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Automated Prostate Cancer Localization with Multiparametric Magnetic Resonance Imaging

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Abdomen and Thoracic Imaging

Abstract

Prostate cancer is a leading cause of cancer death for men in the world. Fortunately, the survival rate for early-diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however, almost all studies are with human readers. There is a significant inter- and intra-observer variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method. We first perform tests to see the best performing combination of multiparametric MRI, then develop localization methods using cost-sensitive support vector machines (SVMs), and show that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM (C-SVM) results by incorporating spatial information. We test SVM, C-SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images and that using advanced methods such as C-SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM. We finally discuss potentially effective methods of localization using texture as the next steps of research.

Prostate cancer is a leading cause of cancer death for men in the world. Fortunately, the survival rate for early-diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however, almost all studies are with human readers. There is a significant inter- and intra-observer variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method. We first perform tests to see the best performing combination of multiparametric MRI, then develop localization methods using cost-sensitive support vector machines (SVMs), and show that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM (C-SVM) results by incorporating spatial information. We test SVM, C-SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images and that using advanced methods such as C-SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM. We finally discuss potentially effective methods of localization using texture as the next steps of research.

Work of Imam Samil Yetik is partially supported by Marie Curie COFUND/TUBITAK Cocirc grant number 112C011.

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Authors and Affiliations

  1. Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL, USA

    Yusuf Artan

  2. Department of Electrical and Electronics Engineering, TOBB Economy and Technological University, Ankara, Turkey

    Imam Samil Yetik

  3. Institute of Medical Science, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada

    Masoom A. Haider

Authors
  1. Yusuf Artan
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  2. Imam Samil Yetik
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  3. Masoom A. Haider
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Correspondence to Imam Samil Yetik .

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Editors and Affiliations

  1. Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA

    Ayman S. El-Baz

  2. University of Cagliari, Cagliari, Italy

    Luca Saba

  3. Biomedical Technologies, Inc Idaho State University (Affiliated), Roseville, California, USA

    Jasjit Suri

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Artan, Y., Yetik, I.S., Haider, M.A. (2014). Automated Prostate Cancer Localization with Multiparametric Magnetic Resonance Imaging. In: El-Baz, A., Saba, L., Suri, J. (eds) Abdomen and Thoracic Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8498-1_22

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  • DOI: https://doi.org/10.1007/978-1-4614-8498-1_22

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