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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT07605195

Enrollment

5000

Registered

2026-05-22

Start date

2026-05-20

Completion date

2029-06-30

Last updated

2026-05-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Carcinoma

Brief 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.

Interventions

DIAGNOSTIC_TESTTo 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.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to 80 Years

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
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.	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.

Contacts

CONTACTYu Xie

xieyu@kmmu.edu.cn13708445492

CONTACTZhenhui LI

lizhenhui@kmmu.edu.cn13698736132

STUDY_DIRECTORLianhua Ye

Ethics Committee of Yunnan Provincial Cancer Hospital

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 23, 2026