Clinical trial NCT03851497: Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
Official title: A Multi-center Study of Deep Learning Diagnosis and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Breast Cancer
Lead sponsor: Peking Union Medical College Hospital
Sponsor class: OTHER
Collaborators: name: Peking University Third Hospital
name: Beijing Hospital
name: Beijing Chao Yang Hospital
name: Beijing Zhongguancun Hospital
name: Peking University Aerospace Center Hospital
name: Beijing Anzhen Community Health Service Center
name: First Hospital of Tsinghua University
name: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: The First Affiliated Hospital of Zhengzhou University
name: Henan Provincial People's Hospital
name: Third Affiliated Hospital of Zhengzhou University
name: Xinxiang Central Hospital
name: Henan Cancer Hospital
name: First Hospital of China Medical University
name: Shengjing Hospital
name: Liaoning Cancer Hospital & Institute
name: West China Hospital
name: Sichuan Provincial People's Hospital
name: Yan'an Hospital of Kunming City
name: Xi'an Central Hospital
name: Ningxia Medical University
name: First Hospital of Shijiazhuang City
name: Chengde Central Hospital
name: Qinghai Province Cancer Hospital
name: Gansu Cancer Hospital
name: Shanghai Zhongshan Hospital
name: Ruijin Hospital
name: The Affiliated Hospital of Qingdao University
name: Qingdao Central Hospital
name: Jining First People's Hospital
name: Linyi Tumour Hospital
name: The First Affiliated Hospital of Shanxi Medical University
name: The Second Affiliated Hospital of Harbin Medical University
name: Second Hospital of Jilin University
name: The Second Hospital of the West Coast New Area of Qingdao
name: Fudan University
name: Tongji Hospital
name: Jiangsu Province People's Hospital
name: Peking University Shougang Hospital
name: Gansu Jiugang Hospital
First posted: 2019-02-22
Enrollment: 1200
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Asymptomatic female patients voluntarily asked for breast US examination in comprehensive hospitals for breast cancer screening.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Benign or malignant lesions as determined by pathology
timeFrame: From 2019.1.1 to 2020.1.1
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking Union Medical College Hospital
state: Beijing
Study officials:
References: