Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose

Abstract

Lung cancer mortality rate can be significantly reduced by up to 20% through routine low-dose computed tomography (LDCT) screening, which, however, has high sensitivity but low specificity, resulting in a high rate of false-positive nodules. Combining PET with CT may provide more accurate diagnosis for indeterminate screening-detected nodules. In this work, we investigated low-dose dynamic 18F-FDG PET in discrimination between benign and malignant nodules using a virtual clinical trial based on patient study with ground truth. Six patients with initial LDCT screening-detected lung nodules received 90 min single-bed PET scans following a 10 mCi FDG injection. Low-dose static and dynamic images were generated from under-sampled list-mode data at various count levels (100%, 50%, 10%, 5%, and 1%). A virtual clinical trial was performed by adding nodule population variability, measurement noise, and static PET acquisition start time variability to the time activity curves (TACs) of the patient data. We used receiver operating characteristic (ROC) analysis to estimate the classification capability of standardized uptake value (SUV) and net uptake constant K i from their simulated benign and malignant distributions. Various scan durations and start times (t *) were investigated in dynamic Patlak analysis to optimize simplified acquisition protocols with a population-based input function (PBIF). The area under curve (AUC) of ROC analysis was higher with increased scan duration and earlier t *. Highly similar results were obtained using PBIF to those using image-derived input function (IDIF). The AUC value for K i using optimized t * and scan duration with 10% dose was higher than that for SUV with 100% dose. Our results suggest that dynamic PET with as little as 1 mCi FDG could provide discrimination between benign and malignant lung nodules with higher than 90% sensitivity and specificity for patients similar to the pilot and simulated population in this study, with LDCT screening-detected indeterminate lung nodules.

Figures

Figure 1.

Illustration of dynamic image analysis for each low-dose replicate. TACs and IDIFs were generated from dynamic reconstructed images. ROI-based analysis includes nonlinear regression of 2Ti model and Patlak analysis. Voxel-based analysis was accomplished by Patlak analysis.

Figure 2.

Process of generating virtual patient data based on real patient scans. Biological variability was added to K1 to extend the population. Based on the simulated K1 and noise level estimated from patient data, TACs and input functions were simulated for various count levels. SUV was computed from static analysis with acquisition start time variability consistent with clinical practice. Ki was generated from dynamic Patlak analysis with simplified protocols.

Figure 3.

Illustration of classification capability estimation for SUV and Ki distributions among all the simulated datasets of a given count level using ROC analysis.

Figure 4.

CT, SUV and Ki images for two sample patients. The first row shows a benign nodule while the second row shows a malignant nodule.

Figure 5.

SUV and Ki images at various count levels for a sample patient with an 8-mm lung nodule. Left: SUV using 60–80 min post-injection data. Right: Ki images generated from voxel-based Patlak analysis using IDIF with t* of 20 min.

Figure 6.

Ratios of parameters at various count levels compared to those with 100% counts: (a) nodule SUV; (b) nodule Ki derived from nonlinear regression with the 2Ti compartmental model; (c) nodule Ki derived from Patlak analysis (20–90 min); (d) tissue noise scale factors in equation (2). Mean and standard deviation from the low-count replicates are shown.

Figure 7.

Distributions generated from the virtual clinical trial at various count levels. SUV derived from ~ 60–80 min (with acquisition variability) data (left), Ki derived from 20–90 min Patlak analysis using PBIF (right) are compared.

Figure 8.

SUV (first row) and Ki (second row) images of sample nodules. A benign nodule selected from Group 1 (first column), benign nodules selected from Group 2 (second column), and a malignant nodule (third column) are compared.

Figure 9.

ROC AUC values of Ki with different scan durations as a function of fit start time, t*, compared with the AUC values of SUV (~ 60–80 min post injection, red lines) at various count levels. Ki values were also generated by Patlak analysis using data from t* up to 90 min postinjection (black lines).

Figure 10.

AUC values of SUV and Ki with several sample protocols at various count levels.