Despite considerable improvements in the treatment options for advanced-stage non-small-cell lung cancer (NSCLC), disease-specific survival remains poor. With the aim of improving patient outcome, the treatment paradigm of locally advanced NSCLC has shifted from solely radiotherapy towards combined and intensified treatment approaches. Also, treatment for patients with stage IV (oligo)metastatic NSCLC has evolved rapidly, with therapeutic options that include a number of targeted agents, surgery, and stereotactic ablative radiotherapy. However, personalizing treatment to the individual patient remains difficult and requires monitoring of biological parameters responsible for treatment resistance to facilitate treatment selection, guidance, and adaptation. PET is a well-established molecular imaging platform that enables non-invasive quantification of many biological parameters that are relevant to both local and systemic therapy. With increasing clinical evidence, PET has gradually evolved from a purely diagnostic tool to a multifunctional imaging modality that can be utilized for treatment selection, adaptation, early response monitoring, and follow up in patients with NSCLC. Herein, we provide a comprehensive overview of the available clinical data on the use of this modality in this setting, and discuss future perspectives of PET imaging for the clinical management of patients with locally advanced and metastatic NSCLC.

Accurate measurement of intratumor heterogeneity using parameters of texture on PET images is essential for precise characterization of cancer lesions. In this study, we investigated the influence of respiratory motion and varying noise levels on quantification of textural parameters in patients with lung cancer. Methods: We used an optimal-respiratory-gating algorithm on the list-mode data of 60 lung cancer patients who underwent 18 F-FDG PET. The images were reconstructed using a duty cycle of 35% (percentage of the total acquired PET data). In addition, nongated images of varying statistical quality (using 35% and 100% of the PET data) were reconstructed to investigate the effects of image noise. Several global image-derived indices and textural parameters (entropy, high-intensity emphasis, zone percentage, and dissimilarity) that have been associated with patient outcome were calculated. The clinical impact of optimal respiratory gating and image noise on assessment of intratumor heterogeneity was evaluated using Cox regression models, with overall survival as the outcome measure. The threshold for statistical significance was adjusted for multiple comparisons using Bonferroni correction. Results: In the lower lung lobes, respiratory motion significantly affected quantification of intratumor heterogeneity for all textural parameters (P , 0.007) except entropy (P . 0.007). The mean increase in entropy, dissimilarity, zone percentage, and high-intensity emphasis was 1.3% ± 1.5% (P 5 0.02), 11.6% ± 11.8% (P 5 0.006), 2.3% ± 2.2% (P 5 0.002), and 16.8% ± 17.2% (P 5 0.006), respectively. No significant differences were observed for lesions in the upper lung lobes (P . 0.007). Differences in the statistical quality of the PET images affected the textural parameters less than respiratory motion, with no significant difference observed. The median follow-up time was 35 mo (range, 7-39 mo). In multivariate analysis for overall survival, total lesion glycolysis and high-intensity emphasis were the two most relevant image-derived indices and were considered to be independent significant covariates for the model regardless of the image type considered. Conclusion: The tested textural parameters are robust in the presence of respiratory motion artifacts and varying levels of image noise.

Quantifying lesion volume and uptake in PET is important for patient management. Respiratory motion artefacts introduce inaccuracies in quantification of PET images. Amplitude-based optimal respiratory gating maintains image quality through selection of duty cycle. The effect of respiratory gating on lesion quantification depends on anatomical location.

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This study investigates whether radiomic features derived from preoperative positron emission tomography (PET) images could predict both tumor biology and prognosis in women with invasive squamous cell carcinoma of the vulva. Patients were retrospectively included when they had a unifocal primary cancer of ≥ 2.6 cm in diameter, had received a preoperativeF-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) scan followed by surgery and had at least six months of follow-up data. F-FDG-PET images were analyzed by semi-automatically drawing on the primary tumor in each PET image, followed by the extraction of 83 radiomic features. Unique radiomic features were identified by principal component analysis (PCA), after which they were compared with histopathology using non-pairwise group comparison and linear regression. Univariate and multivariate Cox regression analyses were used to correlate the identified features with progression-free survival (PFS) and overall survival (OS). Survival curves were estimated using the Kaplan-Meier method. Forty women were included. PCA revealed four unique radiomic features, which were not associated with histopathologic characteristics such as grading, depth of invasion, lymph-vascular space invasion and metastatic lymph nodes. No statistically significant correlation was found between the identified features and PFS. However, Moran's I, a feature that identifies global spatial autocorrelation, was correlated with OS ( = 0.03). Multivariate Cox regression analysis showed that extracapsular invasion of the metastatic lymph nodes and Moran's I were independent prognostic factors for PFS and OS. Our data show that PCA is usable to identify specific radiomic features. Although the identified features did not correlate strongly with tumor biology, Moran's I was found to predict patient prognosis. Larger studies are required to establish the clinical relevance of the observed findings.

In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.

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PurposeMetabolic parameters are increasingly being used to characterize tumors. Motion artifacts due to patient respiration introduce uncertainties in quantification of metabolic parameters during positron emission tomography (PET) image acquisition. The present study investigates the impact of amplitude-based optimal respiratory gating (ORG) on quantification of PET-derived image features in patients with pancreatic ductal adenocarcinoma (PDAC), in correlation with overall survival (OS).MethodsSixty-nine patients with histologically proven primary PDAC underwent 2′-deoxy-2′-[18F]fluoroglucose ([18F]FDG) PET/CT imaging during diagnostic work-up. Standard image acquisition and reconstruction was performed in accordance with the EANM guidelines and ORG images were reconstructed with a duty cycle of 35%. PET-derived image features, including standard parameters, first- and second-order texture features, were calculated from the standard and corresponding ORG images, and correlation with OS was assessed.ResultsORG significantly impacts the quantification of nearly all features; values of single-voxel parameters (e.g., SUVmax) showed a wider range, volume-based parameters (e.g., SUVmean) were reduced, and texture features were significantly changed. After correction for motion artifacts using ORG, some features that describe intra-tumoral heterogeneity were more strongly correlated to OS.ConclusionsCorrection for respiratory motion artifacts using ORG impacts the quantification of metabolic parameters in PDAC lesions. The correlation of metabolic parameters with OS was significantly affected, in particular parameters that describe intra-tumor heterogeneity. Therefore, interpretation of single-voxel or average metabolic parameters in relation to clinical outcome should be done cautiously. Furthermore, ORG is a valuable tool to improve quantification of intra-tumoral heterogeneity in PDAC.Electronic supplementary materialThe online version of this article (10.1186/s13550-019-0492-y) contains supplementary material, which is available to authorized users.