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Therapeutic Response Assessment Using a Novel Gray Level Local Power Matrix (GLLPM) in DCE-MRI Texture Analysis: Feasibility Study

C Wang

C Wang*, E Subashi , F Yin , Z Chang , Duke University Medical Center, Durham, NC


SU-D-BRA-1 (Sunday, July 12, 2015) 2:05 PM - 3:00 PM Room: Ballroom A

Purpose:Most current DCE-MRI texture analysis methods focus on the spatial information of chosen contrast-enhanced MR volumes or pharmacokinetic (PK) parameter maps, and the temporal information is not well included. This work proposed a novel texture matrix called Gray Level Local Power Matrix (GLLPM) for the accurate and efficient spatiotemporal DCE-MRI texture analysis in therapeutic response assessment.

Methods:A retrospective study with two groups (n=8/group) of tumor implanted mice was conducted. The treatment/control groups received bevacizumab/saline treatment with pre- and post-treatment DCE-MRI exams. For each scan, the GLLPM was calculated and compared with classic 3D/4D Gray Level Co-Occurrence Matrices (GLCOM) using the CA concentration maps in the first 10-minute post-injection time. The calculation time of each matrix was recorded for efficiency evaluation. Using each matrix, a set of 22 Haralick texture features’ dynamic curves were calculated. The Mann-Whitney U-test was used to assess the differences of the Area Under Curve (AUC) of all derived texture feature curves between treatment/control groups. The post-treatment texture feature curves were fitted by cubic polynomial. Experiments using support vector machine in a leave-one-out approach were performed to validate the use of fitted polynomial coefficients of each texture feature curve in treatment/control group classification.

Results:The computation efficiency of GLLPM had improved factors of 3 and 20 in comparison with 3D/4D GLCOM, respectively. 21 out of 22 GLLPM texture feature dynamic curves’ AUCs between treatment/control groups had significant differences in post-treatment scan but not in pre-treatment scan. N=19 dynamic curves from GLLPM can be fitted by cubic polynomial (R2>0.8), and N for 3D/4D GLCOM were 14 and 19, respectively. The averaged classification accuracies using the post-treatment texture features curves based on GLLPM, 3D/4D GLCOM were (84.5±12.1)%, (65.6±10.5)% and (73.3±12.8)%, respectively.

Conclusion:The proposed GLLPM and its features can be used for the efficient DCE-MRI therapeutic response assessment.

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