hospital Brandenburg
Brandenburg an der Havel, Brandenburg, 14770
About this study
Official title: Concordance of Large Language Model-Generated Treatment Recommendations With Multidisciplinary Tumor Board and Guideline-Based Decisions in Gastrointestinal Cancer: A Retrospective Cohort Study
The goal of this observational study is to learn whether a computer program can suggest cancer treatments that match expert recommendations for people with gastrointestinal cancer (cancer of the pancreas, stomach, or colon and rectum).
The main questions it aims to answer are:
Read more
- Do the treatment suggestions from the computer program match current medical guidelines?
- Do these suggestions match decisions made by a multidisciplinary tumor board (a team of cancer specialists)?
Researchers will review existing medical records from people who have already been treated for these cancers. They will enter key clinical information into a computer program that uses artificial intelligence (AI). The program will generate treatment suggestions for each case.
Researchers will then compare these suggestions with:
- guideline-based treatment recommendations
- decisions made by the tumor board
This study will help researchers understand whether AI tools could support doctors in making cancer treatment decisions in the future.
Read the detailed description
Gastrointestinal cancers require complex treatment planning that often involves surgery, systemic therapy, and multidisciplinary coordination. Clinical decision-making is typically guided by evidence-based recommendations and discussed in multidisciplinary tumor boards. However, the increasing complexity of treatment strategies and guideline frameworks can make consistent and reproducible decision-making challenging in routine clinical practice.
Recent advances in artificial intelligence have enabled the development of large language models (LLMs) that can process structured clinical information and generate text-based recommendations. These systems may offer a scalable approach to support clinical workflows, but their ability to produce reliable and clinically appropriate treatment suggestions in oncology remains uncertain.
This study evaluates the performance of an LLM-based system in the context of gastrointestinal oncology using retrospectively collected clinical case data. Structured case summaries derived from routine clinical documentation are used as standardized input. The model generates treatment recommendations under controlled conditions, allowing systematic comparison with established clinical reference standards.
The analysis focuses on the level of agreement between model-generated recommendations and established decision-making frameworks. In addition, the study explores how model performance varies across different clinical scenarios, including varying levels of disease complexity. Particular attention is given to situations in which recommendations differ, in order to better understand potential limitations of the model and identify patterns that may be clinically relevant.
Furthermore, the study examines the consistency of model outputs when the same clinical information is processed multiple times. This provides insight into the stability and reproducibility of the system, which are important considerations for potential real-world use.
The findings of this study are intended to inform the potential role of LLM-based tools as supportive systems in clinical decision-making. The study does not evaluate clinical outcomes or patient benefit, but instead focuses on agreement with established standards and expert-driven decisions as an initial step in assessing feasibility and safety.
- Conditions
- Gastric Cancer (GC), Colorectal Cancer, Pancreatic Cancer
- Being tested
- Treatment recommendation according to official German cancer guideline, Treatment recommendation of a LLM, Treatment recommendation of a multidisciplinary tumor board
- Keywords
- Colorectal Neoplasms, Stomach Neoplasms, Pancreatic Neoplasms, Artificial Intelligence, Clinical Decision Support Systems
- Medical subject headings
- Stomach Neoplasms, Colorectal Neoplasms, Pancreatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Treatment recommendation according to official German cancer guidelineOther
Detailed treatment recommendation according to the official guideline of the Association of the Scientific Medical Societies in Germany (AWMF; Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften),
Treatment recommendation of a LLMOther
Structured clinical case summaries were analyzed by a GPT-4-class large language model to generate treatment recommendations.
Treatment recommendation of a multidisciplinary tumor boardOther
Detailed treatment recommendation according to the case-specific postoperative tumor board review.
| Study group | Role | What they receive |
|---|---|---|
| Pancreatic cancer | Patients with pancreatic cancer Receives: Other: Treatment recommendation according to official German cancer guideline; Other: Treatment recommendation of a LLM; Other: Treatment recommendation of a multidisciplinary tumor board | |
| Gastric cancer | Patients with gastric cancer Receives: Other: Treatment recommendation according to official German cancer guideline; Other: Treatment recommendation of a LLM; Other: Treatment recommendation of a multidisciplinary tumor board | |
| Colorectal cancer | Patients with colorectal cancer Receives: Other: Treatment recommendation according to official German cancer guideline; Other: Treatment recommendation of a LLM; Other: Treatment recommendation of a multidisciplinary tumor board |
What is being measured
The primary outcome is the main question the study is designed to answer.
Concordance with guideline-based management
Agreement between LLM-generated recommendations and AWMF guideline-supported treatment strategies
Measured: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Secondary outcomes (3)
Concordance with multidisciplinary tumor board decisions
Agreement between LLM-generated recommendations and tumor board treatment strategies
Measured: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Reproducibility of LLM recommendations across repeated runs
Structured clinical case vignettes were entered into ChatGPT using a standardized prompt template. To assess within-model reproducibility, each clinical vignette was analyzed in 3 independent model sessions performed on different days using identical clinical input.
Measured: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Characterization of discordant recommendations (e.g., overtreatment, undertreatment)
Overtreatment was defined as an LLM-generated recommendation exceeding the intensity of the reference recommendation.
Undertreatment was defined as omission of a recommended treatment or recommendation of a less intensive strategy.
Measured: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2026
- Study start1 Jan 2025
- Primary completion1 Jan 2026
- Study completion25 Feb 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer (GC)Studies 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
- 30 participants (actual)
Sponsor and investigators
- Lead sponsor
- Medizinische Hochschule Brandenburg Theodor Fontane
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Rene Mantke, Medizinische Hochschule Brandenburg Theodor Fontane
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07592338
- Sponsor's protocol ID
- KITuKo
- Acronym
- KITuKo
- Status last verified
- May 2026
- First submitted
- 4 May 2026
- Last updated
- 18 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared. The dataset consists of retrospective, pseudonymized clinical data from a single institution, and sharing is restricted due to data protection regulations and institutional policies.
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Concordance of Large Language Model-Generated Treatment Recommendations With Multidisciplinary Tumor Board and Guideline-Based Decisions in Gastrointestinal Cancer: A Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT07592338
- Acronym / sponsor ID
- KITuKo / KITuKo
- Conditions
- Gastric Cancer (GC), Colorectal Cancer, Pancreatic Cancer
- Keywords
- Colorectal Neoplasms, Stomach Neoplasms, Pancreatic Neoplasms, Artificial Intelligence, Clinical Decision Support Systems
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 30 participants (actual)
- Lead sponsor
- Medizinische Hochschule Brandenburg Theodor FontaneUniversity, hospital or other
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 1 Jan 2026 (actual)
- Study completion
- 25 Feb 2026 (actual)
- First posted
- 18 May 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.
The goal of this observational study is to learn whether a computer program can suggest cancer treatments that match expert recommendations for people with gastrointestinal cancer (cancer of the pancreas, stomach, or colon and rectum).
The main questions it aims to answer are:
- Do the treatment suggestions from the computer program match current medical guidelines?
- Do these suggestions match decisions made by a multidisciplinary tumor board (a team of cancer specialists)?
Researchers will review existing medical records from people who have already been treated for these cancers. They will enter key clinical information into a computer program that uses artificial intelligence (AI). The program will generate treatment suggestions for each case.
Researchers will then compare these suggestions with:
- guideline-based treatment recommendations
- decisions made by the tumor board
This study will help researchers understand whether AI tools could support doctors in making cancer treatment decisions in the future.
Detailed description
Gastrointestinal cancers require complex treatment planning that often involves surgery, systemic therapy, and multidisciplinary coordination. Clinical decision-making is typically guided by evidence-based recommendations and discussed in multidisciplinary tumor boards. However, the increasing complexity of treatment strategies and guideline frameworks can make consistent and reproducible decision-making challenging in routine clinical practice.
Recent advances in artificial intelligence have enabled the development of large language models (LLMs) that can process structured clinical information and generate text-based recommendations. These systems may offer a scalable approach to support clinical workflows, but their ability to produce reliable and clinically appropriate treatment suggestions in oncology remains uncertain.
This study evaluates the performance of an LLM-based system in the context of gastrointestinal oncology using retrospectively collected clinical case data. Structured case summaries derived from routine clinical documentation are used as standardized input. The model generates treatment recommendations under controlled conditions, allowing systematic comparison with established clinical reference standards.
The analysis focuses on the level of agreement between model-generated recommendations and established decision-making frameworks. In addition, the study explores how model performance varies across different clinical scenarios, including varying levels of disease complexity. Particular attention is given to situations in which recommendations differ, in order to better understand potential limitations of the model and identify patterns that may be clinically relevant.
Furthermore, the study examines the consistency of model outputs when the same clinical information is processed multiple times. This provides insight into the stability and reproducibility of the system, which are important considerations for potential real-world use.
The findings of this study are intended to inform the potential role of LLM-based tools as supportive systems in clinical decision-making. The study does not evaluate clinical outcomes or patient benefit, but instead focuses on agreement with established standards and expert-driven decisions as an initial step in assessing feasibility and safety.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Pancreatic cancer | – | Other: Treatment recommendation according to official German cancer guideline, Other: Treatment recommendation of a LLM, Other: Treatment recommendation of a multidisciplinary tumor boardPatients with pancreatic cancer |
| Gastric cancer | – | Other: Treatment recommendation according to official German cancer guideline, Other: Treatment recommendation of a LLM, Other: Treatment recommendation of a multidisciplinary tumor boardPatients with gastric cancer |
| Colorectal cancer | – | Other: Treatment recommendation according to official German cancer guideline, Other: Treatment recommendation of a LLM, Other: Treatment recommendation of a multidisciplinary tumor boardPatients with colorectal cancer |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients with gastrointestinal adenocarcinoma treated at a tertiary care academic center in the Federal State of Brandenburg, Germany. The population is derived from routine clinical practice and includes patients whose cases were evaluated in a multidisciplinary tumor board.
Key inclusion criteria (2 listed)
- Histologically confirmed pancreatic, gastric, or colorectal adenocarcinoma
- Treatment discussed in a multidisciplinary tumor board
Key exclusion criteria (1 listed)
- Non-adenocarcinoma histology
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 |
|---|---|---|
| Treatment recommendation according to official German cancer guidelineDetailed treatment recommendation according to the official guideline of the Association of the Scientific Medical Societies in Germany (AWMF; Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften), | Other | Colorectal cancer, Gastric cancer, Pancreatic cancer |
| Treatment recommendation of a LLMStructured clinical case summaries were analyzed by a GPT-4-class large language model to generate treatment recommendations. | Other | Colorectal cancer, Gastric cancer, Pancreatic cancer |
| Treatment recommendation of a multidisciplinary tumor boardDetailed treatment recommendation according to the case-specific postoperative tumor board review. | Other | Colorectal cancer, Gastric cancer, Pancreatic cancer |
Primary outcome
Concordance with guideline-based management
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Agreement between LLM-generated recommendations and AWMF guideline-supported treatment strategies
3 secondary outcomes
Concordance with multidisciplinary tumor board decisions
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Reproducibility of LLM recommendations across repeated runs
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Characterization of discordant recommendations (e.g., overtreatment, undertreatment)
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
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 18 May 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared. The dataset consists of retrospective, pseudonymized clinical data from a single institution, and sharing is restricted due to data protection regulations and institutional policies.
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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 Gastric Cancer (GC) and Colorectal Cancer, run by Medizinische Hochschule Brandenburg Theodor Fontane at 1 site in Germany.
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 2 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 18 May 2026. 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 Germany. 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?
Concordance with guideline-based management (time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery). 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 18 May 2026.