Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis.

PURPOSE To assess the value of semi-automated segmentation applied to diffusion MRI for predicting the therapeutic response of liver metastasis. METHODS Conventional diffusion weighted magnetic resonance imaging (MRI) was performed using b-values of 0, 150, 300 and 450s/mm(2) at baseline and days 4, 11 and 39 following initiation of a new chemotherapy regimen in a pilot study with 18 women with 37 liver metastases from primary breast cancer. A semi-automated segmentation approach was used to identify liver metastases. Linear regression analysis was used to assess the relationship between baseline values of the apparent diffusion coefficient (ADC) and change in tumor size by day 39. RESULTS A semi-automated segmentation scheme was critical for obtaining the most reliable ADC measurements. A statistically significant relationship between baseline ADC values and change in tumor size at day 39 was observed for minimally treated patients with metastatic liver lesions measuring 2-5cm in size (p=0.002), but not for heavily treated patients with the same tumor size range (p=0.29), or for tumors of smaller or larger sizes. ROC analysis identified a baseline threshold ADC value of 1.33μm(2)/ms as 75% sensitive and 83% specific for identifying non-responding metastases in minimally treated patients with 2-5cm liver lesions. CONCLUSION Quantitative imaging can substantially benefit from a semi-automated segmentation scheme. Quantitative diffusion MRI results can be predictive of therapeutic outcome in selected patients with liver metastases, but not for all liver metastases, and therefore should be considered to be a restricted biomarker.

[1]  B. Ma,et al.  Clinical predictors of response to cetuximab-chemotherapy in metastatic colorectal cancer. , 2010, Hong Kong medical journal = Xianggang yi xue za zhi.

[2]  Oliver Geier,et al.  Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2010, Acta oncologica.

[3]  E. J. van der Jagt,et al.  Diffusion weighted imaging in the liver. , 2010, World journal of gastroenterology.

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

[5]  Jürgen Griebel,et al.  Tumor microcirculation and diffusion predict therapy outcome for primary rectal carcinoma. , 2003, International journal of radiation oncology, biology, physics.

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

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

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

[9]  M. Tozaki,et al.  Preliminary study of early response to neoadjuvant chemotherapy after the first cycle in breast cancer: comparison of 1H magnetic resonance spectroscopy with diffusion magnetic resonance imaging , 2010, Japanese Journal of Radiology.

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

[11]  Jürgen Griebel,et al.  Diffusion-weighted magnetic resonance imaging for monitoring diffusion changes in rectal carcinoma during combined, preoperative chemoradiation: preliminary results of a prospective study. , 2003, European journal of radiology.

[12]  U. Kontny,et al.  Diffusion-Weighted Imaging as Predictor of Therapy Response in an Animal Model of Ewing Sarcoma , 2009, Investigative radiology.

[13]  Y. Ni,et al.  Diffusion-weighted magnetic resonance imaging allows noninvasive in vivo monitoring of the effects of combretastatin a-4 phosphate after repeated administration. , 2005, Neoplasia.

[14]  Stephan E Maier,et al.  Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  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.

[16]  R. Bammer.  Basic principles of diffusion-weighted imaging. , 2003, European journal of radiology.

[17]  S E Maier,et al.  Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. , 2001, Cancer research.

[18]  Matthew A. Kupinski,et al.  Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard , 2012, Physics in medicine and biology.

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

[20]  T. Chenevert,et al.  Diffusion magnetic resonance imaging: an imaging treatment response biomarker to chemoradiotherapy in a mouse model of squamous cell cancer of the head and neck. , 2008, Translational oncology.

[21]  Alexander de Vries,et al.  Preliminary Results on the Influence of Chemoradiation on Apparent Diffusion Coefficients of Primary Rectal Carcinoma Measured by Magnetic Resonance Imaging , 2003, Strahlentherapie und Onkologie.

[22]  Bradford A Moffat,et al.  Therapeutic Efficacy of DTI-015 using Diffusion Magnetic Resonance Imaging as an Early Surrogate Marker , 2004, Clinical Cancer Research.

[23]  T. Hirai,et al.  Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: initial results , 2006, European Radiology.

[24]  R. Gillies,et al.  Diffusion-weighted MRI and response to anti-cancer therapies , 2003 .

[25]  J C Gore,et al.  Diffusion‐weighted imaging in tissues: Theoretical models , 1995, NMR in biomedicine.

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

[27]  H. Amthauer,et al.  Early prediction of anticancer effects with diffusion-weighted MR imaging in patients with colorectal liver metastases following selective internal radiotherapy , 2010, European Radiology.

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

[29]  Lei Tang,et al.  Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. , 2010, Radiology.

[30]  Abhinav K Jha,et al.  A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images , 2010, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI).

[31]  Matthew A. Kupinski,et al.  ADC Estimation in Multi-scan DWMRI , 2010 .

[32]  Robert J Gillies,et al.  Dynamic contrast-enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1alpha using PX-478. , 2005, Neoplasia.

[33]  Matthew A Kupinski,et al.  ADC estimation of lesions in diffusion-weighted MR images: A maximum-likelihood approach , 2010, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI).

[34]  Barjor Gimi,et al.  Diffusion-weighted and macromolecular contrast enhanced MRI of tumor response to antivascular therapy with ZD6126 , 2007, Cancer biology & therapy.

[35]  Jean-Philippe Galons,et al.  Early response of prostate carcinoma xenografts to docetaxel chemotherapy monitored with diffusion MRI. , 2002, Neoplasia.

[36]  Jeffrey J. Rodriguez,et al.  A maximum-likelihood approach for ADC estimation of lesions in visceral organs , 2012, 2012 IEEE Southwest Symposium on Image Analysis and Interpretation.

[37]  D. Collins,et al.  The role of pre-treatment diffusion-weighted MRI in predicting long-term outcome of colorectal liver metastasis. , 2013, The British journal of radiology.

[38]  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.

[39]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Matthew A Kupinski,et al.  Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard , 2012, Physics in medicine and biology.