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Not yet recruiting NCT07605195

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

No phase Interventional Breast Carcinoma

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer.
Who it may be relevant to
Registry conditions: Breast Carcinoma. Basic parameters: 18 years — 80 years · Female.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Center list to be confirmed — check the primary protocol.
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

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

Overview

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 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.

Primary outcome measures

  • 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]

Eligibility criteria

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.

2\. 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
N/A
Model
Single group
Masking
Open label
Primary purpose
Diagnostic

Study locations

Center list to be confirmed — check the primary protocol.

Identifiers

NCT: NCT07605195 · KYLX2026-064

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗