Clinical trial NCT03887598: Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Breast Nodules
Official title: A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Breast Cancer
Keywords: Artificial Intelligence; Ultrasound; Breast nodule; ECI
Lead sponsor: Tongji Hospital
Sponsor class: OTHER
Collaborators: name: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
name: Taizhou Hospital
name: Wuhan Hospital of Traditional Chinese Medicine
name: Macheng People's Hospital
name: Huangshi Central Hospital, China
name: Affiliated Hospital of Jiangsu University
name: The First People's Hospital of Yichang
name: The Second People's Hospital of Yichang
name: Xiangyang Central Hospital
name: The Second Hospital of Anhui Medical University
name: Anqing People's Hospital
name: Huainan People's Hospital
name: Wenzhou Central Hospital
name: Xuzhou First People's Hospital
name: The Central Hospital of Lishui City
name: Huai'an First People's Hospital
name: WISCO General Hospital
name: First People's Hospital of Jiangxia District, Wuhan City
name: Enshi State Central Hospital
name: Lianyungang Third People's Hospital
name: First People's Hospital of Xianyang
First posted: 2019-03-25
Enrollment: 2000
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with breast nodules in large tertiary hospitals

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

1. Had breast lesions detected by ultrasound
2. Age 18 or older
3. Upcoming FNAB or surgery
4. Signing informed consent

Exclusion Criteria:

1. Patients who had received a biopsy of breast lesion before the ultrasound examination
2. Can not cooperate with the test operation
3. Patients who were pregnant or lactating
4. Patients who were undergoing neoadjuvant treatment.

Primary outcomes:
measure: Benign or malignant lesions as determined by pathology
timeFrame: Before surgery or biopsy
measure: Elastic ratio
timeFrame: Before surgery or biopsy
Secondary outcomes:
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Xin-Wu Cui
state: Hubei
Study officials:
affiliation: Tongji Hospital
name: Xin-Wu Cui, PhD,MD
role: STUDY_CHAIR
affiliation: Tongji Hospital
name: You-Bin Deng, PhD,MD
role: STUDY_CHAIR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.