Department of Radiation Oncology, Shandong Cancer Hospital and Institute
Jinan, Shandong, 0531, China
NCT Number: NCT07595107
This prospective single-center observational study will evaluate the concordance between recommendations generated by a locally deployed large language model and standardized multidisciplinary team recommendations for patients with rectal cancer.
Consecutive adult patients with pathologically confirmed rectal adenocarcinoma who are scheduled for routine rectal cancer multidisciplinary team discussion will be enrolled. For each case, investigators will prepare a standardized de-identified clinical summary before the multidisciplinary team meeting. The same summary will be used for large language model generation and routine multidisciplinary team discussion.
The large language model recommendation will not be disclosed to the clinical team and will not influence actual patient management. Concordance between the large language model recommendation and the multidisciplinary team reference recommendation will be assessed using predefined structured rules and blinded expert review.
Trial opening soon.
Get Notified18 year and older
All sexes
Observational
Jinan, Shandong, 0531, China
Management of rectal cancer often requires multidisciplinary decision-making based on tumor location, pelvic magnetic resonance imaging findings, clinical stage, mesorectal fascia or circumferential resection margin status, extramural vascular invasion, lateral lymph node status, metastatic status, previous treatment, surgical feasibility, organ preservation considerations, and patient preferences. Large language models have shown potential in medical information processing and clinical decision support, but their performance in complex oncologic decision-making has not been fully validated.
This study is designed as a prospective, single-center, observational concordance study. Consecutive patients with rectal adenocarcinoma who are scheduled for routine rectal cancer multidisciplinary team discussion at the study center will be screened. Before the multidisciplinary team meeting, investigators will prepare a standardized de-identified case summary using a predefined template. The summary will include relevant demographic information, clinical status, endoscopic findings, pathological and molecular information, key imaging findings, previous treatments, and patient preferences or practical constraints when available.
The same standardized case summary will be used as the input for a locally deployed large language model. A fixed prompt, fixed model version, and fixed inference parameters will be used throughout the study. Each case will be processed in an independent session, without additional interactive prompting or manual correction. The model will not use internet access, external knowledge retrieval, or retrieval-augmented generation during the study.
Routine multidisciplinary team discussion will proceed independently according to standard clinical workflow. The large language model output will not be provided to the multidisciplinary team and will not be used to guide patient treatment. Actual treatment decisions will be made by the treating physicians and multidisciplinary team according to routine clinical practice.
After both recommendations have been generated, the large language model recommendation and the multidisciplinary team recommendation will be transformed into a structured format. The structured recommendations will include the preferred treatment pathway, specific treatment plan, acceptable alternative options, key rationale, and need for additional examinations or information. De-identified and randomly ordered recommendations will then be evaluated using predefined concordance rules and blinded expert review.
The primary objective is to estimate the complete concordance rate between the large language model recommendation and the multidisciplinary team reference recommendation for the preferred treatment pathway. Secondary objectives include evaluation of concordance in specific treatment implementation, acceptable alternative options, identification of additional examinations or information needs, and the rate of major discordance. Exploratory analyses will assess patterns of major discordance and clinical features associated with concordant or discordant recommendations.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
For each enrolled case, a standardized de-identified clinical summary will be entered into a locally deployed large language model using a fixed prompt and fixed inference parameters. The model will generate a structured treatment recommendation for concordance assessment. The large language model output will not be disclosed to the multidisciplinary team and will not influence actual patient management.
Time frame: From enrollment to completion of recommendation adjudication for each case, up to 12 months.
Percentage of enrolled cases in which the preferred treatment pathway recommended by the large language model is completely concordant with the multidisciplinary team reference recommendation, as assessed using a predefined structured concordance adjudication form. The unit of measure is percentage of cases.
Time frame: From enrollment to completion of recommendation adjudication for each case, up to 12 months.
Percentage of cases in which the large language model recommendation and the multidisciplinary team reference recommendation are rated as concordant for the specific treatment implementation plan using a predefined structured concordance adjudication form and blinded expert review. The unit of measure is percentage of cases.
Time frame: From enrollment to completion of recommendation adjudication for each case, up to 12 months.
Percentage of applicable cases in which the large language model recommendation and the multidisciplinary team reference recommendation are rated as concordant regarding acceptable alternative treatment options using a predefined structured concordance adjudication form and blinded expert review. The unit of measure is percentage of applicable cases.
Time frame: From enrollment to completion of recommendation adjudication for each case, up to 12 months.
Percentage of cases in which the large language model recommendation and the multidisciplinary team reference recommendation agree on whether additional examinations, restaging, or key information are needed before treatment decision-making, as assessed using a predefined structured concordance adjudication form. The unit of measure is percentage of cases.
Time frame: From enrollment to completion of recommendation adjudication for each case, up to 12 months.
Percentage of enrolled cases in which differences between the large language model recommendation and the multidisciplinary team reference recommendation are classified as major discordance using a predefined structured adjudication form and blinded expert review. Major discordance is defined as a clinically substantial difference potentially associated with undertreatment, overtreatment, incorrect treatment sequencing, inappropriate organ preservation or local control judgment, or omission of necessary additional evaluation. The unit of measure is percentage of cases.
Time frame: At completion of blinded expert review, up to 12 months.
Inter-rater agreement between independent blinded reviewers for judgment-based endpoints will be assessed using Cohen's kappa coefficient. Judgment-based endpoints include specific treatment implementation concordance, alternative treatment option concordance, major discordance classification, and failure mode classification when applicable. The unit of measure is the kappa coefficient.
Contact information is provided by the study sponsor or research team.
Shandong Cancer Hospital and Institute
Other
A Prospective Single-Center Observational Study Evaluating Concordance Between Large Language Model-Generated Recommendations and Multidisciplinary Team Recommendations in Rectal Cancer
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