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

N/ANot Yet RecruitingNCT07605195

Yunnan Cancer Hospital5,000 enrolled

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

5,000

Conditions

Breast Carcinoma

Interventions

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

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

Eligibility

Sex: FEMALEMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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.

Time frame: 1day

Central Contacts

Yu Xie

CONTACT

13708445492 xieyu@kmmu.edu.cn

Zhenhui LI

CONTACT

13698736132 lizhenhui@kmmu.edu.cn

Data from ClinicalTrials.gov

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