Renu M Stephen1, Abhinav K Jha2, Denise J Roe3, Theodore P Trouard4, Jean-Philippe Galons5, Matthew A Kupinski6, Georgette Frey7, Haiyan Cui7, Scott Squire5, Mark D Pagel8, Jeffrey J Rodriguez9, Robert J Gillies10, Alison T Stopeck7. 1. University of Arizona Cancer Center, University of Arizona, Tucson, AZ. Electronic address: rstephen1@gmail.com. 2. College of Optical Sciences, University of Arizona, Tucson, AZ. 3. University of Arizona Cancer Center, University of Arizona, Tucson, AZ; Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ. 4. Department of Biomedical Engineering, University of Arizona, Tucson, AZ; Department of Medical Imaging, University of Arizona, Tucson, AZ. 5. Department of Medical Imaging, University of Arizona, Tucson, AZ. 6. College of Optical Sciences, University of Arizona, Tucson, AZ; Department of Medical Imaging, University of Arizona, Tucson, AZ. 7. University of Arizona Cancer Center, University of Arizona, Tucson, AZ. 8. University of Arizona Cancer Center, University of Arizona, Tucson, AZ; Department of Biomedical Engineering, University of Arizona, Tucson, AZ; Department of Medical Imaging, University of Arizona, Tucson, AZ; Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ. Electronic address: mpagel@u.arizona.edu. 9. Electrical and Computer Engineering, University of Arizona, Tucson, AZ. 10. H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Abstract
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.
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.
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