Literature DB >> 21330080

Radiologists' performance in the detection of benign and malignant masses with 3D automated breast ultrasound (ABUS).

Jung Min Chang1, Woo Kyung Moon, Nariya Cho, Jeong Seon Park, Seung Ja Kim.   

Abstract

OBJECTIVES: To retrospectively evaluate the detection performance of benign and malignant breast masses using 3D volume data obtained by ABUS and to determine lesion variables which affect detectability.
METHODS: Between November and December of 2007, bilateral whole breast US images were obtained using ABUS in 67 consecutive women who were scheduled to undergo US-guided needle biopsy due to suspicious breast masses. Twenty-four invasive ductal cancers in 23 breasts, 46 benign breast lesions in 44 breasts and 38 normal breasts were included. Three breast radiologists (experience range, 8-16 years) who did not perform the examinations and were blinded to the histology independently reviewed the ABUS data of the 105 breasts to detect suspicious solid masses with pathology as the standard of reference. Sensitivity and specificity in detecting benign and malignant masses were calculated, and lesion characteristics affecting detectability were analyzed.
RESULTS: Sensitivities for benign and malignant mass detections were 65.2% (30/46), 95.8% (23/24) for reader 1 (p=0.007), 66.7% (31/46), 87.5% (21/24) for reader 2 (p=0.087), and 56.3% (24/46), 91.7% (22/24), for reader 3 (p=0.001), respectively. Logistic analysis showed that mass size (odds ratio, 95% CI; 1.12, 1.02-1.24), surrounding tissue changes (odds ratio, 95% CI; 0.11, 0.02-0.47), and shape of the mass (odds ratio, 95% CI; 3.12, 1.02-9.55) were the variables associated with detectability at ABUS.
CONCLUSION: In reader studies using ABUS data, significantly higher sensitivity was noted for malignant breast masses than for benign masses.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Year:  2011        PMID: 21330080     DOI: 10.1016/j.ejrad.2011.01.074

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  11 in total

Review 1.  Diagnostic performance of the automated breast volume scanner: a systematic review of inter-rater reliability/agreement and meta-analysis of diagnostic accuracy for differentiating benign and malignant breast lesions.

Authors:  Zheying Meng; Cui Chen; Yitong Zhu; Shuling Zhang; Cong Wei; Bin Hu; Li Yu; Bing Hu; E Shen
Journal:  Eur Radiol       Date:  2015-04-28       Impact factor: 5.315

Review 2.  Automated breast ultrasound: basic principles and emerging clinical applications.

Authors:  Martina Zanotel; Iliana Bednarova; Viviana Londero; Anna Linda; Michele Lorenzon; Rossano Girometti; Chiara Zuiani
Journal:  Radiol Med       Date:  2017-08-28       Impact factor: 3.469

3.  Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images.

Authors:  Ji-Wook Jeong; Donghoon Yu; Sooyeul Lee; Jung Min Chang
Journal:  Healthc Inform Res       Date:  2016-10-31

4.  Objective breast tissue image classification using Quantitative Transmission ultrasound tomography.

Authors:  Bilal Malik; John Klock; James Wiskin; Mark Lenox
Journal:  Sci Rep       Date:  2016-12-09       Impact factor: 4.379

5.  Multimodal ultrasound tomography for breast imaging: a prospective study of clinical feasibility.

Authors:  S Forte; S Dellas; B Stieltjes; B Bongartz
Journal:  Eur Radiol Exp       Date:  2017-12-22

Review 6.  Automatic breast ultrasound: state of the art and future perspectives.

Authors:  Luca Nicosia; Federica Ferrari; Anna Carla Bozzini; Antuono Latronico; Chiara Trentin; Lorenza Meneghetti; Filippo Pesapane; Maria Pizzamiglio; Nicola Balesetreri; Enrico Cassano
Journal:  Ecancermedicalscience       Date:  2020-06-23

7.  False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients.

Authors:  Youngjune Kim; Jiwon Rim; Sun Mi Kim; Bo La Yun; So Yeon Park; Hye Shin Ahn; Bohyoung Kim; Mijung Jang
Journal:  Ultrasonography       Date:  2020-03-24

Review 8.  Evaluation of Diagnostic Performance of Automatic Breast Volume Scanner Compared to Handheld Ultrasound on Different Breast Lesions: A Systematic Review.

Authors:  Shahad A Ibraheem; Rozi Mahmud; Suraini Mohamad Saini; Hasyma Abu Hassan; Aysar Sabah Keiteb; Ahmed M Dirie
Journal:  Diagnostics (Basel)       Date:  2022-02-19

9.  Radiologists' performance for detecting lesions and the interobserver variability of automated whole breast ultrasound.

Authors:  Sung Hun Kim; Bong Joo Kang; Byung Gil Choi; Jae Jung Choi; Ji Hye Lee; Byung Joo Song; Byung Joo Choe; Sarah Park; Hyunbin Kim
Journal:  Korean J Radiol       Date:  2013-02-22       Impact factor: 3.500

10.  Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness.

Authors:  Jeoung Hyun Kim; Joo Hee Cha; Namkug Kim; Yongjun Chang; Myung-Su Ko; Young-Wook Choi; Hak Hee Kim
Journal:  Ultrasonography       Date:  2014-02-26
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