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Medical Image Computing for Oncology: Review and Clinical Examples

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Abstract

Medical imaging has been widely used in clinical and preclinical assessment of cancer. This chapter describes the techniques and components of medical image computing for oncological applications and introduces two representative applications of medical image computing in cancer. First, image computing tools such as volumetric MRI brain image segmentation are presented for computer-aided diagnosis and follow-up of glioblastoma multiforme (GBM) treatment. Then, using PET/CT imaging, quantitative monitoring of patients undergoing adoptive cytotoxic T lymphocytes (CTL) therapy is described. In addition, we also discuss that the trend in this area is to integrate microscopy imaging that captures cellular and tissue-level morphometry and molecular activities with medical images of organ and tissue-level properties to help better diagnose, subtype, and quantify the formulation, progression, and mechanisms of cancers.

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Abbreviations

AFINITI:

Assisted Follow-up in NeuroImaging of Therapeutic Intervention

BWH:

Brigham Women’s Hospital

CT:

Computed tomography

CTL:

Cytotoxic T-lymphocytes

DICOM:

Digital Imaging and Communications in Medicine

EBV:

Epstein Barr virus

FCM:

Fuzzy C-means

FDG:

Fluorodeoxyglucose

FFD:

Free-Form Deformations

FLAI:

Fluid-attenuated inversion recovery

GBM:

Glioblastoma multiforme

GUI:

Graphical user interface

HD:

Hodgkin's disease

ITK:

Insight Toolkit

LINA:

Longitudinal Image Navigation and Analysis

LMP:

Latent membrane protein

MRI:

Magnetic resonance imaging

NHL:

Non-Hodgkin’s lymphoma

PACS:

Picture Archiving and Communication Systems

PFS:

Progression free survival

PICE:

Prior Information Constrained Evolution

QI:

Quantitative Index

ROI:

Region of Interest

ROI:

Regions of interest

SMD:

Statistical model of deformation

SUV:

Standard uptake value

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Acknowledgements

The research is supported by NLM G08-LM008937, CPRIT RR100627, and JS Dunn Research Foundation. The authors would like to thank the contribution of Tiancheng He, Ph.D.; Po Su, M.E.; Ying Zhu, M.Sc.; Yong Zhang, Ph.D.; XinGao, Ph.D.; and Hai Li, Ph.D. in the development of AFINITI and LINA systems, as well as Geoffrey Young, M.D., of Brigham and Women’s Hospital in the consultation of AFINITI and Stephen Gottschalk and Catherine Bollard, M.D., of Texas Children Hospital in the consultation of LINA.

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  1. Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, 77030, USA

    Zhong Xue & Stephen T. C. Wong

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  1. Zhong Xue
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  2. Stephen T. C. Wong
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Correspondence to Stephen T. C. Wong .

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Editors and Affiliations

  1. Health Time Group, Jaén, Spain

    Antonio Luna

  2. Dept. of MRI, Clínica Girona, Girona, Spain

    Joan C. Vilanova

  3. Botafogo - Rio de Janeiro/RJ, Brazil

    L. Celso Hygino da Cruz Jr.

  4. Centro de Diagnóstico Dr. Enrique Rossi, Buenos Aires, Argentina

    Santiago E. Rossi

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Xue, Z., Wong, S.T.C. (2014). Medical Image Computing for Oncology: Review and Clinical Examples. In: Luna, A., Vilanova, J., Hygino da Cruz Jr., L., Rossi, S. (eds) Functional Imaging in Oncology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40412-2_6

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