Concordance between a Large Language Model and a Multidisciplinary Team for Rectal Cancer
Official title: A Prospective Single-Center Observational Study Evaluating Concordance Between Large Language Model-Generated Recommendations and Multidisciplinary Team Recommendations in Rectal Cancer
Run by Shandong Cancer Hospital and Institute · NCT07595107 · Data updated 2026-11-12
A large language model will generate treatment recommendations for rectal cancer patients, and researchers will compare those to the real multidisciplinary team recommendations to see how closely they match.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 180 (estimated) |
| Sponsor | Shandong Cancer Hospital and Institute Academic / other |
| Locations | 1 site (Jinan, Shandong) |
| Status | Recruiting |
| Started | June 1, 2026 |
| Main results expected | June 1, 2027 |
| Study ends | December 1, 2027 |
| Registry record updated | October 15, 2026 |
| Trial ID | NCT07595107 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (18+) with pathologically confirmed rectal adenocarcinoma who are scheduled for routine MDT discussion and have complete standardized decision-making information available.
✕Probably not a fit
Patients with incomplete data, unusual cases, or cases where MDT decisions are highly individualized may not benefit from AI concordance.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a prospective, single-center observational study involving adults with pathologically confirmed rectal adenocarcinoma who are scheduled for a standard multidisciplinary team discussion. For each case, a de-identified clinical summary is prepared and used both by the local large language model and by the actual MDT, with the LLM’s recommendation kept hidden from clinicians. Concordance will be measured using predefined rules and blinded expert review. The study aims to assess the potential role of AI-based decision support in complex rectal cancer management. Management considerations include tumor location, MRI findings, staging, margins, invasion, nodal status, prior treatments, feasibility of surgery, organ preservation, and patient preferences.
Why it matters
Potential benefit: If the LLM concordance is high, AI-supported tools could help streamline MDT workflows and support decision-making in rectal cancer care.
How similar studies have gone: Prior work shows AI tools can process clinical information; however, performance in complex oncologic decision-making like rectal cancer remains to be validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Age 18 years or older. 2. Pathologically confirmed rectal adenocarcinoma. 3. Scheduled for routine rectal cancer multidisciplinary team discussion at the study center. 4. Availability of complete or substantially complete standardized decision-making information before multidisciplinary team discussion, including clinical, pathological, and key imaging information. 5. Availability of a structured pelvic magnetic resonance imaging report meeting the requirements of the institutional rectal cancer multidisciplinary team, including at least tumor location, clinical T stage, clinical N stage, circumferential resection margin or mesorectal fascia status, extramural vascular invasion status, and lateral lymph node status. 6. Presence of a defined clinical treatment decision question. 7. Clinical data can be sufficiently de-identified for research use. Exclusion Criteria: 1. Severely incomplete clinical information preventing preparation of a standardized case summary. 2. Non-rectal primary tumor. 3. Routine follow-up cases without a defined treatment decision question. 4. Absence of a structured pelvic magnetic resonance imaging report meeting the requirements of the institutional rectal cancer multidisciplinary team, or missing pelvic magnetic resonance imaging elements that preclude key rectal cancer decision-making. 5. Cases containing sensitive information considered unsuitable for large language model input by the study team. 6. Cases in which a definitive treatment decision has already been made before the multidisciplinary team discussion and the meeting serves only as a formal review.
Where it's running
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Department of Radiation Oncology, Shandong Cancer Hospital and Institute
Jinan, Shandong, China Recruiting
Study contacts
- Principal investigator: Jinbo Yue, MD, PhD — Shandong Cancer Hospital and Institute
- Study coordinator: Jinbo Yue, MD, PhD
- Email: jbyue@sdfmu.edu.cn
- Phone: 053167626929
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.