Utilizing temporal information to assess metabolic heterogeneity: a study of F-FDG dynamic positron emission tomography as a treatment response biomarker in small cell lung cancer.

BACKGROUND

Extensive-stage small cell lung cancer (ES-SCLC) comprises most SCLC cases, with up to 40% of patients failing to achieve an objective response (OR) to first-line treatment. The prognostic value of conventional fluorine-18 fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters, such as maximum standardized uptake value (SUVmax), remains limited and controversial. Dynamic PET imaging with F-FDG provides detailed temporal and metabolic data, reflecting tumor heterogeneity more effectively, but its potential for predicting treatment response in ES-SCLC remains inadequately explored. This study aimed to evaluate the relationship between time-activity curve (TAC) features from dynamic PET imaging and treatment outcomes in ES-SCLC, assisting in developing personalized treatment strategies.

METHODS

This prospective pilot cohort study enrolled 15 patients with SCLC who planned to undergo dynamic PET imaging (November 2022 to January 2024). All participants underwent dynamic PET imaging before receiving first-line treatment. Tumor regions of interest (ROIs) were delineated on the PET images to facilitate the calculation of TAC. From these curves, 6 dynamic features were derived. The Mann-Whitney U test was applied to evaluate the significance of variations in continuous variables, encompassing both TAC features and conventional metabolic parameters. Statistically significant features were used to distinguish between the OR group and the non-objective response (non-OR) group and the area under the receiver operating characteristic curve (AUC) was calculated.

RESULTS

A total of 10 patients were included for analysis. Clinical characteristics such as age, gender, smoking history, and treatment regimens were similar between the OR and non-OR groups. Analyses of conventional metabolic features [SUXmax, minimum standardized uptake value (SUVmin), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] did not reveal significant differences between the groups (all P>0.05), with MTV showing a trend towards significance (P=0.095). Among the TAC features, the slope of the TAC between 10 to 30 minutes ( ) demonstrated a statistically significant difference between the OR and non-OR groups (P=0.011), suggesting its potential as a predictive marker for treatment response (AUC: 0.960). We identified two optimal cutoff values for : a threshold of 0.070 and a threshold of -0.018. After excluding an outlier patient with extensive metastatic dissemination affecting typical uptake patterns, the optimal cutoff value was determined to be -0.018.

CONCLUSIONS

The TAC feature ( ) in dynamic PET imaging may serve as an indicative predictor of treatment response in ES-SCLC, suggesting its utility in guiding treatment personalization by assessing metabolic heterogeneity between tumors.