Evaluating dementia using PET: how do we put into clinical perspective what we know to date?

Evaluating Dementia Using PET: How Do We Put into Clinical Perspective What We Know to Date? Decreasing mortality, with consequent progressive aging of the mature adult population, has led to a rising prevalence of senile dementia. The condition is tremendously costly to patients, their families, and society in general. Alzheimer’s disease (AD) in the United States affects over 4 million people, who incur associated yearly expenses of nearly $70 billion; when indirect costs such as the lost productivity of caregivers are considered, total annual expenditures approximate $100 billion. As the ‘‘baby boomers’’ approach senior citizen status in the 21st century, it has been estimated that over 14 million Americans will suffer from AD by 2050 (1–4). Regional cerebral metabolic patterns of patients with dementia reflect pathophysiologic alterations, even before they lead to symptomatic expression (5). The use of PET in evaluating dementia has been studied since the early 1980s ( 6–9) and has been extensively reviewed in recent years ( 10– 14). The best studied application of PET in dementia uses FDG to evaluate AD. Assessment of the diagnostic accuracy of PET, even for this application, however, has been hindered by the paucity of data involving scans of patients undergoing subsequent neuropathologic examination. The article by Hoffman et al. ( 15) in this issue ofThe Journal of Nuclear Medicineaddresses an issue of pressing clinical relevance: the establishment of reliable estimates of diagnostic accuracy of FDG PET (the present imaging modality of choice in the assessment of primary neurodegenerative dementias) for AD. In doing so, their article provides a substantial addition to the currently limited base of data in the peer-reviewed scientific literature on patients studied by both brain PET and pathologic analysis. The authors sampled patients at a universitybased memory disorder clinic, staffed by board-certified neurologists who specialized in dementia evaluation; patients were included if their impairment of memory was objectively documentable but the diagnosis was ‘‘challenging or difficult’’ according to the evaluating clinicians. A nuclear medicine physician who was unaware of clinical information and who interpreted FDG PET was able to predict the ultimate diagnosis of AD in 13 of the 14 patients (92.9%) for whom that was the only evident neuropathology and for 14 of the 16 patients (87.5%) harboring AD-related changes in their brains. In comparison, a diagnosis of AD had been clinically suspected (considered probable) in 64.3% and 62.5%, respectively, and considered possible in another 14.3% and 12.5%, respectively. Thus, under the best of circumstances, AD would be clinically missed in 21% of these patients, whereas under those same circumstances, only 7% of AD cases would escape detection on FDG PET, even in the absence of knowledge of any other clinical information. Because of the lower diagnostic specificity found for FDG PET (75% vs. 100%), however, corresponding overall accuracies for clinical evaluation and PET were identical (86.4%). The difference in specificity was entirely accounted for by 2 cases—1 of Creutzfeldt-Jacob disease and 1 of Lewy Body disease—both with clinically evident diagnoses and associated with bilateral parietotemporal hypometabolism. Thus, if PET had been interpreted in light of the available clinical information (as would likely occur in its routine clinical application), rather than blindly (as done here to address a specific research question), a diagnostic specificity of 100% would pertain. FDG PET did correctly classify as non-AD the cases (1 each) of progressive supranuclear palsy, preamyloid, mesio-limbo cortical degeneration, and nonspecific neuronal degenerative change, as did clinical evaluation. The most serious problem of the current investigation ( 15) is the inadequacy of its statistical strength to meaningfully assess specificity, because of the low number of patients included in the study who were found on histopathologic exam to be free of AD. This problem arises from the general difficulty, shared by all previous such studies, in obtaining sufficiently sizable samples of patients who have had brain PET and are followed to pathologic confirmation. Because of this, values are reported that would be substantially altered by shifts in the ‘‘truth tables’’ of just 1 or 2 patients. This statistical weakness can be appreciated quantitatively by considering that the 95% confidence interval (not reported by the Hoffman et al.) pertaining to the 75% specificity value ranges from 45% to 100%. In other words, the actual specificity for correctly identifying non-AD patients could range anywhere from essentially perfect to a coin flip, given the number and distribution of cases included in the study. Another shortcoming with this study, as presented, is that it is difficult to determine the appropriate setting to which it applies, because the degree of cognitive impairment of the patients examined is nowhere quantified (e.g., by MiniReceived May 31, 2000; accepted Jun. 1, 2000. For correspondence or reprints contact: Daniel H. Silverman, MD, PhD, Department of Molecular and Medical Pharmacology, UCLA School of Medicine, CHS AR-144, Los Angeles, CA 900956942.