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COMPLETED

NCT07654036

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

Sponsor: Peking University People's Hospital

View on ClinicalTrials.gov

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.

Key Details

Gender

All

Age Range

18 Years - 65 Years

Study Type

INTERVENTIONAL

Enrollment

Start Date

2026-06-10

Completion Date

2026-06-21

Last Updated

2026-07-27

Healthy Volunteers

Conditions

Large Language Models Lung Cancer (NSCLC)

Interventions

OTHER

GAPS-Agent

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

OTHER

LLM

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

Inclusion Criteria: 1. Resident Physician Subjects: 1. Holds a valid and legally effective Physician Practice License of the People's Republic of China; 2. Currently holds the rank of resident physician in a thoracic surgery department at a tertiary Class A (3A) hospital; 3. Agrees to complete all assessment tasks of the main study phase in accordance with the study protocol; 4. Can guarantee the time and effort required to complete all assessment tasks of the main study. 2. Study Cases: 1. The case was discussed at the Thoracic Oncology Multidisciplinary Team (MDT) conference of Peking University People's Hospital between January 2025 and May 2026; 2. The current version of the NCCN guidelines does not provide an explicit recommendation covering the management of the case; 3. Does not overlap with the GAPS evaluation set; 4. The case is presented in pure text in a structured format, with all direct and indirect identifiers removed and complete de-identification performed prior to inclusion; 5. 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 1 - 2 cases per theme. 3. Adjudication Expert Panel: 1. Holds a valid and legally effective Physician Practice License of the People's Republic of China; 2. Currently holds the rank of attending physician or above in a thoracic surgery department at a tertiary Class A hospital; 3. Chairs or regularly participates in lung cancer multidisciplinary team (MDT) work in their department. Exclusion Criteria: 1. Resident Physician Subjects: 1. Has previously participated in the construction of the GAPS evaluation set or the development of GAPS-Agent; 2. Unable to complete the tasks of the study phase. 2. Study Cases: 1. 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; 2. Decision-making for the case is strictly dependent on non-text information. 3. Adjudication Expert Panel: 1. Participated in the construction of the GAPS evaluation set, the content validity verification, or the development of GAPS-Agent for this study; 2. Has a direct conflict of interest with any specific product among the two-arm tools of this study.

Locations (1)

Peking University People's Hospital

Beijing, Beijing Municipality, China