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SU‐D‐BRA‐01: Therapeutic Response Assessment Using a Novel Gray Level Local Power Matrix (GLLPM) in DCE‐MRI Texture Analysis: Feasibility Study
Author(s) -
Wang C,
Subashi E,
Yin F,
Chang Z
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4923881
Subject(s) - artificial intelligence , mathematics , mann–whitney u test , pattern recognition (psychology) , texture (cosmology) , image texture , histogram , receiver operating characteristic , feature (linguistics) , feature extraction , medicine , computer science , nuclear medicine , statistics , image processing , image (mathematics) , linguistics , philosophy
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 (R 2 >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.