NCT07626736

Brief Summary

The goal of this clinical trial is to evaluate the effectiveness and safety of a locally deployed artificial intelligence (AI) decision-support model in the multidisciplinary team (MDT) process for patients with non-small cell lung cancer (NSCLC). The main questions it aims to answer : What is the level of agreement between treatment recommendations generated by the AI model and those made by a traditional MDT? How often do clinicians modify their final treatment decision after reviewing the AI model's recommendation? Researchers will compare treatment plans from the traditional MDT (Arm 1), the AI model (Arm 2), and the clinician's final decision after reviewing the AI output (Arm 3) to assess consistency, decision modification rates, and clinical efficiency. Participants will: Have their clinical, imaging, and molecular data submitted to both the traditional MDT and the AI model for independent treatment recommendations Receive a final treatment plan determined by clinicians after reviewing both recommendations, with follow-up for safety and survival outcomes

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
300

participants targeted

Target at P75+ for not_applicable nonsmall-cell-lung-cancer

Timeline
31mo left

Started Dec 2025

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress17%
Dec 2025Dec 2028

Study Start

First participant enrolled

December 1, 2025

Completed
4 months until next milestone

First Submitted

Initial submission to the registry

March 25, 2026

Completed
2 months until next milestone

First Posted

Study publicly available on registry

June 4, 2026

Completed
1.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

October 31, 2027

Expected
1.2 years until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2028

Last Updated

June 4, 2026

Status Verified

May 1, 2026

Enrollment Period

1.9 years

First QC Date

March 25, 2026

Last Update Submit

May 31, 2026

Conditions

Nonsmall Cell Lung Cancer

Keywords

Non-Small Cell Lung CancerMultidisciplinary TeamLocally Deployed AI ModelLarge Language ModelTreatment Decision-Making

Outcome Measures

Primary Outcomes (1)

  • Consistency rate

    Consistency rate between Option 1 and Option 2 (calculated using Kappa value). Consistency rate between Option 1 and Option 3 (decision modification rate).

    Baseline(MDT 1 Day)

Secondary Outcomes (12)

  • MDT Discussion Process Time

    Baseline(MDT Day 1)

  • Quality of AI Recommendations

    Baseline(MDT Day 1)

  • Clinical Acceptability of AI

    Baseline(MDT Day 1)

  • MDT Discussion Efficiency

    Baseline(MDT Day 1)

  • Process Convenience

    Baseline(MDT Day 1)

  • +7 more secondary outcomes

Study Arms (1)

AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer

EXPERIMENTAL
Diagnostic Test: Treat Regimen

Interventions

Treat RegimenDIAGNOSTIC_TEST

The impact of artificial intelligence on clinicians' treatment plans

AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Age ≥ 18 years;
  • MDT (Multidisciplinary Team) discussion deems a systemic treatment plan necessary;
  • Complete clinical, imaging, and molecular pathological data.

You may not qualify if:

  • Stage I patients;
  • Diagnosed with a thoracic tumor other than NSCLC;
  • Lack of detailed medical data, or missing data;

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Guangdong Provincial People's Hospital

Guangzhou, Guangdong, 510000, China

RECRUITING

Related Publications (3)

  • Pillay B, Wootten AC, Crowe H, Corcoran N, Tran B, Bowden P, Crowe J, Costello AJ. The impact of multidisciplinary team meetings on patient assessment, management and outcomes in oncology settings: A systematic review of the literature. Cancer Treat Rev. 2016 Jan;42:56-72. doi: 10.1016/j.ctrv.2015.11.007. Epub 2015 Nov 24.

    PMID: 26643552RESULT

  • Kim JK, Chua ME, Li TG, Rickard M, Lorenzo AJ. Novel AI applications in systematic review: GPT-4 assisted data extraction, analysis, review of bias. BMJ Evid Based Med. 2025 Sep 22;30(5):313-322. doi: 10.1136/bmjebm-2024-113066.

    PMID: 40199559RESULT

  • Wiegand TLT, Jung LB, Gudera JA, Schuhmacher LS, Moehrle P, Rischewski JF, Mehrzad P, Jeong S, Nguyen LH, Poeschla M, Velezmoro LI, Kruk L, Dimitriadis K, Koerte IK. Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators. NPJ Digit Med. 2025 Jul 19;8(1):459. doi: 10.1038/s41746-025-01817-6.

    PMID: 40683994RESULT

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Carcinoma, BronchogenicBronchial NeoplasmsLung NeoplasmsRespiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by SiteNeoplasmsLung DiseasesRespiratory Tract Diseases

Central Study Contacts

qing liang, Dr.

CONTACT

+86 17863321987liangtsing99@163.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
TREATMENT
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

March 25, 2026

First Posted

June 4, 2026

Study Start

December 1, 2025

Primary Completion (Estimated)

October 31, 2027

Study Completion (Estimated)

December 31, 2028

Last Updated

June 4, 2026

Record last verified: 2026-05

Locations

CN(1)