Preliminary Evaluation of a Large Language Model-Based Tool for Complex Surgical Decision Support in Lung Cancer

NCT07654036 | Enrolling By Invitation

• 1 other identifier: 2026PHB458-001
• Study Type: interventional
• Target: 12
• Locations: 1 country
• Sites: 1

Brief Summary

This study is an exploratory effect-size estimation study, with the following specific objectives: ① to estimate the point estimate and 95% confidence interval of the Win Ratio for the experimental group (GAPS-Agent) versus the control group (large language model) in blinded pairwise preference judgments by thoracic surgery expert adjudicators, to serve as a sample size planning parameter for subsequent multicenter confirmatory clinical trials; ② to preliminarily evaluate the value of GAPS-Agent within clinical workflows.The hypothesis of this study is as follows: compared with a general-purpose large language model without medical enhancement (control group), a structured agentic workflow optimized on the basis of the GAPS evaluation framework (GAPS-Agent, experimental group) can help junior resident physicians generate clinical decision plans for complex lung cancer cases that are more strongly preferred by senior thoracic surgery expert adjudicators.

Conditions

Large Language Models; Lung Cancer (NSCLC)

Keywords

Large Language Models; Lung Cancer; Multidisciplinary Team; Benchmark Test; Clinical Trial

Outcome Measures

Primary Outcomes (1)

• Overall plan Win Ratio

  A total of 10 blinded expert judges made Win/Tie/Loss ternary preference judgments on 192 paired scheme comparisons in terms of overall scheme quality. The win ratio was calculated as Wins ÷ Losses, and the 95% confidence interval was estimated using a two-level (physician × case) cluster bootstrap resampling method (B = 10,000, quantile method on the log scale).

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

Secondary Outcomes (11)

• Inter-rater agreement

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

• Redundancy Win Ratio

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

• Evidence-based medicine adherence Win Ratio

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

• Actionability Win Ratio

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

• Completeness Win Ratio

  Measured at the time when experts completed their preference judgements. Calculated up to 3 weeks after the preference judgements.

Study Arms (2)

test arm

EXPERIMENTAL

GAPS-Agent

Other: GAPS-Agent

control arm

ACTIVE COMPARATOR

LLM

Other: LLM

Interventions

GAPS-Agent OTHER

The research group has previously developed the GAPS evaluation framework for complex clinical decision-making in lung cancer. In this framework, G (Grounding) characterizes the cognitive depth of decision-making (ranging from knowledge retrieval to decisions that go beyond clinical guidelines), A (Authority) corresponds to the grading of evidence strength, P (Perturbation) describes the identification and management of real-world clinical confounding factors, and S (Strength) corresponds to the calibration of recommendation strength. Within this framework, the research group has completed the construction of a 100-item complex lung cancer decision-making evaluation set along with its corresponding rubrics, and has invited multiple thoracic oncology experts to complete content validity validation. Based on this, the research group developed GAPS-Agent, which uses an open-source large language model as its foundation and integrates functional modules such as guideline and evidence retri

test arm

LLM OTHER

Open source large language model that is not specifically enhanced in medical field.

control arm

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Resident Physician Subjects:
• Holds a valid and legally effective Physician Practice License of the People's Republic of China;
• Currently holds the rank of resident physician in a thoracic surgery department at a tertiary Class A (3A) hospital;
• Agrees to complete all assessment tasks of the main study phase in accordance with the study protocol;
• Can guarantee the time and effort required to complete all assessment tasks of the main study.
• Study Cases:
• The case was discussed at the Thoracic Oncology Multidisciplinary Team (MDT) conference of Peking University People's Hospital between January 2025 and May 2026;
• The current version of the NCCN guidelines does not provide an explicit recommendation covering the management of the case;
• Does not overlap with the GAPS evaluation set;
• From the pool of eligible cases, 12 cases will be randomly drawn using Python (numpy.random, with a fixed and archived seed) to serve as the main study cases. The cases will cover 6 themes (chest mass of undetermined diagnosis, early-stage lung cancer, locally advanced lung cancer, oligometastatic/oligoprogressive disease, special intraoperative situations, and tumor recurrence), with 2 cases per theme.
• Adjudication Expert Panel:
• Holds a valid and legally effective Physician Practice License of the People's Republic of China;
• Currently holds the rank of attending physician or above in a thoracic surgery department at a tertiary Class A hospital;
• Chairs or regularly participates in lung cancer multidisciplinary team (MDT) work in their department.

You may not qualify if:

• Resident Physician Subjects:
• Has previously participated in the construction of the GAPS evaluation set or the development of GAPS-Agent;
• Unable to complete the tasks of the study phase.
• Study Cases:
• Key case information is missing, such as text-form data on pathology (including IHC/NGS), imaging, laboratory tests, prior medical history, comorbidities, or PS score;
• Decision-making for the case is strictly dependent on non-text information.
• Adjudication Expert Panel:
• Participated in the construction of the GAPS evaluation set, the content validity verification, or the development of GAPS-Agent for this study;
• Has a direct conflict of interest with any specific product among the two-arm tools of this study.

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, 100044, China

Location

MeSH Terms

Conditions

Lung Neoplasms; Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Carcinoma, Bronchogenic; Bronchial Neoplasms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chief Physician

Study Record Dates

• First Submitted: June 10, 2026
• First Posted: June 17, 2026
• Study Start: June 10, 2026
• Primary Completion (Estimated): June 21, 2026
• Study Completion (Estimated): June 21, 2026
• Last Updated: June 17, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)