Nonsmall Cell Lung CancerNCT07626736
Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC
A study testing Treat Regimen in people with Nonsmall Cell Lung Cancer, run by Wen-zhao ZHONG at 1 site in China.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Nonsmall Cell Lung Cancer
- What is being tested?
- Treat Regimen
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2025to 31 Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age ≥ 18 years;
- MDT (Multidisciplinary Team) discussion deems a systemic treatment plan necessary;
- Complete clinical, imaging, and molecular pathological data.
Who cannot join
- Stage I patients;
- Diagnosed with a thoracic tumor other than NSCLC;
- Lack of detailed medical data, or missing data;
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Guangdong Provincial People's Hospital
Guangzhou, Guangdong, 510000
Wenzhao Zhong, Dr., +8613609777314, 13609777314@163.com
About this study
Official title: Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC: a Prospective, Controlled Clinical Trial Protocol
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 :
Read more
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
- Conditions
- Nonsmall Cell Lung Cancer
- Being tested
- Treat Regimen
- Keywords
- Non-Small Cell Lung Cancer, Multidisciplinary Team, Locally Deployed AI Model, Large Language Model, Treatment Decision-Making
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Treat RegimenDiagnostic test
The impact of artificial intelligence on clinicians' treatment plans
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer | Experimental | Not described Receives: Diagnostic Test: Treat Regimen |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Baseline(MDT 1 Day)
Secondary outcomes (12)
MDT Discussion Process Time
Time from start to end of multidisciplinary team (MDT) discussion, measured immediately after MDT end.
Measured: Baseline(MDT Day 1)
Quality of AI Recommendations
Physician-rated quality of AI recommendations using a Likert 5-point scale (1 = very poor, 5 = excellent).
Measured: Baseline(MDT Day 1)
Clinical Acceptability of AI
Physician-rated clinical acceptability of AI recommendations using a Likert 5-point scale (1 = unacceptable, 5 = fully acceptable).
Measured: Baseline(MDT Day 1)
MDT Discussion Efficiency
Physician-rated efficiency of MDT discussion process aided by AI using a Likert 5-point scale (1 = very inefficient, 5 = very efficient).
Measured: Baseline(MDT Day 1)
Process Convenience
Physician-rated convenience of the AI-integrated workflow using a Likert 5-point scale (1 = very inconvenient, 5 = very convenient).
Measured: Baseline(MDT Day 1)
Added Value to Clinical Decision
Physician-rated added value of AI to clinical decision-making using a Likert 5-point scale (1 = no added value, 5 = significant added value).
Measured: Baseline(MDT Day 1)
Learning and Training Value
Physician-rated learning and training value of AI system using a Likert 5-point scale (1 = no value, 5 = high value).
Measured: Baseline(MDT Day 1)
Overall Satisfaction
Physician-rated overall satisfaction with AI-assisted MDT using a Likert 5-point scale (1 = very dissatisfied, 5 = very satisfied).
Measured: Baseline(MDT Day 1)
Willingness to Use in Future
Physician-rated willingness to use AI system in future clinical practice using a Likert 5-point scale (1 = definitely not willing, 5 = definitely willing).
Measured: Baseline(MDT Day 1)
Disease-Free Survival (DFS)
Time from treatment initiation to disease recurrence or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Measured: 3 years
Progression-Free Survival (PFS)
Time from treatment initiation to disease progression or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Measured: 3 years
Overall Survival (OS)
Time from treatment initiation to death from any cause, assessed every 3-6 months during 2-3 years follow-up.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2026
- Study start1 Dec 2025
- Primary completion31 Oct 2027 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Nonsmall Cell Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Wen-zhao ZHONG
- Sponsor type
- University, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Responsible party
- Sponsor-investigator, Wen-zhao ZHONG, Guangdong Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07626736
- Sponsor's protocol ID
- KY2025-1003-02
- Status last verified
- May 2026
- First submitted
- 25 Mar 2026
- Last updated
- 4 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Patient information cannot be disclosed.
Publications
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.
PubMed 26643552Kim 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.
PubMed 40199559Wiegand 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.
PubMed 40683994
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC: a Prospective, Controlled Clinical Trial Protocol
- ClinicalTrials.gov ID
- NCT07626736
- Acronym / sponsor ID
- KY2025-1003-02
- Conditions
- Nonsmall Cell Lung Cancer
- Keywords
- Non-Small Cell Lung Cancer, Multidisciplinary Team, Locally Deployed AI Model, Large Language Model, Treatment Decision-Making
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Wen-zhao ZHONGUniversity, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Study start
- 1 Dec 2025 (actual)
- Primary completion
- 31 Oct 2027 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 4 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer | Experimental | Diagnostic Test: Treat Regimen |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Age ≥ 18 years;
- MDT (Multidisciplinary Team) discussion deems a systemic treatment plan necessary;
- Complete clinical, imaging, and molecular pathological data.
Key exclusion criteria (3 listed)
- Stage I patients;
- Diagnosed with a thoracic tumor other than NSCLC;
- Lack of detailed medical data, or missing data;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Treat RegimenThe impact of artificial intelligence on clinicians' treatment plans | Diagnostic test | AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer |
Primary outcome
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).
12 secondary outcomes
MDT Discussion Process Time
Time frame: Baseline(MDT Day 1)
Quality of AI Recommendations
Time frame: Baseline(MDT Day 1)
Clinical Acceptability of AI
Time frame: Baseline(MDT Day 1)
MDT Discussion Efficiency
Time frame: Baseline(MDT Day 1)
Process Convenience
Time frame: Baseline(MDT Day 1)
Added Value to Clinical Decision
Time frame: Baseline(MDT Day 1)
Learning and Training Value
Time frame: Baseline(MDT Day 1)
Overall Satisfaction
Time frame: Baseline(MDT Day 1)
Willingness to Use in Future
Time frame: Baseline(MDT Day 1)
Disease-Free Survival (DFS)
Time frame: 3 years
Progression-Free Survival (PFS)
Time frame: 3 years
Overall Survival (OS)
Time frame: 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2026.
- Results publication: PubMed 26643552Pillay 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.
- Results publication: PubMed 40199559Kim 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.
- Results publication: PubMed 40683994Wiegand 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.
- Individual participant dataSharing planned: Undecided. Patient information cannot be disclosed.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Treat Regimen in people with Nonsmall Cell Lung Cancer, run by Wen-zhao ZHONG at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 4 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact qing liang, Dr. (+86 17863321987, liangtsing99@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Consistency rate (time frame: Baseline(MDT 1 Day)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07626736, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jun 2026.