Improving tumour heterogeneity MRI assessment with histograms

By definition, tumours are heterogeneous. They are defined by marked differences in cells, microenvironmental factors (oxygenation levels, pH, VEGF, VPF and TGF-α) metabolism, vasculature, structure and function that in turn translate into heterogeneous drug delivery and therapeutic outcome. Ways to estimate quantitatively tumour heterogeneity can improve drug discovery, treatment planning and therapeutic responses. It is therefore of paramount importance to have reliable and reproducible biomarkers of cancerous lesions’ heterogeneity. During the past decade, the number of studies using histogram approaches increased drastically with various magnetic resonance imaging (MRI) techniques (DCE-MRI, DWI, SWI etc.) although information on tumour heterogeneity remains poorly exploited. This fact can be attributed to a poor knowledge of the available metrics and of their specific meaning as well as to the lack of literature references to standardised histogram methods with which surrogate markers of heterogeneity can be compared. This review highlights the current knowledge and critical advances needed to investigate and quantify tumour heterogeneity. The key role of imaging techniques and in particular the key role of MRI for an accurate investigation of tumour heterogeneity is reviewed with a particular emphasis on histogram approaches and derived methods.

[1]  Jing Yuan,et al.  Head and neck squamous cell carcinoma: diagnostic performance of diffusion-weighted MR imaging for the prediction of treatment response. , 2013, Radiology.

[2]  A. Padhani Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions , 2002, Journal of magnetic resonance imaging : JMRI.

[3]  P. Withers,et al.  MRI measurements of vessel calibre in tumour xenografts: Comparison with vascular corrosion casting , 2012, Microvascular Research.

[4]  R. Gillies,et al.  Diffusion MRI and Novel Texture Analysis in Osteosarcoma Xenotransplants Predicts Response to Anti-Checkpoint Therapy , 2013, PloS one.

[5]  Geoff J M Parker,et al.  Indexed distribution analysis for improved significance testing of spatially heterogeneous parameter maps: Application to dynamic contrast‐enhanced MRI biomarkers , 2013, Magnetic resonance in medicine.

[6]  Brandon Whitcher,et al.  Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps , 2009, Magnetic resonance in medicine.

[7]  G. Metzger,et al.  Quantitative multiparametric MRI of ovarian cancer , 2013, Journal of magnetic resonance imaging : JMRI.

[8]  T. Jaspan,et al.  Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors , 2014, American Journal of Neuroradiology.

[9]  Paul S Tofts,et al.  Apparent diffusion coefficient histograms may predict low‐grade glioma subtype , 2007, NMR in biomedicine.

[10]  Hervé Saint-Jalmes,et al.  Can Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined with Texture Analysis Differentiate Malignant Glioneuronal Tumors from Other Glioblastoma? , 2011, Neurology research international.

[11]  F. Howe,et al.  Vessel size index magnetic resonance imaging to monitor the effect of antivascular treatment in a rodent tumor model. , 2008, International journal of radiation oncology, biology, physics.

[12]  K. Chang,et al.  Histogram analysis of apparent diffusion coefficient map of standard and high B-value diffusion MR imaging in head and neck squamous cell carcinoma: a correlation study with histological grade. , 2012, Academic radiology.

[13]  Steinar Lundgren,et al.  Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE‐MRI , 2009, Journal of magnetic resonance imaging : JMRI.

[14]  Fu-Nien Wang,et al.  Analysis of parametric histogram from dynamic contrast‐enhanced MRI: application in evaluating brain tumor response to radiotherapy , 2013, NMR in biomedicine.

[15]  R. Gillies,et al.  The thioredoxin-1 inhibitor 1-methylpropyl 2-imidazolyl disulfide (PX-12) decreases vascular permeability in tumor xenografts monitored by dynamic contrast enhanced magnetic resonance imaging. , 2005, Clinical cancer research : an official journal of the American Association for Cancer Research.

[16]  S. N. Friedman,et al.  Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements. , 2012, The British journal of radiology.

[17]  Jyh-Horng Chen,et al.  Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI. , 2004, Physics in medicine and biology.

[18]  L. Vermeulen,et al.  Cancer heterogeneity—a multifaceted view , 2013, EMBO reports.

[19]  N. Just Histogram analysis of the microvasculature of intracerebral human and murine glioma xenografts , 2011, Magnetic resonance in medicine.

[20]  Andrew B Rosenkrantz,et al.  Histogram-based apparent diffusion coefficient analysis: an emerging tool for cervical cancer characterization? , 2013, AJR. American journal of roentgenology.

[21]  Jian Z. Wang,et al.  Predicting Control of Primary Tumor and Survival by DCE MRI During Early Therapy in Cervical Cancer , 2009, Investigative radiology.

[22]  N. deSouza,et al.  Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. , 2013, AJR. American journal of roentgenology.

[23]  Geon-Ho Jahng,et al.  Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging , 2013, Acta radiologica.

[24]  Chul-Kee Park,et al.  Gliomas: Application of Cumulative Histogram Analysis of Normalized Cerebral Blood Volume on 3 T MRI to Tumor Grading , 2013, PloS one.

[25]  N. Hylton,et al.  Heterogeneity in the angiogenic response of a BT474 human breast cancer to a novel vascular endothelial growth factor‐receptor tyrosine kinase inhibitor: Assessment by voxel analysis of dynamic contrast‐enhanced MRI , 2005, Journal of magnetic resonance imaging : JMRI.

[26]  Vincent Grégoire,et al.  Tumor radiosensitization by antiinflammatory drugs: evidence for a new mechanism involving the oxygen effect. , 2005, Cancer research.

[27]  Geon-Ho Jahng,et al.  True Progression versus Pseudoprogression in the Treatment of Glioblastomas: A Comparison Study of Normalized Cerebral Blood Volume and Apparent Diffusion Coefficient by Histogram Analysis , 2013, Korean journal of radiology.

[28]  J R Griffiths,et al.  Clinical studies. , 2005, Advances in pharmacology.

[29]  D. Collins,et al.  Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. , 2011, Radiology.

[30]  Jeong Yeon Cho,et al.  Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade , 2014, Acta radiologica.

[31]  M. Schocke,et al.  ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma , 2011, Neuroradiology.

[32]  Namkug Kim,et al.  Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. , 2012, Radiology.

[33]  R. Gillies,et al.  Monitoring chemotherapeutic response by hyperpolarized 13C-fumarate MRS and diffusion MRI. , 2014, Cancer research.

[34]  P. Choyke,et al.  Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.

[35]  A. Padhani,et al.  Assessing changes in tumour vascular function using dynamic contrast‐enhanced magnetic resonance imaging , 2002, NMR in biomedicine.

[36]  F. Howe,et al.  Tumor vascular architecture and function evaluated by non‐invasive susceptibility MRI methods and immunohistochemistry , 2003, Journal of magnetic resonance imaging : JMRI.

[37]  Jason A Koutcher,et al.  Dynamic contrast-enhanced magnetic resonance imaging as a predictor of outcome in head-and-neck squamous cell carcinoma patients with nodal metastases. , 2012, International journal of radiation oncology, biology, physics.

[38]  Berthold Kiefer,et al.  Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer. , 2012, Radiology.