[Excerpt from the Article: With Figures (.pdf 515K) ]
Haney S, Thompson PM, Alger JR, Cloughesy TF, Frew A, Toga AW
Laboratory of Neuro Imaging, Dept. Neurology, Division of Brain Mapping,
UCLA School of Medicine, Los Angeles CA 90095, USA,
UCLA Dept. of Radiological Sciences, and
Neuro-Oncology Program, UCLA School of Medicine
Surface modeling approaches
(described here) can create maps of changes in brain tumors on a point-by-point
These algorithms are able to follow focal growth while maintaining stereotaxic space. Genetic analyses may reveal inhomogeneities between a region of aggressive growth and the remainder of the tumor. Therapy may then be specifically tailored to the most aggressive regions.
A tissue classification method and a surface modeling method were compared in their ability to analyze changes in brain tumors, based on volumetric MRI data. Measures were derived from serially acquired T2 and gadolinium-enhanced T1-weighted SPGR (spoiled GRASS) MRIs. Volumes for contrast-enhancing tissue, necrosis, and edema were determined and cross-validated against manually defined volumes. Volumes generated by both algorithms were highly correlated with volumes generated by manual segmentation (r2=0.99 for the tissue segmentation method; r2=0.96 for the surface modeling algorithm). Growth rates were calculated from contrast-enhancing tissue volumes. Growth rates derived from the tissue classification approach were highly correlated with growth rates derived from manually segmented images (r2=0.94). Growth rates were significantly correlated with survival (p<0.03) as was the choline to creatine ratio (CHO/CRE; p<0.02). [1H]-MR spectroscopy measures, linked to the rates of cellular proliferation, were also examined to assess their relationship with growth rates.
Grant Support: (to P.T. and A.W.T.): NIMH/NIDA (P20 MH/DA52176), P41 NCRR (RR13642); (A.W.T.): NLM (LM/MH05639), NSF (BIR 93-22434), NCRR (RR05956) and NINCDS/NIMH (NS38753).
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