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| Name | Class |
|---|---|
| Guangdong Provincial People's Hospital | OTHER |
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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Treat Regimen | Diagnostic Test | The impact of artificial intelligence on clinicians' treatment plans |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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) |
| Measure | Description | Time Frame |
|---|---|---|
| MDT Discussion Process Time | Time from start to end of multidisciplinary team (MDT) discussion, measured immediately after MDT end. | 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). |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| qing liang, Dr. | Contact | +86 17863321987 | liangtsing99@163.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Guangdong Provincial People's Hospital | Recruiting | Guangzhou | Guangdong | 510000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26643552 | Result | 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. | |
| 40199559 |
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Patient information cannot be disclosed.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 5, 2026 | May 23, 2026 | Prot_SAP_000.pdf |
| Prot_ICF | Yes | No | Yes | Study Protocol and Informed Consent Form | Apr 5, 2026 | May 23, 2026 | Prot_ICF_001.pdf |
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| ID | Term |
|---|---|
| D002289 | Carcinoma, Non-Small-Cell Lung |
| ID | Term |
|---|---|
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
| D008175 | Lung Neoplasms |
| D012142 | Respiratory Tract Neoplasms |
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| 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). | 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). | 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). | 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). | 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). | 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). | 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). | 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. | 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. | 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. | 3 years |
| 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. |
| 40683994 | Result | 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. |
| D013899 |
| Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |