Abstract
Liver Imaging Reporting Data System (LI-RADS) aims to standardize liver lesion imaging findings and diagnostic reports, and it is used as an accurate noninvasive diagnosis and staging method of hepatocellular carcinoma (HCC) nowadays. In this study, we proposed several computerized features for LI-RADS based computer-aided diagnosis of liver lesions. We used several popular machining learning approaches for computerized LI-RADS classification (benign and malignant classification) with our proposed features. The performance of each method was evaluated by using ROC curve and the best AUC score was 0.965 reached by the gradient boosting classifier.
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References
An, C., Rakhmonova, G., Choi, J.Y., Kim, M.J.: Liver imaging reporting and data system (LI-RADS) version 2014: understanding and application of the diagnostic algorithm. J. Clin. Mol. Hepatol. 22(2), 296–307 (2016)
The American College of Radiology. https://nrdr.acr.org/lirads/
Mitchell, D.G., Bruix, J., Sherman, M., Sirlin, C.B.: LI-RADS (liver imaging reporting and data system): summary, discussion, and consensus of the LI-RADS management working group and future directions. J. Hepatol. 61(3), 1056–1065 (2014)
Ehman, E.C., Behr, S.C., Umetsu, S.E., Fidelman, N., Yeh, B.M., Ferrell, L.D., Hope, T.A.: Rate of observation and inter-observer agreement for LI-RADS major features at CT and MRI in 184 pathology proven hepatocellular carcinomas. J. Abdom. Radiol. 41(5), 963–969 (2016)
Clark, T.J., Mcneeley, M.F., Maki, J.H.: Design and implementation of handheld and desktop software for the structured reporting of hepatic masses using the LI-RADS schema. J. Acad. Radiol. 21(4), 491–506 (2014)
Shan, J., Alam, S.K., Garra, B., Zhang, Y., Ahmed, T.: Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. J. Ultrasound Med. Biol. 42(4), 980 (2016)
Jha, R.C., Mitchell, D.G., Weinreb, J.C., Santillan, C.S., Yeh, B.M., Francois, R., Sirlin, C.B.: LI-RADS categorization of benign and likely benign findings in patients at risk of hepatocellular carcinoma: a pictorial atlas. Am. J. Roentgenol. 203(1), 48–69 (2014)
Khan, A.S., Hussain, H.K., Johnson, T.D., Weadock, W.J., Pelletier, S.J., Marrero, J.A.: Value of delayed hypointensity and delayed enhancing rim in magnetic resonance imaging diagnosis of small hepatocellular carcinoma in the cirrhotic liver. J. Magn. Reson. Imaging 32(2), 360–366 (2010)
Marrero, J.A., Hussain, H.K., Nghiem, H.V., Umar, R., Fontana, R.J., Lok, A.S.: Improving the prediction of hepatocellular carcinoma in cirrhotic patients with an arterially-enhancing liver mass. J. Liver Transplant. 11(3), 281–289 (2005)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Acknowledgement
This research was supported in part by the National Key Basic Research Program of China (973 Grant No. 2015CB352400), in part by the National Key Research and Development Program of China under the Grant No. 2016YFB1200203-03, in part by the Recruitment Program of Global Experts (HAIOU Program) from Zhejiang Province, China, in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 15H01130, No. 15K00253 and No. 16H01436.
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Chen, M. et al. (2018). Computerized Features for LI-RADS Based Computer-Aided Diagnosis of Liver Lesions. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_16
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DOI: https://doi.org/10.1007/978-3-319-59397-5_16
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