Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: A preliminary study

Radiology

Volumn 286, Issue 3, 2018, Pages 887-896

  Yasaka, Koichiro ^a   Akai, Hiroyuki ^a   Abe, Osamu ^a   Kiryu, Shigeru ^a  

^a UNIVERSITY OF TOKYO HOSPITAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; CONTRAST ENHANCEMENT; CONTROLLED STUDY; DIAGNOSTIC VALUE; DIFFERENTIAL DIAGNOSIS; FEMALE; HUMAN; IMAGE PROCESSING; LEARNING ALGORITHM; LIVER CELL CARCINOMA; LIVER CYST; LIVER HEMANGIOMA; LIVER NODULE; LIVER TUMOR; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; TUMOR CLASSIFICATION; AGED; ARTIFICIAL NEURAL NETWORK; BILE DUCT CARCINOMA; BILE DUCT TUMOR; DIAGNOSTIC IMAGING; MIDDLE AGED; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; VERY ELDERLY; X-RAY COMPUTED TOMOGRAPHY;

CONTRAST MEDIUM;

AGED; AGED, 80 AND OVER; BILE DUCT NEOPLASMS; CARCINOMA, HEPATOCELLULAR; CHOLANGIOCARCINOMA; CONTRAST MEDIA; DIAGNOSIS, DIFFERENTIAL; FEMALE; HUMANS; LIVER NEOPLASMS; MACHINE LEARNING; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); RETROSPECTIVE STUDIES; ROC CURVE; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85042428409     PISSN: 00338419     EISSN: 15271315     Source Type: Journal    
DOI: 10.1148/radiol.2017170706     Document Type: Article

References (25)

1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136(5): E359-E386.
2. Van Hoe L, Baert AL, Gryspeerdt S, et al. Dual-phase helical CT of the liver: value of an early-phase acquisition in the differential diagnosis of noncystic focal lesions. AJR Am J Roentgenol 1997;168(5):1185-1192.
3. Nino-Murcia M, Olcott EW, Jeffrey RB Jr, Lamm RL, Beaulieu CF, Jain KA. Focal liver lesions: pattern-based classification scheme for enhancement at arterial phase CT. Radiology 2000;215(3):746-751.
4. van Leeuwen MS, Noordzij J, Feldberg MA, Hennipman AH, Doornewaard H. Focal liver lesions: characterization with triphasic spiral CT. Radiology 1996;201(2):327-336.
5. Itai Y, Ohtomo K, Kokubo T, et al. CT of hepatic masses: significance of prolonged and delayed enhancement. AJR Am J Roentgenol 1986;146(4):729-733.
6. Blachar A, Federle MP, Ferris JV, et al. Radiologists’ performance in the diagnosis of liver tumors with central scars by using specific CT criteria. Radiology 2002;223(2):532-539.
7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436-444.
8. Krizhevsky A, Sutskever I, Hinton G. Ima-geNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (NIPS 2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks. Published 2012. Accessed January 31, 2017.
9. Le QV, Ranzato M, Monga R, et al. Building high-level features using large scale unsupervised learning. International Conference on Machine Learning http://icml.cc/2012/papers. Published 2012. Accessed January 31, 2017.
10. Fukushima K, Miyake S. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognit 1982;15(6):455-469.
11. LeCun Y, Boser B, Denker JS, et al. Back-propagation applied to handwritten zip code recognition. Neural Comput 1989;1(4):541-551.
12. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278-2324.
13. Farabet C, Couprie C, Najman L, Lecun Y. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 2013;35(8):1915-1929.
14. Tompson J, Jain A, LeCun Y, Bregler C. Joint training of a convolutional network and a graphical model for human pose estimation. Advances in Neural Information Processing Systems 27 (NIPS 2014). https://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014. Published 2014. Accessed January 31, 2017.
15. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Cornell University Library. http://arxiv.org/abs/1409.4842. Published 2014. Accessed January 31, 2017.
16. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 2013;48(3): 452-458.
17. Mougiakakou SG, Valavanis IK, Nikita A, Nikita KS. Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 2007;41(1):25-37.
18. Huang YL, Chen JH, Shen WC. Diagnosis of hepatictumorswithtextureanalysisinnonen-hanced computed tomography images. Acad Radiol 2006;13(6):713-720.
19. Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inf Technol Biomed 2003;7(3):153-162.
20. Ye J, Sun Y, Wang S. Multi-phase CT image based hepatic lesion diagnosis by SVM. Biomedical Engineering and Informatics, 2009. http://ieeexplore.ieee.org/document/5304774. Published 2009. Accessed January 31, 2017.
21. Choi BI, Han JK, Hong SH, et al. Dysplastic nodules of the liver: imaging findings. Abdom Imaging 1999;24(3):250-257.
22. Sano K, Ichikawa T, Motosugi U, et al. Imaging study of early hepatocellular carcinoma: usefulness of gadoxetic acid-enhanced MR imaging. Radiology 2011;261(3):834-844.
23. Takayasu K, Furukawa H, Wakao F, et al. CT diagnosis of early hepatocellular carcinoma: sensitivity, findings, and CT-patho-logic correlation. AJR Am J Roentgenol 1995;164(4):885-890.
24. Jha RC, Mitchell DG, Weinreb JC, et al. LI-RADS categorization of benign and likely benign findings in patients at risk of hepatocellular carcinoma: a pictorial atlas. AJR Am J Roentgenol 2014;203(1):W48-W69.
25. Vieira S, Pinaya WH, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 2017;74(Pt A):58-75.