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Human-AI Collaborative INSIGHT Diagnostic Workflow for in Breast Cancer With Extensive Intraductal Component
Sponsor: Sun Yat-sen University
Summary
The goal of this clinical trial is to see if an artificial intelligence (AI)-assisted method helps doctors more accurately detect invasive breast cancer in people with a specific type of tumor called "extensive intraductal carcinoma" (EIC). This type of tumor is challenging to diagnose correctly using standard methods. The main question this study aims to answer is: Does the new AI-assisted method find more invasive breast cancer in EIC tumors compared to the standard method? Researchers will compare two groups: * Group 1 (INSIGHT): Doctors review breast tissue samples using an AI tool that highlights suspicious areas needing closer attention. * Group 2 (Conventional): Doctors review breast tissue samples without AI help, using the standard method. This comparison will show if the AI-assisted method works better at finding invasive cancer. What happens in the study? * Researchers will use stored breast tissue samples already collected during the participant's surgery. * Each sample will be randomly assigned to be reviewed using either the new AI-assisted method (Group 1) or the standard method (Group 2). * In Group 1, an AI program will scan the tissue images first and point out areas that might contain invasive cancer for the doctor to check closely. * In Group 2, doctors will review the tissue images without any AI help, using their standard process. * Researchers will measure which method finds invasive cancer more accurately, how long the review takes, and how many additional tests (called IHC stains) are needed. No new procedures are required from participants; the study uses existing tissue samples.
Official title: A Prospective Multicenter Randomized Trial Comparing the Human-AI Collaborative INSIGHT Workflow vs. Conventional Pathology Diagnosis for Detecting Invasive Carcinoma in Breast Cancer With Extensive Intraductal Component (EIC)
Key Details
Gender
FEMALE
Age Range
Any - Any
Study Type
INTERVENTIONAL
Enrollment
480
Start Date
2025-08-01
Completion Date
2027-08-01
Last Updated
2025-07-11
Healthy Volunteers
No
Interventions
INvasion Screening with Intelligent Guidance for Histopathology Triage (INSIGHT) Workflow
An AI-generated segmentation model are refined through a post-processing pipeline: retaining only invasive carcinoma (IC) regions, filtering detections \<500 µm², grouping adjacent IC areas, and generating per-cluster bounding boxes (red boxes). This converted raw segmentations into clinically actionable ROI proposals, balancing sensitivity and specificity for pathologist review in external testing and clinical validation. The INSIGHT workflow addresses key diagnostic challenges in EIC cases by pre-screening whole-slide images (WSIs) and intelligently marking potential IC regions. This guides pathologists to prioritize diagnostically critical areas across multiple slides or within extensive DCIS - a task particularly valuable when IC is multifocal or presents as subtle micro-invasive foci easily overlooked during routine manual examination.
Locations (1)
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China