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| Name | Class |
|---|---|
| Xinhua Hospital, Shanghai Jiao Tong University School of Medicine | OTHER |
| Taizhou Hospital | OTHER |
| Wuhan Hospital of Traditional Chinese Medicine | OTHER |
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The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.
Breast cancer is the most common malignancy in women and the second leading cause of cancer deaths worldwide. Therefore, early detection of breast cancer and timely treatment are of great significance for controlling and reducing breast cancer mortality. Breast ultrasound is an adjunct to extensive use in the detection of breast cancer, but ultrasound is highly technically dependent on the examiner, and the results are greatly influenced by the subjective nature of the examiner, adding unnecessary surgery and puncture, which causes great problems for clinicians and patients.Moreover, the value of conventional ultrasound in the differential diagnosis of breast mass is still limited, and the emergence of new technologies such as artificial intelligence and elastography has improved the accuracy of ultrasound diagnosis to varying degrees.
S-Detect technology is a computer-aided (CAD) system recently developed by Samsung Medical Center for breast ultrasound to assist in morphological analysis based on the Breast Imaging Reporting and Data System (BI-RADS) description and final assessment.This provides a new way to identify the benign and malignant breast nodules.
The E-Breast technique, unlike conventional strain-elastic imaging technology, performs an elastic analysis of the entire two-dimensional image.Moreover, when measuring the elastic ratio, it is only necessary to place a region of interest (ROI) at the nodule.Compared with the average elasticity of the surrounding area, it is more reflective of the elastic ratio of the mass to the surrounding tissue.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| breast nodule | Those with one or more breast nodules, age 18 or older, upcoming FNAB or surgery and signed informed consent.Those without adverse effects on the test or threatening other candidates, such as mental illness, pregnancy, poor ultrasound image quality, history of breast surgery or breast biopsy, simple cystic nodules, calcification, excessive mass or too small, the S-DetectTM system can not identify the boundary of the tumor, the basic information is incomplete. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ultrasound diagnosis | Device | Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology |
|
| Measure | Description | Time Frame |
|---|---|---|
| Benign or malignant lesions as determined by pathology | The pathological diagnosis of benign or malignant lesions from surgery samples | Before surgery or biopsy |
| Elastic ratio | Clear ECI value | Before surgery or biopsy |
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Inclusion Criteria:
Exclusion Criteria:
Eligibility is based on gender
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Patients with breast nodules in large tertiary hospitals
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Li-Qiang Zhou, MD | Contact | 15387076275 | zlq_1118@hust.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| Xin-Wu Cui, PhD,MD | Tongji Hospital | Study Chair |
| You-Bin Deng, PhD,MD | Tongji Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Xin-Wu Cui | Recruiting | Wuhan | Hubei | 430030 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28992680 | Result | Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography. 2018 Jul;37(3):217-225. doi: 10.14366/usg.17046. Epub 2017 Aug 14. | |
| 24034748 | Result | Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19. |
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Not public because of the personal information of the participants
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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| ID | Term |
|---|---|
| D014463 | Ultrasonography |
| ID | Term |
|---|---|
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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| Macheng People's Hospital |
| UNKNOWN |
| Huangshi Central Hospital | OTHER |
| Affiliated Hospital of Jiangsu University | OTHER |
| The First People's Hospital of Yichang | UNKNOWN |
| Yichang Second People's Hospital | OTHER |
| Xiangyang Central Hospital | OTHER |
| The Second Hospital of Anhui Medical University | OTHER |
| Anqing Municipal Hospital | OTHER |
| Huainan People's Hospital | UNKNOWN |
| Wenzhou Central Hospital | OTHER |
| Xuzhou First People's Hospital | UNKNOWN |
| The Central Hospital of Lishui City | OTHER |
| Huai'an First People's Hospital | OTHER |
| WISCO General Hospital | UNKNOWN |
| First People's Hospital of Jiangxia District, Wuhan City | UNKNOWN |
| Enshi State Central Hospital | UNKNOWN |
| Lianyungang Third People's Hospital | UNKNOWN |
| First People's Hospital of Xianyang | OTHER |
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| 29681007 | Result | Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound. 2018 Jun;21(2):105-118. doi: 10.1007/s40477-018-0297-2. Epub 2018 Apr 21. |
| 27184656 | Result | Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg.16012. Epub 2016 Apr 14. |
| 35066633 | Derived | Wei Q, Yan YJ, Wu GG, Ye XR, Jiang F, Liu J, Wang G, Wang Y, Song J, Pan ZP, Hu JH, Jin CY, Wang X, Dietrich CF, Cui XW. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol. 2022 Jun;32(6):4046-4055. doi: 10.1007/s00330-021-08452-1. Epub 2022 Jan 23. |
| D017437 |
| Skin and Connective Tissue Diseases |