Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization

BackgroundHepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm.Methods63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures.ResultsComputed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0 cm was found for small-sized tumors.ConclusionsDifferences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small decrease for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

[1]  D. Woodfield Hepatocellular carcinoma. , 1986, The New Zealand medical journal.

[2]  Volodymyr Ponomaryov,et al.  Medical image processing using novel wavelet filters based on atomic functions: optimal medical image compression. , 2011, Advances in experimental medicine and biology.

[3]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[4]  Shadi AlZu'bi,et al.  Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation , 2011, Int. J. Biomed. Imaging.

[5]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[6]  Yaguang Chen,et al.  Multiresolution Medical Image Segmentation Based on Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[7]  M. Giger,et al.  Automatic segmentation of liver structure in CT images. , 1993, Medical physics.

[8]  Q Guihong,et al.  Medical image fusion by wavelet transform modulus maxima. , 2001, Optics express.

[9]  Xiaoying Wu,et al.  [An algorithm of a wavelet-based medical image quantization]. , 2002, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[10]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[11]  G. Dusheiko,et al.  Management of hepatocellular carcinoma. , 1992, Journal of hepatology.

[12]  H. El‐Serag,et al.  Surveillance for hepatocellular carcinoma: in whom and how? , 2011, Therapeutic advances in gastroenterology.

[13]  Stéphane Mallat,et al.  APPLIED MATHEMATICS MEETS SIGNAL PROCESSING , 2001 .

[14]  Alexander Hammers,et al.  Wavelet-based resolution recovery using anatomical prior provides quantitative recovery for human population phantom PET [ 11 C]raclopride data , 2011 .

[15]  T. Seo,et al.  Comparison of the methods for tumor response assessment in patients with hepatocellular carcinoma undergoing transarterial chemoembolization. , 2013, Journal of hepatology.

[16]  D. Chan,et al.  Comparing Hepatic Resection and Transarterial Chemoembolization for Barcelona Clinic Liver Cancer (BCLC) Stage B Hepatocellular Carcinoma: Change for Treatment of Choice? , 2010, World Journal of Surgery.

[17]  M Alvarez,et al.  Application of wavelets to the evaluation of phantom images for mammography quality control , 2012, Physics in medicine and biology.

[18]  Yozo Sato,et al.  Tumor response evaluation criteria for HCC (hepatocellular carcinoma) treated using TACE (transcatheter arterial chemoembolization): RECIST (response evaluation criteria in solid tumors) version 1.1 and mRECIST (modified RECIST): JIVROSG-0602 , 2013, Upsala journal of medical sciences.

[19]  J. Todd Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[20]  S. Mallat A wavelet tour of signal processing , 1998 .

[21]  O. Chutatape,et al.  Wavelet transform domain data embedding in a medical image , 2004, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Lau Wy Management of hepatocellular carcinoma. , 2002, Journal of the Royal College of Surgeons of Edinburgh.

[23]  Riccardo Lencioni,et al.  Modified RECIST (mRECIST) Assessment for Hepatocellular Carcinoma , 2010, Seminars in liver disease.

[24]  I Daubechies,et al.  Independent component analysis for brain fMRI does not select for independence , 2009 .

[25]  Klaus Markwardt WAVELET ANALYSIS AND FREQUENCY BAND DECOMPOSITIONS , 2006 .

[26]  Din-Chang Tseng,et al.  Wavelet-based medical image compression with adaptive prediction , 2005, 2005 International Symposium on Intelligent Signal Processing and Communication Systems.

[27]  Mette Jensen,et al.  Tumor volume in subcutaneous mouse xenografts measured by microCT is more accurate and reproducible than determined by 18F-FDG-microPET or external caliper , 2008, BMC Medical Imaging.

[28]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[29]  J. Ferlay,et al.  Globocan 2000 : cancer incidence, mortality and prevalence worldwide , 2001 .

[30]  L. Costaridou,et al.  Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. , 2007, The British journal of radiology.

[31]  Alexander Hammers,et al.  Wavelet-based resolution recovery using an anatomical prior provides quantitative recovery for human population phantom PET [11C]raclopride data , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[32]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[33]  ndrea,et al.  Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. , 1996, The New England journal of medicine.

[34]  J. Bruix,et al.  Prognosis of Hepatocellular Carcinoma: The BCLC Staging Classification , 1999, Seminars in liver disease.