Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning

May 19, 2026 updated by: Yunnan Cancer Hospital

Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: Multicenter Retrospective and Prospective Validation

This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

5000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria

  1. Female patients aged ≥ 18 years.
  2. Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views.
  3. Confirmed pathological diagnosis (core needle biopsy or surgical resection) serving as the reference standard; or benign lesions with stable findings on follow-up for more than 2 years.
  4. (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results.

2. Exclusion Criteria

  1. Poor image quality with severe artifacts that precluded reliable analysis.
  2. History of previous breast surgery or radiotherapy (except for the recurrence risk subgroup).
  3. Incomplete clinical or pathological data.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer
This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.
Diagnostic test: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer
The digital breast tomosynthesis is part of the standard treatment protocol.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images.
Time Frame: 1day
Taking surgical or puncture histopathological results as the gold standard, the accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images was evaluated. It focuses on challenging BI-RADS 4A lesions, covering retrospective multi-center validation sets and prospective multi-center validation sets to ensure the representativeness and rigor of the indicator.
1day

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Yunnan Cancer Hospital

Investigators

  • Study Director: Lianhua Ye, Ethics Committee of Yunnan Provincial Cancer Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 20, 2026

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

June 30, 2029

Study Registration Dates

First Submitted

April 27, 2026

First Submitted That Met QC Criteria

May 19, 2026

First Posted (Actual)

May 22, 2026

Study Record Updates

Last Update Posted (Actual)

May 22, 2026

Last Update Submitted That Met QC Criteria

May 19, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KYLX2026-064

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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