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NCT Number: NCT07626736

Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC

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

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Key information

Conditions

Nonsmall Cell Lung Cancer Bronchial Neoplasms Carcinoma, Bronchogenic Carcinoma, Non-Small-Cell Lung Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Guangdong Provincial People's Hospital

Guangzhou, Guangdong, 510000, China

Location status: Recruiting

Location contact

Wenzhao Zhong, Dr.

CONTACT

[email protected]

+8613609777314

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Treat Regimen

Diagnostic Test

The impact of artificial intelligence on clinicians' treatment plans

Primary outcomes

  1. Consistency rate

    Time frame: Baseline(MDT 1 Day)

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

Secondary outcomes

  1. MDT Discussion Process Time

    Time frame: Baseline(MDT Day 1)

    Time from start to end of multidisciplinary team (MDT) discussion, measured immediately after MDT end.

  2. Quality of AI Recommendations

    Time frame: Baseline(MDT Day 1)

    Physician-rated quality of AI recommendations using a Likert 5-point scale (1 = very poor, 5 = excellent).

  3. Clinical Acceptability of AI

    Time frame: Baseline(MDT Day 1)

    Physician-rated clinical acceptability of AI recommendations using a Likert 5-point scale (1 = unacceptable, 5 = fully acceptable).

  4. MDT Discussion Efficiency

    Time frame: Baseline(MDT Day 1)

    Physician-rated efficiency of MDT discussion process aided by AI using a Likert 5-point scale (1 = very inefficient, 5 = very efficient).

  5. Process Convenience

    Time frame: Baseline(MDT Day 1)

    Physician-rated convenience of the AI-integrated workflow using a Likert 5-point scale (1 = very inconvenient, 5 = very convenient).

  6. Added Value to Clinical Decision

    Time frame: Baseline(MDT Day 1)

    Physician-rated added value of AI to clinical decision-making using a Likert 5-point scale (1 = no added value, 5 = significant added value).

  7. Learning and Training Value

    Time frame: Baseline(MDT Day 1)

    Physician-rated learning and training value of AI system using a Likert 5-point scale (1 = no value, 5 = high value).

  8. Overall Satisfaction

    Time frame: Baseline(MDT Day 1)

    Physician-rated overall satisfaction with AI-assisted MDT using a Likert 5-point scale (1 = very dissatisfied, 5 = very satisfied).

  9. Willingness to Use in Future

    Time frame: Baseline(MDT Day 1)

    Physician-rated willingness to use AI system in future clinical practice using a Likert 5-point scale (1 = definitely not willing, 5 = definitely willing).

  10. Disease-Free Survival (DFS)

    Time frame: 3 years

    Time from treatment initiation to disease recurrence or death from any cause, assessed every 3-6 months during 2-3 years follow-up.

  11. Progression-Free Survival (PFS)

    Time frame: 3 years

    Time from treatment initiation to disease progression or death from any cause, assessed every 3-6 months during 2-3 years follow-up.

  12. Overall Survival (OS)

    Time frame: 3 years

    Time from treatment initiation to death from any cause, assessed every 3-6 months during 2-3 years follow-up.

Study contacts

Contact information is provided by the study sponsor or research team.

qing liang, Dr.

CONTACT

[email protected]

+86 17863321987

Sponsors and collaborators

Lead sponsor

Wen-zhao ZHONG

Other

Collaborators

  • Guangdong Provincial People's Hospital

Registry information

Official study title

Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC: a Prospective, Controlled Clinical Trial Protocol

Important dates

Study start
2025
Primary completion
2027
Study completion
2028
First posted
Jun 4, 2026
Registry last updated
Jun 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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