Role of Quantitative Magnetic Resonance Imaging Parameters in the Evaluation of Treatment Response in Malignant Tumors

Objective:To elaborate the role of quantitative magnetic resonance imaging (MRI) parameters in the evaluation of treatment response in malignant tumors. Data Sources:Data cited in this review were obtained mainly from PubMed in English from 1999 to 2014, with keywords “dynamic contrast-enhanced (DCE)-MRI,” “diffusion-weighted imaging (DWI),” “microcirculation,” “apparent diffusion coefficient (ADC),” “treatment response” and “oncology.” Study Selection:Articles regarding principles of DCE-MRI, principles of DWI, clinical applications as well as opportunity and aspiration were identified, retrieved and reviewed. Results:A significant correlation between ADC values and treatment response was reported in most DWI studies. Most quantitative DCE-MRI studies showed a significant correlation between Ktrans values and treatment response. However, in different tumors and studies, both high and low pretreatment ADC or Ktrans values were found to be associated with response rate. Both DCE-MRI and DWI demonstrated changes in their parameters hours to days after treatment, showing a decrease in Ktrans or an increase in ADC associated with response in most cases. Conclusions:Combinations of quantitative MRI play an important role in the evaluation of treatment response of malignant tumors and hold promise for use as a cancer treatment response biomarker. However, validation is hampered by the lack of reproducibility and standardization. MRI acquisition protocols and quantitative image analysis approaches should be properly addressed prior to further testing the clinical use of quantitative MRI parameters in the assessment of treatments.

[1]  N. Hylton,et al.  MRI in breast cancer therapy monitoring , 2011, NMR in biomedicine.

[2]  Peter Gibbs,et al.  Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. , 2006, Magnetic resonance imaging.

[3]  William E Grizzle,et al.  Breast tumor xenografts: diffusion-weighted MR imaging to assess early therapy with novel apoptosis-inducing anti-DR5 antibody. , 2008, Radiology.

[4]  C. Kim,et al.  Prediction of locally recurrent prostate cancer after radiation therapy: Incremental value of 3T diffusion‐weighted MRI , 2009, Journal of magnetic resonance imaging : JMRI.

[5]  R. Gillies,et al.  Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. , 2004, Neoplasia.

[6]  D. Choi,et al.  Pathological correlation with diffusion restriction on diffusion-weighted imaging in patients with pathological complete response after neoadjuvant chemoradiation therapy for locally advanced rectal cancer: preliminary results. , 2012, The British journal of radiology.

[7]  A. Jackson,et al.  Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies , 2012, Nature Reviews Clinical Oncology.

[8]  K. Verstraete,et al.  Bone and soft tissue tumors: the role of contrast agents for MR imaging. , 2000, European journal of radiology.

[9]  T. Hirai,et al.  The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: a comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient , 2010, Skeletal Radiology.

[10]  T. Murakami,et al.  Diffusion-weighted single-shot echoplanar MR imaging for liver disease. , 1999, AJR. American journal of roentgenology.

[11]  Milica Medved,et al.  Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time , 2004, Journal of magnetic resonance imaging : JMRI.

[12]  C. Kim,et al.  Assessment of response to radiotherapy for prostate cancer: value of diffusion-weighted MRI at 3 T. , 2010, AJR. American journal of roentgenology.

[13]  Jun Li,et al.  A clinically feasible method to estimate pharmacokinetic parameters in breast cancer. , 2009, Medical physics.

[14]  Sanjeev Chawla,et al.  Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck. , 2013, AJR. American journal of roentgenology.

[15]  G. Bollini,et al.  Value of diffusion-weighted images in differentiating mid-course responders to chemotherapy for osteosarcoma compared to the histological response: preliminary results , 2012, Skeletal Radiology.

[16]  Marcelino Bernardo,et al.  The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. , 2010, Diagnostic and interventional radiology.

[17]  Thomas E Yankeelov,et al.  Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. , 2007, Magnetic resonance imaging.

[18]  L. Bassett,et al.  Combined DCE‐MRI and single‐voxel 2D MRS for differentiation between benign and malignant breast lesions , 2010, NMR in biomedicine.

[19]  R. Hermans,et al.  Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for head and neck squamous cell carcinoma , 2010, European Radiology.

[20]  G. Weinstein,et al.  Prediction of Disease-Free Survival in Patients with Squamous Cell Carcinomas of the Head and Neck Using Dynamic Contrast-Enhanced MR Imaging , 2011, American Journal of Neuroradiology.

[21]  G. Weinstein,et al.  Diffusion-Weighted Magnetic Resonance Imaging for Predicting and Detecting Early Response to Chemoradiation Therapy of Squamous Cell Carcinomas of the Head and Neck , 2009, Clinical Cancer Research.

[22]  G. Weinstein,et al.  Prediction of Response to Chemoradiation Therapy in Squamous Cell Carcinomas of the Head and Neck Using Dynamic Contrast-Enhanced MR Imaging , 2010, American Journal of Neuroradiology.

[23]  S. Takashima,et al.  Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. , 2001, Radiology.

[24]  J. Xian,et al.  Feasibility and value of quantitative dynamic contrast enhancement MR imaging in the evaluation of sinonasal tumors , 2014, Chinese medical journal.

[25]  Clare Allen,et al.  Accuracy of multiparametric magnetic resonance imaging in detecting recurrent prostate cancer after radiotherapy , 2010, BJU international.

[26]  B. K. Park,et al.  MRI techniques for prediction of local tumor progression after high-intensity focused ultrasonic ablation of prostate cancer. , 2008, AJR. American journal of roentgenology.

[27]  Ann D. King,et al.  Squamous cell carcinoma of the head and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response , 2010, European Radiology.

[28]  A. Padhani.  Integrating multiparametric prostate MRI into clinical practice , 2011, Cancer imaging : the official publication of the International Cancer Imaging Society.

[29]  Marek Ancukiewicz,et al.  Magnetic resonance imaging biomarkers in hepatocellular carcinoma: association with response and circulating biomarkers after sunitinib therapy , 2013, Journal of Hematology & Oncology.

[30]  Qing Ji,et al.  Dynamic contrast‐enhanced magnetic resonance imaging as a prognostic factor in predicting event‐free and overall survival in pediatric patients with osteosarcoma , 2012, Cancer.

[31]  Jean-Luc Daire,et al.  Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging sequences: prospective study in 66 patients. , 2003, Radiology.

[32]  J P Logue,et al.  Tumour oxygenation levels correlate with dynamic contrast-enhanced magnetic resonance imaging parameters in carcinoma of the cervix. , 2000, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[33]  T. Ishidate,et al.  Changes in Diffusion-Weighted Images for Visualizing Prostate Cancer during Antiandrogen Therapy: Preliminary Results , 2010, Urologia Internationalis.

[34]  D. Collins,et al.  Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. , 2007, AJR. American journal of roentgenology.

[35]  T. Martín Noguerol,et al.  Utility of MRI Diffusion Techniques in the Evaluation of Tumors of the Head and Neck , 2013, Cancers.

[36]  N. Hylton.  Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  David A Bluemke,et al.  Unresectable hepatocellular carcinoma: serial early vascular and cellular changes after transarterial chemoembolization as detected with MR imaging. , 2009, Radiology.

[38]  Fiona J Gilbert,et al.  Use of new imaging techniques to predict tumour response to therapy. , 2010, The Lancet. Oncology.

[39]  L. Shen,et al.  Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. , 2008, Radiology.

[40]  Katarzyna J Macura,et al.  Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[41]  T. M. Noguerol,et al.  Utility of MRI Diffusion Techniques in the Evaluation of Tumors of the Head and Neck , 2013 .

[42]  J. Gore,et al.  Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. , 2005, Magnetic resonance imaging.

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

[44]  James H Thrall,et al.  Imaging angiogenesis: applications and potential for drug development. , 2005, Journal of the National Cancer Institute.

[45]  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.

[46]  M. Sumi,et al.  Multiparametric MR Imaging of Sinonasal Diseases: Time-Signal Intensity Curve– and Apparent Diffusion Coefficient–Based Differentiation between Benign and Malignant Lesions , 2011, American Journal of Neuroradiology.

[47]  G. Parker,et al.  DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents , 2007, British Journal of Cancer.

[48]  Jan Wolber,et al.  Diffusion MRI for prediction of response of rectal cancer to chemoradiation , 2002, The Lancet.

[49]  Arend Heerschap,et al.  Tumour response prediction by diffusion-weighted MR imaging: ready for clinical use? , 2012, Critical reviews in oncology/hematology.

[50]  Hee Jung Shin,et al.  Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion‐weighted imaging and MRS , 2012, NMR in biomedicine.

[51]  U. Sharma,et al.  Longitudinal study of the assessment by MRI and diffusion‐weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2009, NMR in biomedicine.

[52]  Anwar R. Padhani,et al.  Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy , 2010, Targeted Oncology.

[53]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[54]  Jie Li,et al.  DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2011, Medical Oncology.

[55]  M. Knopp,et al.  Functional tumor imaging with dynamic contrast‐enhanced magnetic resonance imaging , 2003, Journal of magnetic resonance imaging : JMRI.

[56]  Brian D Ross,et al.  Predicting and monitoring cancer treatment response with diffusion‐weighted MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[57]  G Brix,et al.  Tumor microcirculation evaluated by dynamic magnetic resonance imaging predicts therapy outcome for primary rectal carcinoma. , 2001, Cancer research.

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

[59]  Kazuro Sugimura,et al.  Prostate cancer detection with 3 T MRI: Comparison of diffusion‐weighted imaging and dynamic contrast‐enhanced MRI in combination with T2‐weighted imaging , 2010, Journal of magnetic resonance imaging : JMRI.

[60]  Hidetake Yabuuchi,et al.  Index terms: , 2002 .