Clinical trial NCT04270032: Using Deep Learning Methods to Analyze Automated Breast Ultrasound and Hand-held Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.
Official title: To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Breast Cancer
Lead sponsor: The First Affiliated Hospital of the Fourth Military Medical University
Sponsor class: OTHER
Collaborators: name: Seoul National University Bundang Hospital
name: Xidian University
name: Shenzhen University
First posted: 2020-02-17
Enrollment: 10000
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: OTHER
Sampling method: PROBABILITY_SAMPLE
Study population: Female patients over 18 years old from two countries (China and Korea).

Brief summary: The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) and hand-held ultrasound(HHUS) images, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer. The model would provide important references for further early prevention, early diagnosis and personalized treatment.

Detailed description: 1. Establishing a database By collecting ABUS, HHUS and comprehensive breast images data, essential information, clinical treatment information, prognosis, and curative effect information, a complete breast image database is constructed.
2. Marking ABUS images Three doctors use a semi-automatic method to frame the lesions on the image.
3. Building the model Using the deep learning method to preprocess, analyze and train the marked images, and finally get a model diagnosis, efficacy evaluation and prognosis prediction model of breast cancer.
4. Evaluating the model 1）Self-validation： Analyze the sensitivity, AUC of the breast cancer diagnosis model and the false-positive number on each ABUS volume.

2) Compared the sensitivity, AUC and the false-positive number with a commercial diagnosis model.

3)To test the screening and diagnostic efficacy of computer-aided diagnosis systems through prospective or retrospective studies.

4)By analyzing the size and characteristics of the lesions after neoadjuvant chemotherapy, and predicting the OS and DFS time, the therapy assessment and prognosis prediction model were evaluated.

Eligibility criteria: Inclusion Criteria:

1. Female patients over 18 years old who come to the two centers for physical examination or treatment;
2. Complete basic information and image data

Exclusion Criteria:

1. There is no complete ABUS and HHUS images data;
2. The image quality is poor;
3. In multifocal breast cancer, the correlation between the tumor in the image and the postoperative pathological examination is uncertain.

Primary outcomes:
measure: sensitivity
timeFrame: 4 years
measure: false-positive per volume
timeFrame: 4 years
measure: area under curve
timeFrame: 4 years
measure: overall survival(OS) time
timeFrame: up to 10 years
measure: Disease-free survival (DFS) time
timeFrame: up to 5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Xi'an
country: China
facility: The First Affiliated Hospital of Fourth Military Medical University
state: Shaanxi
Study officials:
affiliation: Xijing hospital of The fourth military medical university
name: Hongping Song, MD
role: PRINCIPAL_INVESTIGATOR
References: