Image registration for quantitative parametric response mapping of cancer treatment response.

Imaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy.

[1]  Bradford A Moffat,et al.  The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. , 2006, Neoplasia.

[2]  C. V. D. van de Velde,et al.  Preoperative chemotherapy in primary operable breast cancer: results from the European Organization for Research and Treatment of Cancer trial 10902. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  Michael I Miga,et al.  Non-rigid registration of breast surfaces using the laplace and diffusion equations , 2010, Biomedical engineering online.

[4]  Timothy D Johnson,et al.  Parametric response mapping of CT images provides early detection of local bone loss in a rat model of osteoporosis. , 2012, Bone.

[5]  Steven P Sourbron,et al.  Classic models for dynamic contrast‐enhanced MRI , 2013, NMR in biomedicine.

[6]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[7]  M. Giger,et al.  Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. , 2013, Annual review of biomedical engineering.

[8]  Radhika Sivaramakrishna,et al.  Breast image registration techniques: a survey , 2006, Medical and Biological Engineering and Computing.

[9]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[10]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[11]  H. Iwata,et al.  Prognostic significance of subtype and pathologic response in operable breast cancer; a pooled analysis of prospective neoadjuvant studies of JBCRG , 2015, Breast Cancer.

[12]  C. Meyer,et al.  Evaluation of an automatic registration-based algorithm for direct measurement of volume change in tumors. , 2012, International journal of radiation oncology, biology, physics.

[13]  Timothy D Johnson,et al.  The parametric response map is an imaging biomarker for early cancer treatment outcome , 2009, Nature Medicine.

[14]  Max A. Viergever,et al.  Semi-automatic construction of reference standards for evaluation of image registration , 2011, Medical Image Anal..

[15]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[16]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.

[17]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[18]  Naoki Kagawa,et al.  Use of (11)C-methionine PET parametric response map for monitoring WT1 immunotherapy response in recurrent malignant glioma. , 2012, Journal of neurosurgery.

[19]  D C Barber,et al.  Automatic segmentation of medical images using image registration: diagnostic and simulation applications , 2005, Journal of medical engineering & technology.

[20]  B. Rapoport,et al.  When and How Do I Use Neoadjuvant Chemotherapy for Breast Cancer? , 2014, Current Treatment Options in Oncology.

[21]  J. Bryant,et al.  Paclitaxel after doxorubicin plus cyclophosphamide as adjuvant chemotherapy for node-positive breast cancer: results from NSABP B-28. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  T. Chenevert,et al.  Diffusion-Weighted MRI as a Biomarker of Tumor Radiation Treatment Response Heterogeneity: A Comparative Study of Whole-Volume Histogram Analysis versus Voxel-Based Functional Diffusion Map Analysis. , 2013, Translational oncology.

[23]  Benjamin M Ellingson,et al.  Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma. , 2012, Neuro-oncology.

[24]  Bradford A Moffat,et al.  Prospective Early Response Imaging Biomarker for Neoadjuvant Breast Cancer Chemotherapy , 2007, Clinical Cancer Research.

[25]  J. Ioannidis,et al.  Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. , 2005, Journal of the National Cancer Institute.

[26]  Thomas L Chenevert,et al.  Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements , 2014, Magnetic resonance in medicine.

[27]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[28]  Timothy D Johnson,et al.  Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[29]  Mark Holden,et al.  A Review of Geometric Transformations for Nonrigid Body Registration , 2008, IEEE Transactions on Medical Imaging.

[30]  Michael Brady,et al.  MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration , 2012, Medical Image Anal..

[31]  Bradford A Moffat,et al.  A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy. , 2009, Translational oncology.

[32]  Michael E Phelps,et al.  18F-FDOPA and 18F-FLT positron emission tomography parametric response maps predict response in recurrent malignant gliomas treated with bevacizumab. , 2012, Neuro-oncology.

[33]  Benjamin M Ellingson,et al.  Nonlinear registration of diffusion‐weighted images improves clinical sensitivity of functional diffusion maps in recurrent glioblastoma treated with bevacizumab , 2012, Magnetic resonance in medicine.

[34]  David J. Hawkes,et al.  Validation of nonrigid image registration using finite-element methods: application to breast MR images , 2003, IEEE Transactions on Medical Imaging.

[35]  H. Feigelson,et al.  Utilization of Neoadjuvant Chemotherapy Varies in the Treatment of Women with Invasive Breast Cancer , 2013, PloS one.

[36]  Thomas E Yankeelov,et al.  Clinically Relevant Modeling of Tumor Growth and Treatment Response , 2013, Science Translational Medicine.

[37]  Benoit M Dawant,et al.  A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response. , 2009, Magnetic resonance imaging.

[38]  Timothy D Johnson,et al.  Multi‐system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice‐water phantom , 2013, Journal of magnetic resonance imaging : JMRI.

[39]  M. Kessler.  Image registration and data fusion in radiation therapy. , 2006, The British journal of radiology.

[40]  Bing Ma,et al.  Voxel-by-Voxel Functional Diffusion Mapping for Early Evaluation of Breast Cancer Treatment , 2009, IPMI.

[41]  Thomas E Yankeelov,et al.  Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results. , 2013, Magnetic resonance imaging.

[42]  Anne L. Martel,et al.  Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast , 2007, Physics in medicine and biology.

[43]  Michael Ingrisch,et al.  Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer , 2013, Journal of Pharmacokinetics and Pharmacodynamics.

[44]  Bradford A Moffat,et al.  Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[45]  T. Chenevert,et al.  Diffusion-weighted MRI for assessment of early cancer treatment response. , 2010, Current pharmaceutical biotechnology.

[46]  Bradford A Moffat,et al.  An imaging biomarker of early treatment response in prostate cancer that has metastasized to the bone. , 2007, Cancer research.

[47]  Rüdiger Westermann,et al.  A survey of medical image registration on graphics hardware , 2011, Comput. Methods Programs Biomed..

[48]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.