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NCT Number: NCT07605195

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

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.

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Key information

Conditions

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

Age range

18 year–80 year

Sex eligibility

Female

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.
  • Exclusion Criteria
  • 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.

Treatment and study plan

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.

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.

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Yu Xie

CONTACT

[email protected]

13708445492

Zhenhui LI

CONTACT

[email protected]

13698736132

Sponsors and collaborators

Lead sponsor

Yunnan Cancer Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2029
First posted
May 22, 2026
Registry last updated
May 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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