Literature DB >> 20077046

CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects.

F Fraioli1, G Serra, R Passariello.   

Abstract

Computer-aided detection (CAD) systems allow the automatic identification of lung nodules on chest computed tomography (CT), providing a second opinion to the radiologist's judgement and a volumetric evaluation of lesions - a very important aspect in oncological patients. The natural evolution of these systems has led to the introduction of computer-aided diagnosis (CADx) systems, which are able not only to identify nodules but also to characterise them by determining a likelihood of malignancy or benignity. The aim of this article is to describe the main technical principles of CAD and CADx systems, their applicability and influence in clinical practice and new prospects for their future development.

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Year:  2010        PMID: 20077046     DOI: 10.1007/s11547-010-0507-2

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  42 in total

1.  Computerized detection of pulmonary nodules on CT scans.

Authors:  S G Armato; M L Giger; C J Moran; J T Blackburn; K Doi; H MacMahon
Journal:  Radiographics       Date:  1999 Sep-Oct       Impact factor: 5.333

2.  Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

Authors:  K Nakamura; H Yoshida; R Engelmann; H MacMahon; S Katsuragawa; T Ishida; K Ashizawa; K Doi
Journal:  Radiology       Date:  2000-03       Impact factor: 11.105

3.  Work-up of the solitary pulmonary nodule. American College of Radiology. ACR Appropriateness Criteria.

Authors:  C I Henschke; D Yankelevitz; J Westcott; S D Davis; H Fleishon; W B Gefter; T C McLoud; R D Pugatch; H D Sostman; I Tocino; C S White; F R Bode; S J Swensen
Journal:  Radiology       Date:  2000-06       Impact factor: 11.105

4.  Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility.

Authors:  Dag Wormanns; Gerhard Kohl; Ernst Klotz; Anke Marheine; Florian Beyer; Walter Heindel; Stefan Diederich
Journal:  Eur Radiol       Date:  2003-11-13       Impact factor: 5.315

5.  Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists.

Authors:  M Das; G Mühlenbruch; S Heinen; A H Mahnken; M Salganicoff; S Stanzel; R W Günther; J E Wildberger
Journal:  Br J Radiol       Date:  2008-11       Impact factor: 3.039

6.  Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society.

Authors:  J H Austin; N L Müller; P J Friedman; D M Hansell; D P Naidich; M Remy-Jardin; W R Webb; E A Zerhouni
Journal:  Radiology       Date:  1996-08       Impact factor: 11.105

Review 7.  Tumor angiogenesis: tutorial on implications for imaging.

Authors:  T J Passe; D A Bluemke; S S Siegelman
Journal:  Radiology       Date:  1997-06       Impact factor: 11.105

8.  A general presentation of artificial neural networks. I.

Authors:  M Buscema
Journal:  Subst Use Misuse       Date:  1997-01       Impact factor: 2.164

9.  Volumetric evaluation of therapy response in patients with lung metastases. Preliminary results with a computer system (CAD) and comparison with unidimensional measurements.

Authors:  F Fraioli; L Bertoletti; A Napoli; F A Calabrese; R Masciangelo; E Cortesi; C Catalano; R Passariello
Journal:  Radiol Med       Date:  2006-04-11       Impact factor: 3.469

10.  Contrast-enhanced dynamic computed tomography for the evaluation of tumor angiogenesis in patients with lung carcinoma.

Authors:  Ukihide Tateishi; Masahiko Kusumoto; Hiroshi Nishihara; Kazuo Nagashima; Toshiaki Morikawa; Noriyuki Moriyama
Journal:  Cancer       Date:  2002-08-15       Impact factor: 6.860

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  13 in total

1.  An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management.

Authors:  Christopher G Slatore; Nanda Horeweg; James R Jett; David E Midthun; Charles A Powell; Renda Soylemez Wiener; Juan P Wisnivesky; Michael K Gould
Journal:  Am J Respir Crit Care Med       Date:  2015-08-15       Impact factor: 21.405

2.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

3.  Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

Authors:  Corbin A Cunningham; Trafton Drew; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2017-02       Impact factor: 2.199

Review 4.  Screening for early stage lung cancer and its correlation with lung nodule detection.

Authors:  Fangfei Qian; Wenjia Yang; Qunhui Chen; Xueyan Zhang; Baohui Han
Journal:  J Thorac Dis       Date:  2018-04       Impact factor: 2.895

Review 5.  Lung nodule and cancer detection in computed tomography screening.

Authors:  Geoffrey D Rubin
Journal:  J Thorac Imaging       Date:  2015-03       Impact factor: 3.000

6.  Fate of pulmonary nodules detected by computer-aided diagnosis and physician review on the computed tomography simulation images for hepatocellular carcinoma.

Authors:  Hyojung Park; Jin-Sung Kim; Hee Chul Park; Dongryul Oh
Journal:  Radiat Oncol J       Date:  2014-09-30

7.  Mathematical morphology-based approach to the enhancement of morphological features in medical images.

Authors:  Yoshitaka Kimori
Journal:  J Clin Bioinforma       Date:  2011-12-16

8.  A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm.

Authors:  Juanjuan Zhao; Guohua Ji; Yan Qiang; Xiaohong Han; Bo Pei; Zhenghao Shi
Journal:  PLoS One       Date:  2015-04-08       Impact factor: 3.240

9.  Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

Authors:  Kunwei Li; Kunfeng Liu; Yinghua Zhong; Mingzhu Liang; Peixin Qin; Haijun Li; Rongguo Zhang; Shaolin Li; Xueguo Liu
Journal:  Quant Imaging Med Surg       Date:  2021-08

10.  Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA.

Authors:  Huiyan Jiang; Di Zhao; Ruiping Zheng; Xiaoqi Ma
Journal:  Biomed Res Int       Date:  2015-10-12       Impact factor: 3.411

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