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Human-AI Collaborative INSIGHT Diagnostic Workflow for in Breast Cancer With Extensive Intraductal Component

NCT07060599 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 480

Last updated 2025-07-11

No results posted yet for this study

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.

Conditions

  • Artificial Intelligence (AI) in Diagnosis

Interventions

OTHER

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.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Principal Investigators

  • Peng Sun, MD, PhD. · Sun Yat-sen University

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-01
Primary Completion
2026-12-31
Completion
2027-08-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07060599 on ClinicalTrials.gov