Abstract
Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding that patient undergo any medical invasive procedures such as biopsies. The individuation and characterization of Regions of Interest (ROIs) containing lesions is an important phase that enables an easier classification between two classes of HCCs. Two phases are needed for the individuation of lesioned ROIs: a liver isolation in each CT slice, and a lesion segmentation. Ultimately, all individuated ROIs are described by morphological features and, finally, a feed-forward supervised Artificial Neural Network (ANN) is used to classify them. Testing determined that the ANN topologies found through an evolutionary strategy showed a high generalization on the mean performance indices regardless of applied training, validation and test sets, showing good performances in terms of both accuracy and sensitivity, permitting a correct grading of HCC lesions.
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References
Ferenci, P., Fried, M., Labrecque, D., Bruix, J., Sherman, M., Omata, M., Heathcote, J., Piratsivuth, T., Kew, M., Otegbayo, J.A., et al.: Hepatocellular carcinoma (HCC): a global perspective. J. Clin. Gastroenterol. 44(4), 239–245 (2010)
Llovet, J.M., Burroughs, A., Bruix, J.: Hepatocellular carcinoma. The Lancet 362(9399), 1907 (2003)
Bartolozzi, C., Crocetti, L., Lencioni, R., Cioni, D., Della Pina, C., Campani, D.: Biliary and reticuloendothelial impairment in hepatocarcinogenesis: the diagnostic role of tissue-specific MR contrast media. Eur. Radiol. 17(10), 2519–2530 (2007)
Sanyal, A.J., Yoon, S.K., Lencioni, R.: The etiology of hepatocellular carcinoma and consequences for treatment. Oncologist 15(Suppl. 4), 14–22 (2010)
Kudo, M.: Multistep human hepatocarcinogenesis: correlation of imaging with pathology. J. Gastroenterol. 44(19), 112–118 (2009)
Kudo, M., Chung, H., Haji, S., Osaki, Y., Oka, H., Seki, T., Kasugai, H., Sasaki, Y., Matsunaga, T.: Validation of a new prognostic staging system for hepatocellular carcinoma: the JIS score compared with the CLIP score. Hepatology 40(6), 1396–1405 (2004)
Memeo, M., Stabile Ianora, A.A., Scardapane, A., Suppressa, P., Cirulli, A., Sabba, C., Rotondo, A., Angelelli, G.: Hereditary haemorrhagic telangiectasia study of hepatic vascular alterations with multi-detector row helical CT and reconstruction programs. La Radiologia Medica 109(1–2), 125–138 (2004)
Stabile Ianora, A.A., Memeo, M., Scardapane, A., Rotondo, A., Angelelli, G.: Oral contrast-enhanced three-dimensional helical-CT cholangiography: clinical applications. Eur. Radiol. 13(4), 867–873 (2003)
Choi, J.-Y., Lee, J.-M., Sirlin, C.B.: CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part II. Extracellular agents, hepatobiliary agents, and ancillary imaging features. Radiology 273(1), 30–50 (2014)
Bruix, J., Sherman, M.: Management of hepatocellular carcinoma. Hepatology 42(5), 1208–1236 (2005)
Kleihues, P., Sobin, L.H.: World health organization classification of tumors. Cancer 88(12), 2887 (2000)
Edmondson, H.A., Steiner, P.E.: Primary carcinoma of the liver. A study of 100 cases among 48,900 necropsies. Cancer 7(3), 462–503 (1954)
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)
Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Soh, L.-K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)
Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)
Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427–436 (2008)
He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328. IEEE (2008)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Bevilacqua, V., Pietroleonardo, N., Triggiani, V., Gesualdo, L., Palma, A.M., Rossini, M., Dalfino, G., Mastrofilippo, N.: Neural network classification of blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS (LNAI), vol. 9227, pp. 759–765. Springer, Cham (2015). doi:10.1007/978-3-319-22053-6_81
Zill, D., Wright, W.S., Cullen, M.R.: Advanced Engineering Mathematics. Jones & Bartlett Learning, Burlington (2011)
Bevilacqua, V., Mastronardi, G., Menolascina, F., Pannarale, P., Pedone, A.: A novel multi-objective genetic algorithm approach to artificial neural network topology optimisation: the breast cancer classification problem. In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 1958–1965. IEEE (2006)
Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo, M., Moschetta, M.: An optimized feed-forward artificial neural network topology to support radiologists in breast lesions classification. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 1385–1392. ACM (2016)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
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Bevilacqua, V. et al. (2017). Synthesis of a Neural Network Classifier for Hepatocellular Carcinoma Grading Based on Triphasic CT Images. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_32
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