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


CONCLUSIONS
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.
University of Arizona Cancer Center, University of Arizona, Tucson, AZ. Electronic address: rstephen1@gmail.com.
Renu M
RM
Stephen
College of Optical Sciences, University of Arizona, Tucson, AZ.
Abhinav K
AK
Jha
University of Arizona Cancer Center, University of Arizona, Tucson, AZ; Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ.
Denise J
DJ
Roe
Department of Biomedical Engineering, University of Arizona, Tucson, AZ; Department of Medical Imaging, University of Arizona, Tucson, AZ.
Theodore P
TP
Trouard
Department of Medical Imaging, University of Arizona, Tucson, AZ.
Jean-Philippe
JP
Galons
College of Optical Sciences, University of Arizona, Tucson, AZ; Department of Medical Imaging, University of Arizona, Tucson, AZ.
Matthew A
MA
Kupinski
University of Arizona Cancer Center, University of Arizona, Tucson, AZ.
Georgette
G
Frey
University of Arizona Cancer Center, University of Arizona, Tucson, AZ.
Haiyan
H
Cui
Department of Medical Imaging, University of Arizona, Tucson, AZ.
Scott
S
Squire
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.
Mark D
MD
Pagel
Electrical and Computer Engineering, University of Arizona, Tucson, AZ.
Jeffrey J
JJ
Rodriguez
H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Robert J
RJ
Gillies
University of Arizona Cancer Center, University of Arizona, Tucson, AZ.
Alison T
AT
Stopeck
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000473:pathology; D008113:Liver Neoplasms / Q000188:drug therapy* / Q000556:secondary*; D008875:Middle Aged; D010865:Pilot Projects; D012372:ROC Curve; D016896:Treatment Outcome
D014408:Biomarkers, Tumor
Breast cancer; Diffusion MRI; Liver imaging; Restricted biomarker