Literature DB >> 29569216

Focal breast lesion characterization according to the BI-RADS US lexicon: role of a computer-aided decision-making support.

Tommaso Vincenzo Bartolotta1, Alessia Orlando2, Vito Cantisani3, Domenica Matranga4, Raffele Ienzi2, Alessandra Cirino2, Francesco Amato2, Maria Laura Di Vittorio2, Massimo Midiri2, Roberto Lagalla2.   

Abstract

Entities:  

Keywords:  BI-RADS; Breast; Computer aided; Diagnosis; Neoplasms; Ultrasonography

Mesh：

Year:  2018        PMID： 29569216     DOI： 10.1007/s11547-018-0874-7

Source DB:  PubMed          Journal:  Radiol Med        ISSN： 0033-8362            Impact factor:   3.469


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  23 in total

Review 1.  US of breast masses categorized as BI-RADS 3, 4, and 5: pictorial review of factors influencing clinical management.

Authors:  Sughra Raza; Allison L Goldkamp; Sona A Chikarmane; Robyn L Birdwell
Journal:  Radiographics       Date:  2010-09       Impact factor: 5.333

2.  BI-RADS for sonography: positive and negative predictive values of sonographic features.

Authors:  Andrea S Hong; Eric L Rosen; Mary S Soo; Jay A Baker
Journal:  AJR Am J Roentgenol       Date:  2005-04       Impact factor: 3.959

3.  BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value.

Authors:  Elizabeth Lazarus; Martha B Mainiero; Barbara Schepps; Susan L Koelliker; Linda S Livingston
Journal:  Radiology       Date:  2006-03-28       Impact factor: 11.105

4.  Computer aided classification system for breast ultrasound based on Breast Imaging Reporting and Data System (BI-RADS).

Authors:  Wei-Chih Shen; Ruey-Feng Chang; Woo Kyung Moon
Journal:  Ultrasound Med Biol       Date:  2007-08-03       Impact factor: 2.998

5.  Characterisation of indeterminate focal breast lesions on grey-scale ultrasound: role of ultrasound elastography.

Authors:  T V Bartolotta; R Ienzi; A Cirino; C Genova; F Ienzi; D Pitarresi; E Safina; M Midiri
Journal:  Radiol Med       Date:  2011-03-07       Impact factor: 3.469

Review 6.  BI-RADS update.

Authors:  Cecilia L Mercado
Journal:  Radiol Clin North Am       Date:  2014-05       Impact factor: 2.303

7.  Using computer-aided detection in mammography as a decision support.

Authors:  Maurice Samulski; Rianne Hupse; Carla Boetes; Roel D M Mus; Gerard J den Heeten; Nico Karssemeijer
Journal:  Eur Radiol       Date:  2010-06-09       Impact factor: 5.315

Review 8.  Breast ultrasonography: state of the art.

Authors:  Regina J Hooley; Leslie M Scoutt; Liane E Philpotts
Journal:  Radiology       Date:  2013-09       Impact factor: 11.105

Review 9.  Computer-aided detection in breast MRI: a systematic review and meta-analysis.

Authors:  Monique D Dorrius; Marijke C Jansen-van der Weide; Peter M A van Ooijen; Ruud M Pijnappel; Matthijs Oudkerk
Journal:  Eur Radiol       Date:  2011-03-15       Impact factor: 5.315

10.  Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist.

Authors:  Kiwook Kim; Mi Kyung Song; Eun-Kyung Kim; Jung Hyun Yoon
Journal:  Ultrasonography       Date:  2016-04-14
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  11 in total

1.  Preoperative loco-regional staging of invasive lobular carcinoma with contrast-enhanced digital mammography (CEDM).

Authors:  Francesco Amato; Giulia Bicchierai; Donatello Cirone; Catherine Depretto; Federica Di Naro; Ermanno Vanzi; Gianfranco Scaperrotta; Tommaso Vincenzo Bartolotta; Vittorio Miele; Jacopo Nori
Journal:  Radiol Med       Date:  2019-11-26       Impact factor: 3.469

Review 2.  S-Detect characterization of focal breast lesions according to the US BI RADS lexicon: a pictorial essay.

Authors:  Tommaso Vincenzo Bartolotta; Alessia Angela Maria Orlando; Luigi Spatafora; Mariangela Dimarco; Cesare Gagliardo; Adele Taibbi
Journal:  J Ultrasound       Date:  2020-03-17

Review 3.  A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

Authors:  Simone Vicini; Chandra Bortolotto; Marco Rengo; Daniela Ballerini; Davide Bellini; Iacopo Carbone; Lorenzo Preda; Andrea Laghi; Francesca Coppola; Lorenzo Faggioni
Journal:  Radiol Med       Date:  2022-06-30       Impact factor: 6.313

4.  Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound.

Authors:  Min Young Kim; Soo-Yeon Kim; Yeon Soo Kim; Eun Sil Kim; Jung Min Chang
Journal:  Medicine (Baltimore)       Date:  2021-08-06       Impact factor: 1.817

5.  S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors.

Authors:  Tommaso Vincenzo Bartolotta; Alessia Angela Maria Orlando; Maria Laura Di Vittorio; Francesco Amato; Mariangela Dimarco; Domenica Matranga; Raffaele Ienzi
Journal:  J Ultrasound       Date:  2020-05-23

6.  A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist.

Authors:  Hee Jeong Park; Sun Mi Kim; Bo La Yun; Mijung Jang; Bohyoung Kim; Ja Yoon Jang; Jong Yoon Lee; Soo Hyun Lee
Journal:  Medicine (Baltimore)       Date:  2019-01       Impact factor: 1.817

7.  Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.

Authors:  Soo -Yeon Kim; Yunhee Choi; Eun -Kyung Kim; Boo-Kyung Han; Jung Hyun Yoon; Ji Soo Choi; Jung Min Chang
Journal:  Sci Rep       Date:  2021-01-11       Impact factor: 4.379

8.  Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Authors:  Baptiste Vasey; Stephan Ursprung; Benjamin Beddoe; Elliott H Taylor; Neale Marlow; Nicole Bilbro; Peter Watkinson; Peter McCulloch
Journal:  JAMA Netw Open       Date:  2021-03-01

9.  Differential diagnosis value of the ultrasound gray scale ratio for papillary thyroid microcarcinomas and micronodular goiters.

Authors:  Zhijiang Han; Zhikai Lei; Mingkui Li; Dingcun Luo; Jinwang Ding
Journal:  Quant Imaging Med Surg       Date:  2018-06

10.  Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study.

Authors:  Chenyang Zhao; Mengsu Xiao; He Liu; Ming Wang; Hongyan Wang; Jing Zhang; Yuxin Jiang; Qingli Zhu
Journal:  BMJ Open       Date:  2020-06-07       Impact factor: 2.692
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