NCT07605195尚未招募不适用
Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: Multicenter Retrospective and Prospective Validation
Yunnan Cancer Hospital0 个研究点目标入组 5,000 人开始时间: 2026年5月20日最近更新:
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 5,000
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
研究者
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