The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake.

The number of studies in the literature involving quantification of the metabolic heterogeneity seen in (18)F-FDG PET images has increased sharply over recent years. We hypothesized that inclusion of very small regions of interest as unique data points will have deleterious effects on these studies. Using a combination of probability theory and clinical (18)F-FDG PET data, we numerically calculated the curve describing the probability a given tumor volume is large enough to adequately sample the underlying tumor biology assayed via a PET/CT scanner at a planar resolution of 4 mm and transaxial resolution of 4 mm (64 mm(3) voxel size). We then used a computer simulation to isolate the effects of tumor volume on the image local entropy. We computed the underlying global intensity distribution for 70 cervical cancer tumors ranging from 4 to 248 cm(3)), which were ensemble-averaged over the same intensity scale. From this distribution, we determined that about 700 total voxels (45 cm(3)) are required to give 95% certainty that the global intensity distribution has been sufficiently sampled for common statistical comparisons of individual tumor intensity distributions to be made canonically. We demonstrated that one previously suggested measure of heterogeneity is dependent on tumor volume and that measurement of heterogeneity is about 5 times more sensitive to volume changes for volumes below the proposed minimum than for those above it. Inclusion of tumor volumes below 45 cm(3) can profoundly bias comparisons of intratumoral uptake heterogeneity metrics derived from data from the current generation of whole-body (18)F-FDG PET scanners.