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

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: Breast Carcinoma
• Intervention / Treatment: Diagnostic test: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer

• Study Type: Interventional
• Enrollment (Estimated): 5000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Zhenhui LI; Phone Number: 13698736132; Email: lizhenhui@kmmu.edu.cn
• Study Contact Backup: Name: Yu Xie; Phone Number: 13708445492; Email: xieyu@kmmu.edu.cn

Participation Criteria

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

How is the study designed?

Design Details

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

Number of Arms

1

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.	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

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

Study record dates

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