Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

Virginia Liberini,Michael Messerli,Martin W Huellner,Ivette Engel-Bicik,Fotis Kotasidis,Erika Orita,Alexander Siebenhüner,Valerie Treyer

doi:10.1038/s41598-020-80855-4

Abstract

To evaluate whether quantitative PET parameters of motion-corrected 68Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent 68Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM1000). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM1000 reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM1000 reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM1000/OSEM + DDG/BSREM1000 + DDG, respectively. BSREM1000 + DDG can accurately differentiate pNET from accessory spleen. Both BSREM1000 and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.

Highlights

Abbreviations area under the Receiver operating characteristics (ROC) curve (AUC) Area under the curve BMI Body mass index BPL Bayesian penalized likelihood BSREM Block sequential regularized expectation maximization CT Computed tomography
A single accessory spleen was present in 33 scans (31 scans with one accessory spleen and 2 scans with one accessory spleen and one pancreatic neuroendocrine tumors (pNET)), two accessory spleens were presents in 5 scans
Out of 498 cases, pNET lesions were detected in 9 PET/CT scans (1.8%; mean age 64.2, range 44–74 years), 2 with 1 accessory spleen and 1 pNET already included in the accessory spleen cohort

Summary

Introduction

Abbreviations AUC Area under the curve BMI Body mass index BPL Bayesian penalized likelihood BSREM Block sequential regularized expectation maximization CT Computed tomography. Accessory spleens are relatively common, mostly solitary and have no gender predilection. Intrapancreatic accessory spleens (IPAS) appear as a solid contrast-enhancing mass, usually smaller than 3 cm, located within the tail of the p ancreas[7,8,9]. Owing to their morphology and contrast characteristics, they may be mistaken for pancreatic tumors, in particular neuroendocrine tumors[10]. Accessory spleens can be diagnosed with Tc-99m-labelled colloids These colloids are taken up into splenic tissue due to phagocytosis in the reticulum-endothelial cells and, can identify functioning ectopic splenic tissue[11]. The sensitivity of these exams is hampered by the small size of most IPAS and by the comparably low resolution of conventional scintigraphy, limiting a more widespread use of these techniques

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