Current concepts in F18 FDG PET/CT-based radiation therapy planning for lung cancer

Abstract

Radiation therapy is an important component of cancer therapy for early stage as well as locally advanced lung cancer. The use of F18 FDG PET/CT has come to the forefront of lung cancer staging and overall treatment decision-making. FDG PET/CT parameters such as standard uptake value and metabolic tumor volume provide important prognostic and predictive information in lung cancer. Importantly, FDG PET/CT for radiation planning has added biological information in defining the gross tumor volume as well as involved nodal disease. For example, accurate target delineation between tumor and atelectasis is facilitated by utilizing PET and CT imaging. Furthermore, there has been meaningful progress in incorporating metabolic information from FDG PET/CT imaging in radiation treatment planning strategies such as radiation dose escalation based on standard uptake value thresholds as well as using respiratory-gated PET and CT planning for improved target delineation of moving targets. In addition, PET/CT-based follow-up after radiation therapy has provided the possibility of early detection of local as well as distant recurrences after treatment. More research is needed to incorporate other biomarkers such as proliferative and hypoxia biomarkers in PET as well as integrating metabolic information in adaptive, patient-centered, tailored radiation therapy.

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

“…4 (1) in its neighborhood. The commonly described neighborhood definitions are the 4-connected Von Neumann or the 8-connected Moore neighborhoods.…”

“…Consequently, the amount of normal tissue included in the target volume decreases and geometric mass is reduced. 1 However, uncertainty in target definition still contributes to the majority of the errors in radiation oncology. The inclusion of biological imaging may have the highest impact in the reduction of interobserver variability in target definitions.…”

“…The methods range from simple to complex methods. 1,3,4 The segmentation methods include but are not limited to constant and adaptive threshold methods, [5][6][7][8][9] region growing methods, [10][11][12] gradient-based methods, [13][14][15] fuzzy models, [16][17][18] and Gaussian mixture modeling. 10,19 All these methods offer different compromises in terms of versatility and performance and compare well against manual segmentations by clinicians or the pathological measurements with varying rates of success.…”

“…Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization 1 …”

Application of FDG-PET/CT in Radiation Oncology

“…(Erdi et al, 2002; Bradley et al, 2004; Lavrenkov et al, 2005; Nestle et al, 2005; Paulino et al, 2005; Greco et al, 2007; Lin et al, 2007; van Baardwijk et al, 2007; Dolezelova et al, 2008; Henriques de Figueiredo et al, 2009; MacManus et al, 2009; Yu et al, 2009; De Jong et al, 2010; Terezakis et al, 2011; Lee et al, 2012). …”