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

NCT07605195 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 5000

Last updated 2026-05-22

No results posted yet for this study

Summary

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.

Conditions

  • Breast Carcinoma

Interventions

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.

Sponsors & Collaborators

  • Yunnan Cancer Hospital

    lead OTHER

Principal Investigators

  • Lianhua Ye · Ethics Committee of Yunnan Provincial Cancer Hospital

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-05-20
Primary Completion
2026-12-31
Completion
2029-06-30

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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 NCT07605195 on ClinicalTrials.gov