18F-FDG PET/CT Metabolic Tumor Volume and Intratumoral Heterogeneity in Pancreatic Adenocarcinomas

Abstract

Objectives We aimed to determine the consistency of quantitative PET measurements of metabolic tumor volume (MTV) and intra-tumoral heterogeneity index for primary untreated pancreatic adenocarcinomas, when using dual-time-point 18F-FDG PET/CT imaging. Methods This is an Institutional Review Board approved, retrospective study including 71 patients with pancreatic adenocarcinoma, who underwent dual-time-point 18F-FDG PET/CT imaging, at ~1h (early) and ~2h (delayed), post injection. Automated gradient-based and 50% SUVmax-threshold segmentation methods were used to assess the primary tumor MTV, and metabolic intra-tumoral heterogeneity index, calculated as the area under Cumulative SUV-volume histograms (AUC-CSH), with lower AUC-CHS indexes corresponding to higher degrees of tumor heterogeneity. We defined that more than a ±10% change in MTV or AUC-CSH, compared to baseline, as clinically significant. Results 71 FDG-avid pancreatic tumors were identified, with an average tumor diameter of 3.4±0.9 cm (range, 1.5 to 6.4 cm). MTV values remained consistent between early and delayed imaging when using the gradient PET segmentation method (p=0.086), whereas statistically significant change was seen when using 50% SUVmax-threshold segmentation (p <0.001). A decrease in more than 10% change in MTV (% ΔMTV) was observed in 70.4% (50/71) tumors, and 7.0% (5/71) of the tumors showed an increase more than 10 % ΔMTV, when using the 50% SUVmax-threshold segmentation. AUC-CSH indexes showed statistically significant differences between early and delayed time points (p<0.001), when using the gradient segmentation. AUC-CSH index decreased ≥10% in 40.8% (29/71) of the tumors. AUC-CSH index remained stable between early and delayed when using the 50% SUVmax-threshold segmentation (p=0.148) with % of change of less than 10% for all tumors. Conclusion Metabolic Tumor Volume was relatively stable between early and delayed time points when PET gradient segmentation was used but changed >10% in 77.4% of the tumors at delayed time point when threshold segmentation was used. The tumor heterogeneity index (AUC-CSH) changed >10% in 40.8% of tumors at delayed imaging, when gradient segmentation was used but remained stable when threshold segmentation was used. It is important to standardize uptake time and segmentation methods to use FDG PET metabolic tumor volume and heterogeneity index as imaging biomarkers.

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

“…A particularly important application is quantitative imaging biomarkers (QIBs). Various QIBs are being explored for clinical usage, including tumor standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) measured from 18 F-Fluoro-2-deoxyglucose positron emission tomography (18F-FDG PET), [2][3][4][5][6][7][8][9][10] apparent diffusion coefficient measured with diffusion magnetic resonance imaging, 11,[12][13] and dopamine transporter using single-photon emission computed tomography. 14 A major challenge in QIB development is that the quantitative metrics should be measured precisely.…”

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

“…Evaluating accuracy and comparing to other methods using patient and simulated data. The framework was quantitatively compared to semi-automated segmentation methods, including commonly used thresholding-based approaches (Foster et al 2014, Mena et al 2017a, Shah et al 2012, Mena et al 2017b, Sridhar et al 2014, the active contour-based snakes method (Kass et al 1988), and the Markov random fields-Gaussian mixture model-based clustering technique (Jha et al 2010, Layer et al 2015. Three thresholding approaches using fixed intensity thresholds of 30%, 40%, and 50% of SUV max were considered (Sridhar et al 2014).…”

“…Accurate tumor delineation in positron emission tomography (PET) is important for many clinical tasks, such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features (Jha et al 2017, Foster et al 2014, Mena et al 2017a. However, PET tumor delineation is challenging due to the relatively poor spatial resolution and high noise of PET images (Foster et al 2014).…”

“…To address these issues, several computer-aided segmentation methods for PET images have been developed (Foster et al 2014). Common methods include approaches based on thresholding (Foster et al 2014, Mena et al 2017a, Shah et al 2012, Mena et al 2017b, Sridhar et al 2014, gradient-detection (Werner-Wasik et al 2012), and using image-data statistics or assuming prior knowledge about the tumor (Foster et al 2014, Aristophanous et al 2007, Hatt et al 2009, Jha et al 2010, Soufi et al 2016, Layer et al 2015, Belhassen and Zaidi 2010. Those methods have demonstrated promise but suffer from limitations, such as requiring manual input (e.g.…”

PET Imaging in Pancreatic Cancer

“…But as for diagnosis of tumor itself, 18 F-FDG has its intrinsic limits. In order to overcome the overlaps between malignancy and benign lesions, dual-timepoint-imaging (DPI)--at approximately 1 hour (early) and 2 hours (delayed) after injection are recommended [48,49]. Delayed phase of 18 F-FDG imaging may increase primary lesion detectability due to higher FDG uptake in primary tumors compared to the early phase of imaging [50,51].…”