Literature DB >> 29665706

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review.

Emilie L Henriksen1,2, Jonathan F Carlsen1, Ilse Mm Vejborg1, Michael B Nielsen1, Carsten A Lauridsen1,2.   

Abstract

BACKGROUND: Early detection of breast cancer (BC) is crucial in lowering the mortality.
PURPOSE: To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD.
MATERIAL AND METHODS: PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included.
RESULTS: All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR.
CONCLUSION: All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.

Entities:  

Keywords:  Computer-aided detection; breast cancer; diagnostic accuracy; early detection; mammography screening

Mesh:

Year:  2018        PMID: 29665706     DOI: 10.1177/0284185118770917

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  16 in total

Review 1.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

Review 2.  X-ray-Based 3D Virtual Histology-Adding the Next Dimension to Histological Analysis.

Authors:  J Albers; S Pacilé; M A Markus; M Wiart; G Vande Velde; G Tromba; C Dullin
Journal:  Mol Imaging Biol       Date:  2018-10       Impact factor: 3.488

3.  Visual search in breast imaging.

Authors:  Ziba Gandomkar; Claudia Mello-Thoms
Journal:  Br J Radiol       Date:  2019-07-18       Impact factor: 3.039

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.  Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.

Authors:  Victor Dahlblom; Ingvar Andersson; Kristina Lång; Anders Tingberg; Sophia Zackrisson; Magnus Dustler
Journal:  Radiol Artif Intell       Date:  2021-09-01

6.  Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system.

Authors:  Panpan Zhang; Zhaosheng Ma; Yingtao Zhang; Xiaodan Chen; Gang Wang
Journal:  Gland Surg       Date:  2021-07

7.  Deep learning for automated detection and numbering of permanent teeth on panoramic images.

Authors:  Mohamed Estai; Marc Tennant; Dieter Gebauer; Andrew Brostek; Janardhan Vignarajan; Maryam Mehdizadeh; Sajib Saha
Journal:  Dentomaxillofac Radiol       Date:  2021-10-13       Impact factor: 2.419

Review 8.  A Survey on Human Cancer Categorization Based on Deep Learning.

Authors:  Ahmad Ibrahim; Hoda K Mohamed; Ali Maher; Baochang Zhang
Journal:  Front Artif Intell       Date:  2022-06-27

9.  Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.

Authors:  Rebecca Sawyer Lee; Jared A Dunnmon; Ann He; Siyi Tang; Christopher Ré; Daniel L Rubin
Journal:  J Biomed Inform       Date:  2020-12-11       Impact factor: 6.317

10.  Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Authors:  Abdul Rahman Diab; Bryan Haslam; Jiye G Kim; William Lotter; Giorgia Grisot; Eric Wu; Kevin Wu; Jorge Onieva Onieva; Yun Boyer; Jerrold L Boxerman; Meiyun Wang; Mack Bandler; Gopal R Vijayaraghavan; A Gregory Sorensen
Journal:  Nat Med       Date:  2021-01-11       Impact factor: 87.241

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