A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification
Alessandro Savino, Alfredo Benso, Stefano Di Carlo, Valentina Giannini, Anna Vignati, Gianfranco Politano, Simone Mazzetti, Daniele Regge
2014
Abstract
Prostate Cancer (PCa) is the most common solid neoplasm in males and a major cause of cancer-related death. Screening based on Prostate Specific Antigen (PSA) reduces the rate of death by 31%, but it is associated with a high risk of over-diagnosis and over-treatment. Prostate Magnetic Resonance Imaging (MRI) has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive detection tool. Research community effort focused on classification techniques based on MRI in order to produce a malignancy likelihood map. The paper describes the prototyping design, the implemented work-flow and the software architecture of a Computer Aided Diagnosis (CAD) software which aims at providing a comprehensive diagnostic tool, including an integrated classification stack, from a preliminary registration of images to the classification process. This software can improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the whole diagnostic work-up.
References
- 3DSlicer (2013). 3DSlicer Project. http://www.slicer.org.
- Baskaran, M., Ramanujam, J., and Sadayappan, P. (2010). Automatic c-to-cuda code generation for affine programs. In Gupta, R., editor, Compiler Construction, volume 6011 of Lecture Notes in Computer Science, pages 244-263. Springer Berlin Heidelberg.
- Betz, K., Leff, A., and Rayfield, J. (2000). Developing highly-responsive user interfaces with dhtml and servlets. In Performance, Computing, and Communications Conference, 2000. IPCCC 7800. Conference Proceeding of the IEEE International, pages 437-443.
- Dawes, B., Abrahams, D., and Rivera, R. (2013). Boost c++ libraries. [Available Online]: http://www.boost.org/doc/libs/.
- Digia plc (2013). QT Project. http://qt-project.org/.
- Ferlay, J., Steliarova-Foucher, E., Lortet-Tieulent, J., Rosso, S., Coebergh, J., Comber, H., Forman, D., and Bray, F. (2013). Cancer incidence and mortality patterns in europe: Estimates for 40 countries in 2012. European Journal of Cancer, 49(6):1374 - 1403.
- Glockner, J. F., Hu, H. H., Stanley, D. W., Angelos, L., and King, K. (2005). Parallel mr imaging: A user's guide1. Radiographics, 25(5):1279-1297.
- Hegde, J., Mulkern, R., Panych, L., Fennessy, F., Fedorov, A., Maier, S., and Tempany, C. (2013). Multiparametric mri of prostate cancer: An update on state-of-theart techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging, 37(5):1035-1054.
- Hoeks, C. M. A., Barentsz, J. O., Hambrock, T., Yakar, D., Somford, D. M., Heijmink, S. W. T. P. J., Scheenen, T. W. J., Vos, P. C., Huisman, H., van Oort, I. M., Witjes, J. A., Heerschap, A., and F ütterer, J. J. (2011). Prostate cancer: Multiparametric mr imaging for detection, localization, and staging. Radiology, 261(1):46-66.
- Invivo (2013). DynaCAD by Invivo Coorporation. http:// www.invivocorp.com/avs/prostate.php.
- Kitware (2013a). ITK Project. http://www.itk.org/ITK/ project/project.html.
- Kitware (2013b). Visualization Toolkit (VTK) Project. http://www.vtk.org/.
- Liu, X., Langer, D., Haider, M., Yang, Y., Wernick, M., and Yetik, I. (2009). Prostate cancer segmentation with simultaneous estimation of markov random field parameters and class. Medical Imaging, IEEE Transactions on, 28(6):906-915.
- Lujan, M., Paez, A., Santonja, C., Pascual, T., Fernandez, I., and Berenguer, A. (2004). Prostate cancer detection and tumor characteristics in men with multiple biopsy sessions. Prostate Cancer Prostatic Dis, 7(3):238- 242.
- Padhani, A. R., Koh, D.-M., and Collins, D. J. (2011). Whole-body diffusion-weighted mr imaging in cancer: Current status and research directions. Radiology, 261(3):700-718.
- Paoin, W. and Boonchai-Apisit, P. (2009). Development of surgical operation data interchange model using xml and relational database. In Natural Language Processing, 2009. SNLP 7809. Eighth International Symposium on, pages 132-136.
- Penzkofer, T. and Tempany-Afdhal, C. (2013). Prostate cancer detection and diagnosis: The role of mr and its comparison with other diagnostic modalities - a radiologist's perspective. NMR Biomed.
- Pieper, S., Lorensen, B., Schroeder, W., and Kikinis, R. (2006). The na-mic kit: Itk, vtk, pipelines, grids and 3d slicer as an open platform for the medical image computing community. In Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on, pages 698-701.
- Taghva, K. and Jayakumar, K. (2009). Xml based implementation of a bibliographic database and recursive queries. In Information Technology: New Generations, 2009. ITNG 7809. Sixth International Conference on, pages 1073-1078.
- , B., Bernardo, M., Merino, M. J., Wood, B. J., Pinto, P. A., and Choyke, P. L. (2012). Mri of localized prostate cancer: coming of age in the psa era. Diagnostic and Interventional Radiology, 18:34-45.
- Vasanawala, S., Murphy, M., Alley, M., Lai, P., Keutzer, K., Pauly, J., and Lustig, M. (2011). Practical parallel imaging compressed sensing mri: Summary of two years of experience in accelerating body mri of pediatric patients. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 1039-1043.
- Vos, P. C., Barentsz, J. O., Karssemeijer, N., and Huisman, H. J. (2012). Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Physics in Medicine and Biology, 57(6):1527.
Paper Citation
in Harvard Style
Savino A., Benso A., Di Carlo S., Giannini V., Vignati A., Politano G., Mazzetti S. and Regge D. (2014). A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification . In Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2014) ISBN 978-989-758-014-7, pages 49-54. DOI: 10.5220/0004799100490054
in Bibtex Style
@conference{bioimaging14,
author={Alessandro Savino and Alfredo Benso and Stefano Di Carlo and Valentina Giannini and Anna Vignati and Gianfranco Politano and Simone Mazzetti and Daniele Regge},
title={A Prostate Cancer Computer Aided Diagnosis Software including
Malignancy Tumor Probabilistic Classification},
booktitle={Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2014)},
year={2014},
pages={49-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004799100490054},
isbn={978-989-758-014-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2014)
TI - A Prostate Cancer Computer Aided Diagnosis Software including
Malignancy Tumor Probabilistic Classification
SN - 978-989-758-014-7
AU - Savino A.
AU - Benso A.
AU - Di Carlo S.
AU - Giannini V.
AU - Vignati A.
AU - Politano G.
AU - Mazzetti S.
AU - Regge D.
PY - 2014
SP - 49
EP - 54
DO - 10.5220/0004799100490054