Literature DB >> 16416275

CAD for mammography: the technique, results, current role and further developments.

Ansgar Malich1, Dorothee R Fischer, Joachim Böttcher.   

Abstract

CAD systems, developed to assist the radiologist in the detection of suspicious lesions on mammograms, are currently controversially discussed. The highly sensitive detection of malignant structures including priors by CAD is linked with a low specific performance and a high rate of falsely positive markings. This causes controversial results regarding the effect of CAD systems for the diagnosing radiologist. This review aims to give an overview of the current literature, to state the currently discussed controversial results of CAD and to give an outlook on the next developments, which are not limited to senology, but include many other applications of CAD systems in radiology.

Mesh：

Year:  2006        PMID： 16416275     DOI： 10.1007/s00330-005-0089-x

Source DB:  PubMed          Journal:  Eur Radiol        ISSN： 0938-7994            Impact factor:   5.315


  58 in total

1.  Radiologist detection of microcalcifications with and without computer-aided detection: a comparative study.

Authors:  R F Brem; J M Schoonjans
Journal:  Clin Radiol       Date:  2001-02       Impact factor: 2.350

2.  Mammography with computer-aided detection: reproducibility assessment initial experience.

Authors:  Bin Zheng; Lara A Hardesty; William R Poller; Jules H Sumkin; Sara Golla
Journal:  Radiology       Date:  2003-05-20       Impact factor: 11.105

3.  Are unnecessary follow-up procedures induced by computer-aided diagnosis (CAD) in mammography? Comparison of mammographic diagnosis with and without use of CAD.

Authors:  Christiane Marx; Ansgar Malich; Mirjam Facius; Uta Grebenstein; Dieter Sauner; Stefan O R Pfleiderer; Werner A Kaiser
Journal:  Eur J Radiol       Date:  2004-07       Impact factor: 3.528

4.  The impact of technical conditions of X-ray imaging on reproducibility and precision of digital computer-assisted X-ray radiogrammetry (DXR).

Authors:  A Malich; J Boettcher; A Pfeil; D Sauner; J P Heyne; A Petrovitch; A Hansch; W Linss; W A Kaiser
Journal:  Skeletal Radiol       Date:  2004-10-09       Impact factor: 2.199

5.  Accurate segmentation and contrast measurement of microcalcifications in mammograms: a phantom study.

Authors:  W J Veldkamp; N Karssemeijer
Journal:  Med Phys       Date:  1998-07       Impact factor: 4.071

6.  Effect of NHS breast screening programme on mortality from breast cancer in England and Wales, 1990-8: comparison of observed with predicted mortality.

Authors:  R G Blanks; S M Moss; C E McGahan; M J Quinn; P J Babb
Journal:  BMJ       Date:  2000-09-16

7.  Impact of breast density on computer-aided detection for breast cancer.

Authors:  Rachel F Brem; Jeffrey W Hoffmeister; Jocelyn A Rapelyea; Gilat Zisman; Kevin Mohtashemi; Guarav Jindal; Martin P Disimio; Steven K Rogers
Journal:  AJR Am J Roentgenol       Date:  2005-02       Impact factor: 3.959

8.  Computer-aided detection in screening mammography: variability in cues.

Authors:  Jay A Baker; Joseph Y Lo; David M Delong; Carey E Floyd
Journal:  Radiology       Date:  2004-09-09       Impact factor: 11.105

9.  Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

Authors:  Mark A Helvie; Lubomir Hadjiiski; Erini Makariou; Heang-Ping Chan; Nicholas Petrick; Berkman Sahiner; Shih-Chung B Lo; Matthew Freedman; Dorit Adler; Janet Bailey; Caroline Blane; Donna Hoff; Karen Hunt; Lynn Joynt; Katherine Klein; Chintana Paramagul; Stephanie K Patterson; Marilyn A Roubidoux
Journal:  Radiology       Date:  2004-02-27       Impact factor: 11.105

10.  Estimating the accuracy of screening mammography: a meta-analysis.

Authors:  A I Mushlin; R W Kouides; D E Shapiro
Journal:  Am J Prev Med       Date:  1998-02       Impact factor: 5.043
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  10 in total

Review 1.  Digital mammography: what do we and what don't we know?

Authors:  Ulrich Bick; Felix Diekmann
Journal:  Eur Radiol       Date:  2007-02-14       Impact factor: 5.315

Review 2.  [Workflow in digital screening mammography].

Authors:  U Bick; F Diekmann; E M Fallenberg
Journal:  Radiologe       Date:  2008-04       Impact factor: 0.635

3.  Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.

Authors:  Thijs Kooi; Nico Karssemeijer
Journal:  J Med Imaging (Bellingham)       Date:  2017-10-10

4.  Mammographic features and histopathological findings of interval breast cancers.

Authors:  S Hofvind; B Geller; P Skaane
Journal:  Acta Radiol       Date:  2008-11       Impact factor: 1.990

5.  Role of computer-aided detection in very small screening detected invasive breast cancers.

Authors:  Xavier Bargalló; Martín Velasco; Gorane Santamaría; Montse Del Amo; Pedro Arguis; Sonia Sánchez Gómez
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

6.  Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study.

Authors:  Carla Guerriero; Maureen G C Gillan; John Cairns; Matthew G Wallis; Fiona J Gilbert
Journal:  BMC Health Serv Res       Date:  2011-01-17       Impact factor: 2.655

7.  Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

Authors:  Jinhua Wang; Xi Yang; Hongmin Cai; Wanchang Tan; Cangzheng Jin; Li Li
Journal:  Sci Rep       Date:  2016-06-07       Impact factor: 4.379

8.  ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.

Authors:  Deepraj Chowdhury; Anik Das; Ajoy Dey; Shreya Sarkar; Ashutosh Dhar Dwivedi; Raghava Rao Mukkamala; Lakhindar Murmu
Journal:  Sensors (Basel)       Date:  2022-01-22       Impact factor: 3.576

9.  Variable size computer-aided detection prompts and mammography film reader decisions.

Authors:  Fiona J Gilbert; Susan M Astley; Caroline Rm Boggis; Magnus A McGee; Pamela M Griffiths; Stephen W Duffy; Olorunsola F Agbaje; Maureen Gc Gillan; Mary Wilson; Anil K Jain; Nicola Barr; Ursula M Beetles; Miriam A Griffiths; Jill Johnson; Rita M Roberts; Heather E Deans; Karen A Duncan; Geeta Iyengar
Journal:  Breast Cancer Res       Date:  2008-08-25       Impact factor: 6.466

10.  Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

Authors:  Hwejin Jung; Bumsoo Kim; Inyeop Lee; Minhwan Yoo; Junhyun Lee; Sooyoun Ham; Okhee Woo; Jaewoo Kang
Journal:  PLoS One       Date:  2018-09-18       Impact factor: 3.240
  10 in total

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