An observational study in Gastric Cancer (GC), Colorectal Cancer and Pancreatic Cancer, sponsored by Medizinische Hochschule Brandenburg Theodor Fontane. Completed at 1 site in Germany. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-05-18.
Sponsored by Medizinische Hochschule Brandenburg Theodor Fontane · Observational
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:
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:
This study will help researchers understand whether AI tools could support doctors in making cancer treatment decisions in the future.
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.
2,851 studies on the registry are indexed under Stomach Neoplasms; 864 are open to participants now.
This study's enrollment of 30 is below the median of 274 across 670 observational studies indexed under Stomach Neoplasms.
Browse Stomach Neoplasms studies →Medizinische Hochschule Brandenburg Theodor Fontane is the lead sponsor of 17 studies on the registry; 2 are open to participants now.
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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.
Exclusion Criteria:
Patients with pancreatic cancer
Other: Treatment recommendation according to official German cancer guideline · Other: Treatment recommendation of a LLM · Other: Treatment recommendation of a multidisciplinary tumor board
Patients with gastric cancer
Other: Treatment recommendation according to official German cancer guideline · Other: Treatment recommendation of a LLM · Other: Treatment recommendation of a multidisciplinary tumor board
Patients with colorectal cancer
Other: Treatment recommendation according to official German cancer guideline · Other: Treatment recommendation of a LLM · Other: Treatment recommendation of a multidisciplinary tumor board
Detailed treatment recommendation according to the official guideline of the Association of the Scientific Medical Societies in Germany (AWMF; Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften),
Structured clinical case summaries were analyzed by a GPT-4-class large language model to generate treatment recommendations.
Detailed treatment recommendation according to the case-specific postoperative tumor board review.
Concordance with guideline-based management
Agreement between LLM-generated recommendations and AWMF guideline-supported treatment strategies
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Concordance with multidisciplinary tumor board decisions
Agreement between LLM-generated recommendations and tumor board treatment strategies
Time frame: 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.
Time frame: 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.
Time frame: At the time of multidisciplinary tumor board evaluation up to 4 weeks after surgery
Plan to share: 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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This study is completed, as verified in May 2026. You cannot join it, but the record below documents what was studied.
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Medizinische Hochschule Brandenburg Theodor Fontane