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.

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.

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.

The study population will consist of consecutive adult patients with pathologically confirmed rectal adenocarcinoma who are scheduled for routine rectal cancer multidisciplinary team discussion at Shandong Cancer Hospital and Institute. Eligible cases must have sufficient clinical, pathological, and imaging information to allow preparation of a standardized de-identified case summary for concordance assessment between large language model-generated recommendations and multidisciplinary team reference recommendations.