Abstract
Delineation of hepatic tumours is challenging in CT due to limited inherent tissue contrast, leading to significant intra-/inter-observer variability. Perfusion CT (pCT) allows quantitative assessment of enhancement patterns in normal and abnormal liver. This study aims to develop a semi-automated perfusion analysis toolkit that classifies hepatic tissue based on perfusion-derived parameters. pCT data from patients with hepatic metastases were used in this study. Tumour motion was minimized through image registration; perfusion parameters were derived and then employed in the training of a machine learning algorithm used to classify hepatic tissue. This method was found to deliver promising results for 10 data sets, with recorded sensitivity and specificity of the tissue classification in the ranges of 0.92–0.99 and 0.98–0.99 respectively. This semi-automated method could be used to analyze response over the treatment course, as it is not based on intensity values.
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Acknowledgments
This research has been supported by the Cancer Research UK and EPSRC Cancer Imaging Centre at Oxford. A.C would like to acknowledge the Oxford EPSRC IAA funding. E.H. is grateful to Oxfordshire Health Services Research Committee and CRUK/ESPRC Imaging Centre for her clinical research fellowship. E.N. also wishes to acknowledge the support of the RCUK Digital Economy Programme (Oxford Centre for Doctoral Training in Healthcare Innovation).
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Naydenova, E., Cifor, A., Hill, E., Franklin, J., Sharma, R.A., Schnabel, J.A. (2014). A Semi-automated Toolkit for Analysis of Liver Cancer Treatment Response Using Perfusion CT. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham. https://doi.org/10.1007/978-3-319-13692-9_3
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