Literature DB >> 31210612

Performance and Reading Time of Automated Breast US with or without Computer-aided Detection.

Shanling Yang1, Xican Gao1, Liwen Liu1, Rui Shu1, Jingru Yan1, Ge Zhang1, Yao Xiao1, Yan Ju1, Ni Zhao1, Hongping Song1.   

Abstract

BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.

Entities:  

Year:  2019        PMID: 31210612     DOI: 10.1148/radiol.2019181816

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  8 in total

1.  The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

Authors:  Xiao Luo PhD; Min Xu; Guoxue Tang; Yi Wang PhD; Na Wang; Dong Ni PhD; Xi Lin PhD; An-Hua Li
Journal:  Br J Radiol       Date:  2021-12-15       Impact factor: 3.039

2.  Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system.

Authors:  Panpan Zhang; Zhaosheng Ma; Yingtao Zhang; Xiaodan Chen; Gang Wang
Journal:  Gland Surg       Date:  2021-07

3.  Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

Authors:  Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng
Journal:  Eur Radiol       Date:  2022-04-30       Impact factor: 7.034

Review 4.  The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

Authors:  Tomoyuki Fujioka; Mio Mori; Kazunori Kubota; Jun Oyama; Emi Yamaga; Yuka Yashima; Leona Katsuta; Kyoko Nomura; Miyako Nara; Goshi Oda; Tsuyoshi Nakagawa; Yoshio Kitazume; Ukihide Tateishi
Journal:  Diagnostics (Basel)       Date:  2020-12-06

5.  Artificial intelligence in breast ultrasonography.

Authors:  Jaeil Kim; Hye Jung Kim; Chanho Kim; Won Hwa Kim
Journal:  Ultrasonography       Date:  2020-11-12

Review 6.  Medical imaging and nuclear medicine: a Lancet Oncology Commission.

Authors:  Hedvig Hricak; May Abdel-Wahab; Rifat Atun; Miriam Mikhail Lette; Diana Paez; James A Brink; Lluís Donoso-Bach; Guy Frija; Monika Hierath; Ola Holmberg; Pek-Lan Khong; Jason S Lewis; Geraldine McGinty; Wim J G Oyen; Lawrence N Shulman; Zachary J Ward; Andrew M Scott
Journal:  Lancet Oncol       Date:  2021-03-04       Impact factor: 41.316

7.  Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.

Authors:  Valentina Giannini; Simone Mazzetti; Giovanni Cappello; Valeria Maria Doronzio; Lorenzo Vassallo; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Journal:  Diagnostics (Basel)       Date:  2021-05-28

8.  Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks.

Authors:  Jeongmin Lee; Bong Joo Kang; Sung Hun Kim; Ga Eun Park
Journal:  Diagnostics (Basel)       Date:  2022-02-24
  8 in total

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