Breast Cancer Screening
Conditions
Brief summary
Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited. This project aims to improve the effectiveness and feasibility of breast cancer screening by addressing the core issues and bottlenecks in population-based breast cancer screening. We will conduct a prospective cluster-controlled screening trial in the general population, with district-based cluster grouping. The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will receive conventional screening: breast ultrasound for initial screening and mammography for secondary screening. Based on population screening practices, we will evaluate the effectiveness of AI-assisted imaging diagnostic technology in various technical aspects of actual screening and perform cost-effectiveness analyses. This study will investigate the application of AI-assisted breast imaging technology in population-based breast cancer screening, providing scientific evidence for the large-scale implementation of AI-assisted imaging technologies. Furthermore, by combining population screening practices with model simulations, we will explore multi-dimensional breast cancer screening strategies to optimize screening approaches and technologies for the Chinese population.
Interventions
DEVICEAI-assisted screening
The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography
Sponsors
Fudan University
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
35 Years to 69 Years
Healthy volunteers
Yes
Inclusion criteria
* women aged 35 to 69 years, who were attending the "Two Cancers (Breast and Cervical Cancer) Screening" project, and had no history of breast cancer, including in-situ cancer, or any other cancers in the previous five years.
Exclusion criteria
* have serious cardiopulmonary insufficiency, liver or kidney insufficiency, or other systemic diseases, and a life expectancy of less than five years
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The incidence of early-stage breast cancer over a one-year follow-up period, compared between women who underwent AI-assisted screening and those with routine screening | From enrollment to 1-year after the end of screening | Early-stage breast cancer was defined as cancer confined to the breast (local) or to the breast and regional lymph nodes (locoregional). Specifically, it referred to tumors \<2 cm in diameter, with no ipsilateral axillary lymph node involvement and no distant metastasis. According to the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition) and the Chinese Guideline for Breast Cancer Screening and Early Diagnosis and Treatment (2021, Beijing), early-stage breast cancer encompassed stage 0 (including ductal carcinoma in situ and lobular carcinoma in situ), stage I, and stage II. |
| The detection rate of suspicious breast lesions (including masses and calcifications) over a one-year follow-up period, compared between women who underwent AI-assisted ultrasound combined with AI-assisted mammography and those who received routine scree | From enrollment to 1-year after the end of screening | — |
Countries
China
Contacts
CONTACTYing Zheng
Outcome results
None listed