Literature DB >> 20835372

Computer-aided diagnostic models in breast cancer screening.

Turgay Ayer1, Mehmet Us Ayvaci, Ze Xiu Liu, Oguzhan Alagoz, Elizabeth S Burnside.   

Abstract

Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.

Entities:  

Year:  2010        PMID: 20835372      PMCID: PMC2936490          DOI: 10.2217/IIM.10.24

Source DB:  PubMed          Journal:  Imaging Med        ISSN: 1755-5191


  71 in total

1.  Computer-aided diagnosis in radiology.

Authors:  Maryellen L Giger
Journal:  Acad Radiol       Date:  2002-01       Impact factor: 3.173

2.  Computerized diagnosis of breast lesions on ultrasound.

Authors:  Karla Horsch; Maryellen L Giger; Luz A Venta; Carl J Vyborny
Journal:  Med Phys       Date:  2002-02       Impact factor: 4.071

3.  Differences between computer-aided diagnosis of breast masses and that of calcifications.

Authors:  Mia K Markey; Joseph Y Lo; Carey E Floyd
Journal:  Radiology       Date:  2002-05       Impact factor: 11.105

4.  Construction of a Bayesian network for mammographic diagnosis of breast cancer.

Authors:  C E Kahn; L M Roberts; K A Shaffer; P Haddawy
Journal:  Comput Biol Med       Date:  1997-01       Impact factor: 4.589

5.  Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

Authors:  D R Chen; R F Chang; Y L Huang
Journal:  Radiology       Date:  1999-11       Impact factor: 11.105

6.  Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.

Authors:  Rich S Rana; Yulei Jiang; Robert A Schmidt; Robert M Nishikawa; Bei Liu
Journal:  Acad Radiol       Date:  2007-03       Impact factor: 3.173

7.  Use and misuse of the receiver operating characteristic curve in risk prediction.

Authors:  Nancy R Cook
Journal:  Circulation       Date:  2007-02-20       Impact factor: 29.690

Review 8.  CADx of mammographic masses and clustered microcalcifications: a review.

Authors:  Matthias Elter; Alexander Horsch
Journal:  Med Phys       Date:  2009-06       Impact factor: 4.071

9.  Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast.

Authors:  Botond K Szabó; Maria Kristoffersen Wiberg; Beata Boné; Peter Aspelin
Journal:  Eur Radiol       Date:  2004-03-18       Impact factor: 5.315

10.  Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Hon J Yu; Yong Chu; Orhan Nalcioglu; Min-Ying Su
Journal:  Acad Radiol       Date:  2008-12       Impact factor: 3.173

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  15 in total

1.  The role of informatics in health care reform.

Authors:  Yueyi I Liu; Daniel L Rubin
Journal:  Acad Radiol       Date:  2012-07-06       Impact factor: 3.173

2.  The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions.

Authors:  Mehmet U S Ayvaci; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Manuf Serv Oper Manag       Date:  2012-04       Impact factor: 7.600

3.  A new hybrid case-based reasoning approach for medical diagnosis systems.

Authors:  Dina A Sharaf-El-Deen; Ibrahim F Moawad; M E Khalifa
Journal:  J Med Syst       Date:  2014-01-28       Impact factor: 4.460

Review 4.  Digital Analysis in Breast Imaging.

Authors:  Giovanna Negrão de Figueiredo; Michael Ingrisch; Eva Maria Fallenberg
Journal:  Breast Care (Basel)       Date:  2019-06-04       Impact factor: 2.860

5.  Computer-aided classification of mammographic masses using visually sensitive image features.

Authors:  Yunzhi Wang; Faranak Aghaei; Ali Zarafshani; Yuchen Qiu; Wei Qian; Bin Zheng
Journal:  J Xray Sci Technol       Date:  2017       Impact factor: 1.535

Review 6.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Authors:  Sanjay Saxena; Biswajit Jena; Neha Gupta; Suchismita Das; Deepaneeta Sarmah; Pallab Bhattacharya; Tanmay Nath; Sudip Paul; Mostafa M Fouda; Manudeep Kalra; Luca Saba; Gyan Pareek; Jasjit S Suri
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

Review 7.  Radiological images and machine learning: Trends, perspectives, and prospects.

Authors:  Zhenwei Zhang; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2019-02-27       Impact factor: 4.589

Review 8.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

9.  Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign.

Authors:  Kendra A Batchelder; Aaron B Tanenbaum; Seth Albert; Lyne Guimond; Pierre Kestener; Alain Arneodo; Andre Khalil
Journal:  PLoS One       Date:  2014-09-15       Impact factor: 3.240

10.  Application of deep learning in the detection of breast lesions with four different breast densities.

Authors:  Hongmei Li; Jing Ye; Hao Liu; Yichuan Wang; Binbin Shi; Juan Chen; Aiping Kong; Qing Xu; Junhui Cai
Journal:  Cancer Med       Date:  2021-06-16       Impact factor: 4.452

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