Literature DB >> 11526283

Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications.

Y Jiang1, R M Nishikawa, R A Schmidt, A Y Toledano, K Doi.   

Abstract

PURPOSE: To evaluate whether computer-aided diagnosis can reduce interobserver variability in the interpretation of mammograms.
MATERIALS AND METHODS: Ten radiologists interpreted mammograms showing clustered microcalcifications in 104 patients. Decisions for biopsy or follow-up were made with and without a computer aid, and these decisions were compared. The computer was used to estimate the likelihood that a microcalcification cluster was due to a malignancy. Variability in the radiologists' recommendations for biopsy versus follow-up was then analyzed.
RESULTS: Variation in the radiologists' accuracy, as measured with the SD of the area under the receiver operating characteristic curve, was reduced by 46% with computer aid. Access to the computer aid increased the agreement among all observers from 13% to 32% of the total cases (P <.001), while the kappa value increased from 0.19 to 0.41 (P <.05). Use of computer aid eliminated two-thirds of the substantial disagreements in which two radiologists recommended biopsy and routine screening in the same patient (P <.05).
CONCLUSION: In addition to its demonstrated potential to improve diagnostic accuracy, computer-aided diagnosis has the potential to reduce the variability among radiologists in the interpretation of mammograms.

Entities:  

Mesh:

Year:  2001        PMID: 11526283     DOI: 10.1148/radiol.220001257

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


  22 in total

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6.  Analysis of perceived similarity between pairs of microcalcification clusters in mammograms.

Authors:  Juan Wang; Hao Jing; Miles N Wernick; Robert M Nishikawa; Yongyi Yang
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

7.  Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks.

Authors:  Yohan Sumathipala; Nathan Lay; Baris Turkbey; Clayton Smith; Peter L Choyke; Ronald M Summers
Journal:  J Med Imaging (Bellingham)       Date:  2018-12-15

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Authors:  Neha Bhooshan; Maryellen Giger; Darrin Edwards; Yading Yuan; Sanaz Jansen; Hui Li; Li Lan; Husain Sattar; Gillian Newstead
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9.  Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.

Authors:  Weijie Chen; Maryellen L Giger; Gillian M Newstead; Ulrich Bick; Sanaz A Jansen; Hui Li; Li Lan
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10.  A Probabilistic Model to Support Radiologists' Classification Decisions in Mammography Practice.

Authors:  Jiaming Zeng; Francisco Gimenez; Elizabeth S Burnside; Daniel L Rubin; Ross Shachter
Journal:  Med Decis Making       Date:  2019-02-28       Impact factor: 2.583

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