Literature DB >> 16493263

Advances in computer-aided diagnosis for breast cancer.

Lubomir Hadjiiski1, Berkman Sahiner, Heang-Ping Chan.   

Abstract

PURPOSE OF REVIEW: Computer-aided diagnosis (CAD) is a technology used for the detection and characterization of cancer. Although CAD is not limited to a single type of cancer, a large number of CAD systems to date have been designed and used for breast cancer. The aim of this review is to discuss the current state of the CAD systems for breast-cancer diagnosis, their application as a second reader in clinical practice, and studies that have evaluated the effect of CAD on radiologists' performance. RECENT
FINDINGS: A large number of CAD applications are being developed for different imaging modalities. Owing to commercially available Food and Drug Administration (FDA) approved systems, the main clinical use of CAD to date is for screen-film mammography. Many studies have shown that CAD improves radiologists' performance. A large number of academic institutions have devoted a substantial research effort to developing CAD methods.
SUMMARY: CAD systems will play an increasingly important role in the clinic as a second reader. Clinical trials have shown that CAD can improve the accuracy of breast-cancer detection. Preclinical studies have demonstrated the potential of CAD to improve the classification of malignant and benign lesions. An increased number of CAD systems are being developed for different breast-imaging modalities.

Entities:  

Mesh:

Year:  2006        PMID: 16493263      PMCID: PMC2800983          DOI: 10.1097/01.gco.0000192965.29449.da

Source DB:  PubMed          Journal:  Curr Opin Obstet Gynecol        ISSN: 1040-872X            Impact factor:   1.927


  52 in total

1.  The positive predictive value of mammography.

Authors:  D B Kopans
Journal:  AJR Am J Roentgenol       Date:  1992-03       Impact factor: 3.959

2.  Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study.

Authors:  Lubomir Hadjiiski; Heang-Ping Chan; Berkman Sahiner; Mark A Helvie; Marilyn A Roubidoux; Caroline Blane; Chintana Paramagul; Nicholas Petrick; Janet Bailey; Katherine Klein; Michelle Foster; Stephanie Patterson; Dorit Adler; Alexis Nees; Joseph Shen
Journal:  Radiology       Date:  2004-08-18       Impact factor: 11.105

Review 3.  Mammographic biopsy recommendations.

Authors:  D D Adler; M A Helvie
Journal:  Curr Opin Radiol       Date:  1992-10

4.  Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation.

Authors:  I Reiser; R M Nishikawa; M L Giger; T Wu; E Rafferty; R H Moore; D B Kopans
Journal:  Technol Cancer Res Treat       Date:  2004-10

5.  Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.

Authors:  Segyeong Joo; Yoon Seok Yang; Woo Kyung Moon; Hee Chan Kim
Journal:  IEEE Trans Med Imaging       Date:  2004-10       Impact factor: 10.048

6.  Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance.

Authors:  Sophie Paquerault; Laura M Yarusso; John Papaioannou; Yulei Jiang; Robert M Nishikawa
Journal:  Med Phys       Date:  2004-09       Impact factor: 4.071

7.  American College of Radiology guidelines for breast cancer screening.

Authors:  S A Feig; C J D'Orsi; R E Hendrick; V P Jackson; D B Kopans; B Monsees; E A Sickles; C B Stelling; M Zinninger; P Wilcox-Buchalla
Journal:  AJR Am J Roentgenol       Date:  1998-07       Impact factor: 3.959

8.  Periodic mammographic follow-up of probably benign lesions: results in 3,184 consecutive cases.

Authors:  E A Sickles
Journal:  Radiology       Date:  1991-05       Impact factor: 11.105

9.  Computer-aided detection in diagnostic mammography: detection of clinically unsuspected cancers.

Authors:  Sherry A Butler; Richard J Gabbay; Deborah A Kass; Daniel E Siedler; Kathryn F O'shaughnessy; Ronald A Castellino
Journal:  AJR Am J Roentgenol       Date:  2004-11       Impact factor: 3.959

10.  Computer-aided detection performance in mammographic examination of masses: assessment.

Authors:  David Gur; Jennifer S Stalder; Lara A Hardesty; Bin Zheng; Jules H Sumkin; Denise M Chough; Betty E Shindel; Howard E Rockette
Journal:  Radiology       Date:  2004-09-09       Impact factor: 11.105

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  8 in total

1.  Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT.

Authors:  Juhun Lee; Robert M Nishikawa; Ingrid Reiser; John M Boone; Karen K Lindfors
Journal:  Med Phys       Date:  2015-09       Impact factor: 4.071

2.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

Authors:  Benjamin Q Huynh; Hui Li; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-08-22

3.  Computer-aided diagnostic models in breast cancer screening.

Authors:  Turgay Ayer; Mehmet Us Ayvaci; Ze Xiu Liu; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Imaging Med       Date:  2010-06-01

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.  A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.

Authors:  Mohammad R Mohebian; Hamid R Marateb; Marjan Mansourian; Miguel Angel Mañanas; Fariborz Mokarian
Journal:  Comput Struct Biotechnol J       Date:  2016-12-06       Impact factor: 7.271

6.  On-Site Validation of a Microwave Breast Imaging System, before First Patient Study.

Authors:  Angie Fasoula; Luc Duchesne; Julio Daniel Gil Cano; Peter Lawrence; Guillaume Robin; Jean-Gael Bernard
Journal:  Diagnostics (Basel)       Date:  2018-08-18

7.  Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies.

Authors:  Olaide N Oyelade; Absalom E Ezugwu; Sunday A Adewuyi
Journal:  Neural Comput Appl       Date:  2021-10-08       Impact factor: 5.606

8.  Computer-aided assessment of diagnostic images for epidemiological research.

Authors:  Alison G Abraham; Donald D Duncan; Stephen J Gange; Sheila West
Journal:  BMC Med Res Methodol       Date:  2009-11-11       Impact factor: 4.615

  8 in total

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