Pathology Foundation Model
Conditions
Keywords
Randomized Controlled Trial
Brief summary
The investigators plan to conduct a multicenter, prospective, randomized controlled trial to systematically evaluate the added value of pathology-based AI models in the gastric cancer diagnostic workflow. The study will focus on comparing AI-assisted platform interpretation with conventional independent slide reading in terms of diagnostic accuracy (e.g., AUC), reading efficiency (e.g., comparison of time to diagnosis), quality of diagnostic reports, diagnostic confidence (Likert scale), and pathologists' satisfaction with the AI models. The investigators will also assess superiority for less-experienced (junior) pathologists and noninferiority for more-experienced (senior) pathologists. Successful completion of this project will provide high-level prospective evidence to support the standardized deployment, quality control, and broader application of pathology AI in the gastric cancer care pathway.
Interventions
OTHERAI pathology model
Doctors in this group are required to use the AI pathology model to assist their diagnoses. The AI pathology model will provide a predicted result for each case.
OTHERControl
Pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence.
Sponsors
Nanfang Hospital, Southern Medical University
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
OTHER
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Sex: ≥ 18 years of age; 2. Patients undergoing gastric mucosal biopsy or gastric cancer surgical resection, with available digital pathology images and clinical information.
Exclusion criteria
1.Missing data or data of insufficient quality for analysis
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under ROC curve (AUC) | Assessments will be conducted within one week after the physicians' diagnoses. | Area under the curve |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic time per case | Measured immediately after the physician's diagnosis. | Time required for the pathologist to complete the diagnosis of each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic time is defined as the duration (in minutes/seconds) from initiating case review to finalizing and submitting the diagnostic report in the study system |
| Diagnostic report quality score | Within 1 week after the initial diagnosis for each case. | Quality score of pathology diagnostic reports in the AI-assisted diagnosis group compared with the independent diagnosis group. Report quality will be evaluated by an independent panel of expert pathologists using a predefined scoring rubric (e.g., 0-100 scale), considering diagnostic accuracy, completeness, clarity, and structure of the report. Higher scores indicate better report quality. |
| Pathologists' diagnostic confidence | At the time of diagnosis for each case. | Self-reported diagnostic confidence of pathologists for each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic confidence will be rated by the reporting pathologist on a \[5\]-point Likert scale (e.g., 1 = very uncertain to 5 = very confident) immediately after completing the diagnosis. Higher scores indicate greater diagnostic confidence. |
| Pathologists' satisfaction with the AI pathology model | Assessed once at the end of the AI-assisted reading period for each pathologist. | Overall satisfaction of pathologists with the AI pathology diagnostic model in terms of usability and perceived effectiveness. Satisfaction will be assessed using a structured questionnaire comprising Likert-scale items that evaluate ease of use, integration into workflow, clarity of AI outputs, perceived impact on diagnostic efficiency, and perceived impact on diagnostic accuracy and confidence. Higher scores indicate higher satisfaction, better usability, and greater perceived effectiveness. |
Countries
China
Contacts
STUDY_DIRECTORLi Liang
Nanfang Hospital, Southern Medical University
Outcome results
None listed