Literature DB >> 27230095

The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images.

Mai Shibusawa1, Ryohei Nakayama2, Yuko Okanami3, Yumi Kashikura4, Nao Imai3, Takashi Nakamura5, Hiroko Kimura3, Masako Yamashita3, Noriko Hanamura3, Tomoko Ogawa3.   

Abstract

PURPOSE: The purpose of this study was to evaluate the usefulness of a computer-aided diagnosis (CAD) scheme for improving the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images.
METHODS: The database included 97 ultrasonographic images with hypoechoic areas: 48 benign cases [benign lesion with benign mammary tissue or fibrocystic disease (n = 20), fibroadenoma (n = 11), and intraductal papilloma (n = 17)] and 49 malignant cases [ductal carcinoma in situ (n = 17) and invasive ductal carcinoma (n = 32)]. Seven clinicians, three expert breast surgeons, and four general surgeons participated in the observer study. They were asked their confidence level concerning the possibility of malignancy in all 97 cases with and without the use of the CAD scheme. Receiver operating characteristic (ROC) analysis was performed to evaluate the usefulness of the CAD scheme.
RESULTS: The areas under the ROC curve (AUC) improved for all observers when they used the CAD scheme and increased from 0.649 to 0.783 (P = 0.0167). Notably, the AUC for the general surgeon group increased from 0.625 to 0.793 (P = 0.045).
CONCLUSIONS: This study showed that the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images was improved by the use of a CAD scheme.

Entities:  

Keywords:  Breast non-mass lesions; Breast ultrasonography; Computer-aided diagnosis

Mesh:

Year:  2016        PMID: 27230095     DOI: 10.1007/s10396-016-0718-9

Source DB:  PubMed          Journal:  J Med Ultrason (2001)        ISSN: 1346-4523            Impact factor:   1.314


  12 in total

1.  ROC analysis of detection of metastatic pulmonary nodules on digital chest radiographs with temporal subtraction.

Authors:  T Uozumi; K Nakamura; H Watanabe; H Nakata; S Katsuragawa; K Doi
Journal:  Acad Radiol       Date:  2001-09       Impact factor: 3.173

2.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method.

Authors:  D D Dorfman; K S Berbaum; C E Metz
Journal:  Invest Radiol       Date:  1992-09       Impact factor: 6.016

3.  Novel computer-aided diagnosis algorithms on ultrasound image: effects on solid breast masses discrimination.

Authors:  Ying Wang; Hong Wang; Yanhui Guo; Chunping Ning; Bo Liu; H D Cheng; Jiawei Tian
Journal:  J Digit Imaging       Date:  2009-11-10       Impact factor: 4.056

4.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.

Authors:  T Kobayashi; X W Xu; H MacMahon; C E Metz; K Doi
Journal:  Radiology       Date:  1996-06       Impact factor: 11.105

5.  Non-mass-like lesions on breast ultrasound: classification and correlation with histology.

Authors:  Zhi Li Wang; Nan Li; Min Li; Wen Bo Wan
Journal:  Radiol Med       Date:  2015-03-01       Impact factor: 3.469

6.  Improved differential diagnosis of breast masses on ultrasonographic images with a computer-aided diagnosis scheme for determining histological classifications.

Authors:  Yumi Kashikura; Ryohei Nakayama; Akiyoshi Hizukuri; Aya Noro; Yuki Nohara; Takashi Nakamura; Minori Ito; Hiroko Kimura; Masako Yamashita; Noriko Hanamura; Tomoko Ogawa
Journal:  Acad Radiol       Date:  2013-04       Impact factor: 3.173

7.  The role of contrast enhanced MRI in the diagnosis of non-mass image-forming lesions on breast ultrasonography.

Authors:  Keiichi Sotome; Yuki Yamamoto; Atsushi Hirano; Takeshi Takahara; Sayuri Hasegawa; Makoto Nakamaru; Akio Furukawa; Hiroshi Miyazaki; Kyoei Morozumi; Tatsuya Onishi; Yoichi Tanaka; Hisami Iri
Journal:  Breast Cancer       Date:  2007       Impact factor: 4.239

8.  Using sonography to screen women with mammographically dense breasts.

Authors:  Pavel Crystal; Selwyn D Strano; Semyon Shcharynski; Michael J Koretz
Journal:  AJR Am J Roentgenol       Date:  2003-07       Impact factor: 3.959

9.  Ultrasound screening of breast cancer.

Authors:  Eriko Tohno; Ei Ueno; Hiroshi Watanabe
Journal:  Breast Cancer       Date:  2008-11-14       Impact factor: 4.239

10.  Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial.

Authors:  Noriaki Ohuchi; Akihiko Suzuki; Tomotaka Sobue; Masaaki Kawai; Seiichiro Yamamoto; Ying-Fang Zheng; Yoko Narikawa Shiono; Hiroshi Saito; Shinichi Kuriyama; Eriko Tohno; Tokiko Endo; Akira Fukao; Ichiro Tsuji; Takuhiro Yamaguchi; Yasuo Ohashi; Mamoru Fukuda; Takanori Ishida
Journal:  Lancet       Date:  2015-11-05       Impact factor: 79.321

View more
  2 in total

1.  Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

Authors:  Miao Wu; Chuanbo Yan; Huiqiang Liu; Qian Liu
Journal:  Biosci Rep       Date:  2018-05-08       Impact factor: 3.840

2.  Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients.

Authors:  Mengsu Xiao; Chenyang Zhao; Jianchu Li; Jing Zhang; He Liu; Ming Wang; Yunshu Ouyang; Yixiu Zhang; Yuxin Jiang; Qingli Zhu
Journal:  Front Oncol       Date:  2020-07-07       Impact factor: 6.244

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.