Concordance of quantitative FDG PET/CT imaging biomarkers for classifying early response to chemoradiotherapy in patients with locally advanced non-small cell lung cancer.

1333 Objectives: Early treatment response assessment is critical for decision support within personalized oncology. We evaluated the concordance of semi-automatically derived fluorodeoxyglucose (FDG) PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). Methods: Patients with AJCC v7 stage IIB-IIIB NSCLC eligible for definitive concurrent chemoradiotherapy were enrolled on the FLARE-RT phase II trial (NCT02773238). Patients underwent baseline imaging (PETpre) prior to photon or proton external beam radiation therapy (EBRT). Multidisciplinary response assessment during week 3 of a planned 6 weeks of EBRT (at a dose of 24 Gy) was performed after interval imaging (PETmid). Patients classified as PETmid responders received EBRT regimens of 60 Gy in 30 fx, while high-risk PETmid non-responders received 74 Gy in 30 fx. PETpre and PETmid scans (skull-base to mid-thigh) were acquired on GE DSTE or DMI PET/CT systems following IV administration of 10-14 mCi (370-518 MBq) [18F]FDG and a 60-minute uptake period. DSTE PET images were reconstructed using ordered subset expectation maximization (OSEM). DMI PET images were reconstructed using OSEM settings harmonized with the DSTE, as well as using GE Q.Clear time-of-flight reconstruction based on block sequential regularized expectation maximization (BSREM). Primary tumor and contiguous involved nodes were contoured on PETpre and PETmid images by 3 observers using the MIM 6.8.8 PET Edge semi-automatic segmentation tool. SUVmax, SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated on PETpre and PETmid. Dice similarity of contours and response classification area under the curve (AUC) of PET imaging biomarkers were evaluated across observers, scanners, and reconstruction algorithms. Multivariate LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data. Results: 31 patients were evaluable for analysis: 18 patients were assessed as PETmid responders and 13 as PETmid non-responders. 20 patients were imaged on the DSTE and 11 were imaged on the DMI. Inter-observer variability of contours on PETpre / PETmid images was low for harmonized OSEM (Dice range 0.93-0.95) and Q.Clear reconstructions (Dice range 0.92-0.93). Intra-observer variability between harmonized OSEM and Q.Clear reconstructions was higher (PETpre Dice range 0.82-0.89, PETmid Dice range 0.79-0.89). Relative changes in imaging biomarkers between PETpre and PETmid were more strongly associated with response status than imaging biomarkers at individual time points. ΔSUVpeak was the most robust response predictor across observers and image reconstructions (cutoff decrease 29.5-31.2%, sensitivity 1.00, specificity 0.83-1.00). Multivariate LASSO logistic regression consistently selected ΔSUVpeak and ΔMTV as response predictors. Multivariate response classification models achieved high cross-validated performance (AUC median 0.91, range 0.88-0.92) on the DSTE cohort and more variable testing performance on the DMI cohort when using harmonized OSEM (AUC median 0.87, range 0.73-0.93) or Q.Clear reconstructions (AUC median 0.86, range 0.75-0.92). Conclusions: Semi-automatic quantification of SUVpeak on interval PET/CT imaging yields concordant early response classification across observers, scanners, and reconstruction algorithms for patients with LA-NSCLC, which may improve the precision of biologically adaptive radiation therapy. This approach can be extended to response assessment of other cohorts receiving molecularly targeted agents and immune checkpoint inhibitors. Acknowledgements: This investigation was supported by NIH/NCI R01CA204301.