The diagnostic utility of the lognormal behavior of PET standardized uptake values in tumors.

UNLABELLED: A meta-analysis of data primarily from PET oncologic investigations using FDG PET was performed. Its purpose was to establish statistical features of the distributions of standardized uptake values (SUVs) as possible aids in the diagnostic process. METHODS: We obtained 1536 values of oncologic markers from patient studies of 40 investigations in the literature. Statistical parameters were tabulated for analysis. RESULTS: A significant observation is that, unlike skewed SUV histograms, log10SUV has Gaussian behavior, which is not uncommon for biologic quantities. This was found for SUVs of FDG and 2 amino acids as well as a few other cancer markers. A possible model for explaining this is proposed. For FDG, the SD sigma of the log10SUVs for an average cancer category was 0.23. Examining data within the framework of the model points to physiologic factors as dominating SUV variability rather than PET protocols. When data for a single cancer category were available from multiple institutions, averages, mean(SUV)s, disagree beyond chance expectations. Diagnostic utility suggestions include a universal linear relationship between sensitivity and severity, defined as SUV/mean(SUV), on semilogarithmic probability paper; a generic receiver-operating-characteristic curve for all cancers; using [log10(mean(SUVmal)/mean(SUVnorm))] divided by (sigma(mal)2 + sigma(norm)2)(1/2) as a simple diagnostic effectiveness measure; and using Gaussian log10SUVs to avoid erroneous P values. CONCLUSION: Using the logarithms of markers, such as SUVs, several advantages stemming from their Gaussian nature can be achieved with benefits ensuing to the diagnostic process.

The Potential of F-18-FDG PET in Breast Cancer. Detection of Primary Lesions, Axillary Lymph Node Metastases, or Distant Metastases.

This retrospective study was done to evaluate the utility of 2-[F-18]fluoro-2-deoxy-D-glucose positron emission tomography (F-18-FDG PET) in identifying primary and recurrent breast cancer and lymph node metastases. One hundred whole-body PET scans of 87 patients were reviewed. PET results obtained with F-18-FDG and an ECAT/EXACT-921 or an ECAT-931 (Siemens/CTI) were based on visual interpretation, or standardized uptake values (SUVs), related to histology and also compared to computerized tomography (CT) and mammography results. The sensitivity for PET in detecting primary (N = 35 studies) and recurrent breast cancer (N = 65 studies) was 96% and 85% with a specificity of 91% and 73%. The sensitivity for lymph node metastases at the time of initial diagnosis was 100% with a specificity of 100%. Quantitative SUV information did not improve the accuracy of F-18-FDG PET in identifying primary breast cancers. The results suggest that whole-body PET is useful in detecting recurrence or metastases, may be useful in detecting lymph node metastases prior to initial axillary lymph node dissection, but is less sensitive in excluding axillary lymph nodes metastases later in the course of the disease.

Positron Emission Tomography (PET) with 1-Aminocyclobutane-1-[(11)C]carboxylic Acid (1-[(11)C]-ACBC) for Detecting Recurrent Brain Tumors.

This study was done to determine whether 1-[(11)C]ACBC PET has any advantages over 2-[(18)F]FDG PET, CT, or MRI in detecting recurrent brain tumors, and whether quantitative 1-[(11)C]ACBC PET information improves the accuracy of "visual" image interpretation.Twenty patients with recurrent brain tumor underwent dynamic PET. Images were analyzed by visual interpretation; in addition, standardized uptake values (SUVs) and Patlak values (k(1)*k(3)/k) were evaluated.1-[(11)C]ACBC identified 19/20 recurrent brain tumors, [18F]FDG 13/19, MRI 13/19, and CT 8/16. Based on SUVs, the average tumor-to-contralateral gray matter ratio of 1-[(11)C]ACBC was 5.0 and 0.5 for 2-[(18)F]FDG. Mean Patlak values of 1-[(11)C]ACBC were 0.044 +/- 0.047 for high and 0.034 +/- 0.026 for low grade tumors. However, visual interpretation was effective without quantitative PET data.1-[(11)C]ACBC, accurately detects recurrent tumors for selecting biopsy sites and treatment planning.

Linear least squares compartmental-model-independent parameter identification in PET.

A simplified approach involving linear-regression straight-line parameter fitting of dynamic scan data is developed for both specific and nonspecific models. Where compartmental-model topologies apply, the measured activity may be expressed in terms of: its integrals, plasma activity and plasma integrals--all in a linear expression with macroparameters as coefficients. Multiple linear regression, as in spreadsheet software, determines parameters for best data fits. Positron emission tomography (PET)-acquired gray-matter images in a dynamic scan are analyzed: both by this method and by traditional iterative nonlinear least squares. Both patient and simulated data were used. Regression and traditional methods are in expected agreement. Monte-Carlo simulations evaluate parameter standard deviations, due to data noise, and much smaller noise-induced biases. Unique straight-line graphical displays permit visualizing data influences on various macroparameters as changes in slopes. Advantages of regression fitting are: simplicity, speed, ease of implementation in spreadsheet software, avoiding risks of convergence failures or false solutions in iterative least squares, and providing various visualizations of the uptake process by straight line graphical displays. Multiparameter model-independent analyses on lesser understood systems is also made possible.