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

NCT07605195 | Not Yet Recruiting

• 1 other identifier: KYLX2026-064
• Study Type: interventional
• Target: 5,000
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Breast Carcinoma

Outcome Measures

Primary Outcomes (1)

• 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.

  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

Study Arms (1)

The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer

EXPERIMENTAL

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

Interventions

To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer DIAGNOSTIC_TEST

The digital breast tomosynthesis is part of the standard treatment protocol.

The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Female patients aged ≥ 18 years.
• Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views.
• 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.
• (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results.

You may not qualify if:

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

Sponsors & Collaborators

• Yunnan Cancer Hospital: lead

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Lianhua Ye

  Ethics Committee of Yunnan Provincial Cancer Hospital

  STUDY DIRECTOR

Central Study Contacts

Yu Xie

CONTACT

13708445492; xieyu@kmmu.edu.cn

Zhenhui LI

CONTACT

13698736132; lizhenhui@kmmu.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 27, 2026
• First Posted: May 22, 2026
• Study Start: May 20, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): June 30, 2029
• Last Updated: May 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share