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Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning
Sponsor: Yunnan Cancer Hospital
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.
Official title: Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: Multicenter Retrospective and Prospective Validation
Key Details
Gender
FEMALE
Age Range
18 Years - 80 Years
Study Type
INTERVENTIONAL
Enrollment
5000
Start Date
2026-05-20
Completion Date
2029-06-30
Last Updated
2026-05-22
Healthy Volunteers
No
Conditions
Interventions
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.