Literature DB >> 28396996

Ultrasound positive predictive values by BI-RADS categories 3-5 for solid masses: An independent reader study.

A Thomas Stavros1, Andrea G Freitas2, Giselle G N deMello2, Lora Barke3, Dennis McDonald4,5, Terese Kaske3,6, Ducly Wolverton3,7, Arnold Honick4,8, Daniela Stanzani2, Adriana H Padovan2, Ana Paula C Moura2, Marilia C V de Campos2.   

Abstract

OBJECTIVE: We assessed multiple readers' positive predictive values (PPVs) for ACR BI-RADS 3, 4a, 4b, 4c and 5 masses on ultrasound (US) pre- and post-proposed guidelines.
METHODS: This retrospective, IRB-approved study included four American and four non-American readers who assigned BI-RADS categories for US images of 374 biopsy-proved masses. Readers were offered guidelines and re-classified the masses. We assessed readers' abilities to achieve ACR benchmarks BI-RADS categories pre- and post-guidelines.
RESULTS: PPVs increased with BI-RADS category. The PPVs pre- and post-guidelines were 6.0% and 4.4% for category 3, 27.3% and 30.5% for category 4a, 49.9% and 51.5% for category 4b, 69.0% and 67.4% for category 4c, and 79.3% and 80.1% for category 5. Readers achieved the PPV benchmark for category 4c, but not for categories 3, 4a, 4b and 5, with no significant improvement after guidelines. Regular BI-RADS 4 subcategory users missed benchmarks by less than non-regular users.
CONCLUSION: Pre- and post-guidelines, readers' PPVs increased with BI-RADS categories, ACR PPV benchmarks were achieved in category 4c, missed in other categories, especially in the critical 4a subcategory, where the PPV was too high. BI-RADS 4 subcategory users performed better than non-users. KEY POINTS: • Readers failed to achieve benchmarks for BI-RADS 4 subcategories, especially 4a. • USA and Brazilian readers performed similarly in ACR BI-RADS 4 subcategorization. • Proposed guidelines did not improve overall, USA or Brazilian reader performance. • Regularly BI-RADS 4 subcategory users performed better than did non-users. • US features distinguished between benign and malignant, not BI-RADS 4 subcategories.

Entities:  

Keywords:  Breast cancer; Breast neoplasms; Breast ultrasonography; Diagnostic, imaging; Tumours, breast

Mesh:

Year:  2017        PMID: 28396996     DOI: 10.1007/s00330-017-4835-7

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  20 in total

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Authors:  Baoxian Liu; Yanling Zheng; Guangliang Huang; Manxia Lin; Quanyuan Shan; Ying Lu; Wenshuo Tian; Xiaoyan Xie
Journal:  Ultrasound Med Biol       Date:  2016-01-06       Impact factor: 2.998

2.  Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses.

Authors:  Wendie A Berg; David O Cosgrove; Caroline J Doré; Fritz K W Schäfer; William E Svensson; Regina J Hooley; Ralf Ohlinger; Ellen B Mendelson; Catherine Balu-Maestro; Martina Locatelli; Christophe Tourasse; Barbara C Cavanaugh; Valérie Juhan; A Thomas Stavros; Anne Tardivon; Joel Gay; Jean-Pierre Henry; Claude Cohen-Bacrie
Journal:  Radiology       Date:  2012-02       Impact factor: 11.105

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.  Breast lesions: imaging with contrast-enhanced subharmonic US--initial experience.

Authors:  Flemming Forsberg; Catherine W Piccoli; Daniel A Merton; Juan J Palazzo; Anne L Hall
Journal:  Radiology       Date:  2007-08-09       Impact factor: 11.105

5.  Training the ACRIN 6666 Investigators and effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS ultrasound feature analysis.

Authors:  Wendie A Berg; Jeffrey D Blume; Jean B Cormack; Ellen B Mendelson
Journal:  AJR Am J Roentgenol       Date:  2012-07       Impact factor: 3.959

6.  Subcategorization of ultrasonographic BI-RADS category 4: positive predictive value and clinical factors affecting it.

Authors:  Jung Hyun Yoon; Min Jung Kim; Hee Jung Moon; Jin Young Kwak; Eun-Kyung Kim
Journal:  Ultrasound Med Biol       Date:  2011-03-31       Impact factor: 2.998

7.  Adjunct Screening With Tomosynthesis or Ultrasound in Women With Mammography-Negative Dense Breasts: Interim Report of a Prospective Comparative Trial.

Authors:  Alberto S Tagliafico; Massimo Calabrese; Giovanna Mariscotti; Manuela Durando; Simona Tosto; Francesco Monetti; Sonia Airaldi; Bianca Bignotti; Jacopo Nori; Antonella Bagni; Alessio Signori; Maria Pia Sormani; Nehmat Houssami
Journal:  J Clin Oncol       Date:  2016-03-09       Impact factor: 44.544

8.  Real-time elastography for the differentiation of benign and malignant breast lesions: a meta-analysis.

Authors:  Xia Gong; Qiuhua Xu; Zhengliang Xu; Ping Xiong; Weili Yan; Yazhu Chen
Journal:  Breast Cancer Res Treat       Date:  2011-08-26       Impact factor: 4.872

9.  Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer.

Authors:  Wendie A Berg; Jeffrey D Blume; Jean B Cormack; Ellen B Mendelson; Daniel Lehrer; Marcela Böhm-Vélez; Etta D Pisano; Roberta A Jong; W Phil Evans; Marilyn J Morton; Mary C Mahoney; Linda Hovanessian Larsen; Richard G Barr; Dione M Farria; Helga S Marques; Karan Boparai
Journal:  JAMA       Date:  2008-05-14       Impact factor: 56.272

10.  Possible net harms of breast cancer screening: updated modelling of Forrest report.

Authors:  James Raftery; Maria Chorozoglou
Journal:  BMJ       Date:  2011-12-08
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  6 in total

1.  Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

Authors:  Alexander Ciritsis; Cristina Rossi; Matthias Eberhard; Magda Marcon; Anton S Becker; Andreas Boss
Journal:  Eur Radiol       Date:  2019-03-29       Impact factor: 5.315

2.  Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

Authors:  Sahar Mansour; Rasha Kamal; Lamiaa Hashem; Basma AlKalaawy
Journal:  Br J Radiol       Date:  2021-10-18       Impact factor: 3.039

3.  Serum semaphorin4C as an auxiliary diagnostic biomarker for breast cancer.

Authors:  Ya Wang; Jiahao Liu; Jiali Li; Huayi Li; Xiong Li; Long Qiao; Jie Yang; Tian Fang; Shaoqi Chen; Jingjing Ma; Junxiang Wan; Xingrui Li; Lin Zhang; Yun Xia; Yaqun Wu; Tao Xu; Jun Shao; Yaojun Feng; Ihab R Kamel; Qifeng Yang; Zhen Li; Qinglei Gao
Journal:  Clin Transl Med       Date:  2021-08

4.  Evaluation of the accuracy of mammography, ultrasound and magnetic resonance imaging in suspect breast lesions.

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Journal:  Clinics (Sao Paulo)       Date:  2020-07-22       Impact factor: 2.365

5.  Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.

Authors:  Ji Soo Choi; Boo Kyung Han; Eun Sook Ko; Jung Min Bae; Eun Young Ko; So Hee Song; Mi Ri Kwon; Jung Hee Shin; Soo Yeon Hahn
Journal:  Korean J Radiol       Date:  2019-05       Impact factor: 3.500

6.  Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study.

Authors:  Qingling Zhang; Qinglu Zhang; Taixia Liu; Tingting Bao; Qingqing Li; You Yang
Journal:  Front Oncol       Date:  2022-04-07       Impact factor: 5.738

  6 in total

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