Literature DB >> 35869768

[Value of ultrasonic S-Detect technique in diagnosis of breast masses].

Y Cheng1, Q Xia1, J Wang1, H Xie1, Y Yu1, H Liu1, Z Yao1, J Hu1.   

Abstract

OBJECTIVE: To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses.
METHODS: A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard.
RESULTS: When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05).
CONCLUSION: S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.

Entities:  

Keywords:  S-Detect technique; breast mass; conventional ultrasound; diagnostic value

Mesh:

Year:  2022        PMID: 35869768      PMCID: PMC9308870          DOI: 10.12122/j.issn.1673-4254.2022.07.12

Source DB:  PubMed          Journal:  Nan Fang Yi Ke Da Xue Xue Bao        ISSN: 1673-4254


  14 in total

1.  Nonpalpable BI-RADS 4 breast lesions: sonographic findings and pathology correlation.

Authors:  Eda Elverici; Ayşe Nurdan Barça; Hafize Aktaş; Arzu Özsoy; Betül Zengin; Mehtap Çavuşoğlu; Levent Araz
Journal:  Diagn Interv Radiol       Date:  2015 May-Jun       Impact factor: 2.630

2.  Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators.

Authors:  Eun Young Jeong; Hye Lin Kim; Eun Ju Ha; Seon Young Park; Yoon Joo Cho; Miran Han
Journal:  Eur Radiol       Date:  2018-10-22       Impact factor: 5.315

3.  A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.

Authors:  Salvatore Gitto; Giorgia Grassi; Chiara De Angelis; Cristian Giuseppe Monaco; Silvana Sdao; Francesco Sardanelli; Luca Maria Sconfienza; Giovanni Mauri
Journal:  Radiol Med       Date:  2018-09-22       Impact factor: 3.469

4.  A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment.

Authors:  Young Jun Choi; Jung Hwan Baek; Hye Sun Park; Woo Hyun Shim; Tae Yong Kim; Young Kee Shong; Jeong Hyun Lee
Journal:  Thyroid       Date:  2017-02-28       Impact factor: 6.568

5.  Significance of fine needle aspiration cytology and vacuum-assisted core needle biopsy for small breast lesions.

Authors:  Satoko Nakano; Masahiko Otsuka; Akemi Mibu; Toshinori Oinuma
Journal:  Clin Breast Cancer       Date:  2014-07-30       Impact factor: 3.225

6.  Application of Computer-Aided Diagnosis on Breast Ultrasonography: Evaluation of Diagnostic Performances and Agreement of Radiologists According to Different Levels of Experience.

Authors:  Eun Cho; Eun-Kyung Kim; Mi Kyung Song; Jung Hyun Yoon
Journal:  J Ultrasound Med       Date:  2017-08-01       Impact factor: 2.153

7.  Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography.

Authors:  Hye Lin Kim; Eun Ju Ha; Miran Han
Journal:  Ultrasound Med Biol       Date:  2019-06-29       Impact factor: 2.998

Review 8.  [Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging].

Authors:  Shihui Chen; Weixiang Liu; Jing Qin; Liangliang Chen; Guo Bin; Yuxiang Zhou; Tianfu Wang; Bingsheng Huang
Journal:  Sheng Wu Yi Xue Gong Cheng Xue Za Zhi       Date:  2017-04-25

9.  Computer-aided assessment of tumor grade for breast cancer in ultrasound images.

Authors:  Dar-Ren Chen; Cheng-Liang Chien; Yan-Fu Kuo
Journal:  Comput Math Methods Med       Date:  2015-02-24       Impact factor: 2.238

10.  Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China.

Authors:  Chenyang Zhao; Mengsu Xiao; Yuxin Jiang; He Liu; Ming Wang; Hongyan Wang; Qiang Sun; Qingli Zhu
Journal:  Cancer Manag Res       Date:  2019-01-23       Impact factor: 3.989

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