Literature DB >> 15559124

Computerized analysis of images in the detection and diagnosis of breast cancer.

Maryellen L Giger1.   

Abstract

Improvements in mammographic acquisition techniques have resulted in making the early signs of breast cancer more apparent on mammograms. However, the accuracy of the overall mammographic examination depends on both the quality of the mammographic images and the ability of the radiologist to interpret those images. While mammography is the best screening method for the early detection of breast cancer, radiologists do miss lesions on mammograms. Use of output, however, from a computerized analysis of an image by a radiologist may help him/her in the detection or diagnostic tasks, and potentially improve the overall interpretation of breast images and the subsequent patient care. Computer-aided detection and diagnosis (CAD) involves the application of computer technology to the process of medical image interpretation. CAD can be defined as a diagnosis made by a radiologist, who uses the output from a computerized analysis of medical images as a "second opinion" in detecting and diagnosing lesions, with the final diagnosis being made by the radiologist. The computer output must be at a sufficient performance level, and in addition, the output must be displayed in a user-friendly format for effective and efficient use by the radiologist. This chapter reviews CAD in breast cancer detection and diagnosis, including examples of image analyses, multi-modality approaches (i.e., special-view diagnostic mammography, ultrasound, and MRI), and means of communicating the computer output to the human.

Entities:  

Mesh:

Year:  2004        PMID: 15559124     DOI: 10.1053/j.sult.2004.07.003

Source DB:  PubMed          Journal:  Semin Ultrasound CT MR        ISSN: 0887-2171            Impact factor:   1.875


  8 in total

1.  Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.

Authors:  Swatee Singh; Jeff Maxwell; Jay A Baker; Jennifer L Nicholas; Joseph Y Lo
Journal:  Radiology       Date:  2010-10-22       Impact factor: 11.105

Review 2.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

Review 3.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

4.  Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy.

Authors:  Yuka Kuramoto; Natsumi Wada; Yoshikazu Uchiyama
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-01-12       Impact factor: 2.924

5.  Computer-aided diagnosis with radiogenomics: analysis of the relationship between genotype and morphological changes of the brain magnetic resonance images.

Authors:  Chiharu Kai; Yoshikazu Uchiyama; Junji Shiraishi; Hiroshi Fujita; Kunio Doi
Journal:  Radiol Phys Technol       Date:  2018-05-10

6.  Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.

Authors:  Hui Li; Maryellen L Giger; Yading Yuan; Weijie Chen; Karla Horsch; Li Lan; Andrew R Jamieson; Charlene A Sennett; Sanaz A Jansen
Journal:  Acad Radiol       Date:  2008-11       Impact factor: 3.173

Review 7.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

Review 8.  A review of explainable and interpretable AI with applications in COVID-19 imaging.

Authors:  Jordan D Fuhrman; Naveena Gorre; Qiyuan Hu; Hui Li; Issam El Naqa; Maryellen L Giger
Journal:  Med Phys       Date:  2021-12-07       Impact factor: 4.506

  8 in total

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