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AJR Am J Roentgenol. 2014 May;202(5):1114-9. doi: 10.2214/AJR.13.11456.

FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors.

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1
1 Department of Radiology, Boston University School of Medicine, Boston, MA.

Abstract

OBJECTIVE:

The purpose of this study was to establish the correlation and reliability among the pathologic tumor volume and gradient and fixed threshold segmentations of (18)F-FDG PET metabolic tumor volume of human solid tumors.

MATERIALS AND METHODS:

There were 52 patients included in the study who had undergone baseline PET/CT with subsequent resection of head and neck, lung, and colorectal tumors. The pathologic volume was calculated from three dimensions of the gross tumor specimen as a reference standard. The primary tumor metabolic tumor volume was segmented using gradient and 30%, 40%, and 50% maximum standardized uptake value (SUVmax) threshold methods. Pearson correlation coefficient, intraclass correlation coefficient, and Bland-Altman analyses were performed to establish the correlation and reliability among the pathologic volume and segmented metabolic tumor volume.

RESULTS:

The mean pathologic volume; gradient-based metabolic tumor volume; and 30%, 40%, and 50% SUVmax threshold metabolic tumor volumes were 13.46, 13.75, 15.47, 10.63, and 7.57 mL, respectively. The intraclass correlation coefficients among the pathologic volume and the gradient-based and 30%, 40%, and 50% SUVmax threshold metabolic tumor volumes were 0.95, 0.85, 0.80, and 0.76, respectively. The Bland-Altman biases were -0.3, -2.0, 2.82, and 5.9 mL, respectively. Of the small tumors (< 10 mL), 23 of the 35 patients had PET segmented volume outside 50% of the pathologic volume, and among the large tumors (≥ 10 mL) three of the 17 patients had PET segmented volumes that were outside 50% of pathologic volume.

CONCLUSION:

FDG PET metabolic tumor volume estimated using gradient segmentation had superior correlation and reliability with the estimated ellipsoid pathologic volume of the tumors compared with threshold method segmentation.

PMID:
24758668
DOI:
10.2214/AJR.13.11456
[Indexed for MEDLINE]

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