Breast Cancer
Conditions
Brief summary
As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. E-breast is a software installed in US machines which automatically reveals tumor elastographic features. This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China. Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors.
Interventions
None listed
Sponsors
Peking University Third Hospital
Beijing Hospital
Beijing Chao Yang Hospital
Beijing Zhongguancun Hospital
Peking University Aerospace Center Hospital
Beijing Anzhen Community Health Service Center
First Hospital of Tsinghua University
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
The First Affiliated Hospital of Zhengzhou University
Henan Provincial People's Hospital
Third Affiliated Hospital of Zhengzhou University
Xinxiang Central Hospital
Henan Cancer Hospital
First Hospital of China Medical University
Shengjing Hospital
Liaoning Cancer Hospital & Institute
West China Hospital
Sichuan Provincial People's Hospital
Yan'an Hospital of Kunming City
Xi'an Central Hospital
Ningxia Medical University
First Hospital of Shijiazhuang City
Chengde Central Hospital
Qinghai Province Cancer Hospital
Gansu Cancer Hospital
Shanghai Zhongshan Hospital
Ruijin Hospital
The Affiliated Hospital of Qingdao University
Qingdao Central Hospital
Jining First People's Hospital
Linyi Tumour Hospital
The First Affiliated Hospital of Shanxi Medical University
The Second Affiliated Hospital of Harbin Medical University
Second Hospital of Jilin University
The Second Hospital of the West Coast New Area of Qingdao
Fudan University
Tongji Hospital
Jiangsu Province People's Hospital
Peking University Shougang Hospital
Gansu Jiugang Hospital
Peking Union Medical College Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Female over 18 years of age; * Had breast lesions detected by ultrasound. * No clinical symptoms such as nipple discharge, while breast lesions were not palpable. * Received breast surgery within one week of ultrasound examination. * Agreed to participant in this study and signed informed consent.
Exclusion criteria
* Patients who had received a biopsy of breast lesion before the ultrasound examination. * Patients who were pregnant or lactating. * Patients who were undergoing neoadjuvant treatment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Benign or malignant lesions as determined by pathology | From 2019.1.1 to 2020.1.1 | The pathological diagnosis of benign or malignant lesions from surgery samples |
Countries
China
Outcome results
None listed