Willem Grootjans

PET in the management of locally advanced and metastatic NSCLC

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.

The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer

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 18F-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 = 0.02), 11.6% ± 11.8% (P = 0.006), 2.3% ± 2.2% (P = 0.002), and 16.8% ± 17.2% (P = 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.

The impact of respiratory gated positron emission tomography on clinical staging and management of patients with lung cancer

OBJECTIVES Respiratory motion artefacts during positron emission tomography (PET) deteriorate image quality, potentially introducing diagnostic uncertainties. The objective of this study was to determine the impact of optimal respiratory gating on clinical staging and management of patients with primary lung cancer. MATERIALS AND METHODS From our fast-track outpatient diagnostic program, 55 patients with primary lung cancer, who underwent whole body [(18)F]-fluorodeoxyglucose (FDG) PET, were included. Respiratory gating was performed on bed positions covering the thorax and abdomen. Independent reading was conducted by two nuclear medicine physicians. The observers scored the number and anatomical location of the lesions, lymph node basins and the presence of distant metastasis in non-gated and gated images. A tumor (T), lymph node (N), and metastasis (M) stage was assigned to each patient according to the 7th revision of the TNM classification. Staging accuracy was determined using histopathological data and follow-up CT imaging. In addition, a management plan was created for each patient based on non-gated and gated images by an experienced pulmonologist. RESULTS For nuclear medicine physician 1 and 2, respiratory gating resulted in detection of more lesions in five and eight patients (9% and 15%) respectively. However, this did not result in any migration in T or M-stage. Migration in N-stage was observed in four and seven patients (7% and 13%) for nuclear medicine physician 1 and 2 respectively. Staging accuracy was slightly improved when respiratory gating was performed. Furthermore, there was substantial agreement in patient management between non-gated and gated images. CONCLUSIONS Respiratory gating improved staging accuracy, mainly in assessment of lymph node involvement. However, the effect on patient management was limited due to the presence of already advanced disease stage in many patients. These findings suggest that the expected impact of respiratory gating will be solely on management of patients with early disease.

Evaluating the use of optimally respiratory gated 18F-FDG-PET in target volume delineation and its influence on radiation doses to the organs at risk in non-small-cell lung cancer patients

ObjectiveThis radiotherapy planning study evaluated tumour delineation using both optimally respiratory gated and nongated fluorine-18 fluorodeoxyglucose-PET (18F-FDG-PET). MethodsFor 22 non-small-cell lung tumours, both scans were used to create the nongated and gated (g) gross tumour volumes (GTVg) together with the accompanying clinical target volumes (CTV) and planning target volumes (PTV). The size of the target volumes (TV) was evaluated and the accompanying radiotherapy plans were created to study the radiation doses to the organs at risk (OAR). ResultsThe median volumes of GTVg, CTVg and PTVg were statistically significantly smaller compared with the corresponding nongated volumes, resulting in a median TV reduction of 0.5 cm3 (interquartile range 0.1–1.2), 1.5 cm3 (−0.2 to 7.0) and 2.3 cm3 (−0.5 to 11.3) for the GTVg, CTVg and PTVg, respectively. For the OAR, only the percentage of lung (GTV included) receiving at least 35 Gy was significantly smaller after gating, with a median difference in lung volume receiving at least 35 Gy of 5.7 cm3 (interquartile range −0.8 to 30.50). ConclusionCompared with nongated 18F-FDG-PET, the TVs obtained with optimally respiratory gated 18F-FDG-PET were significantly smaller, however, without a clinically relevant difference in radiation dose to the OAR.

Comparison of a Free-Breathing CT and an Expiratory Breath-Hold CT with Regard to Spatial Alignment of Amplitude-Based Respiratory-Gated PET and CT Images

Respiratory motion during PET has a significant effect on the quantification of radiotracer uptake in PET images. Even when respiratory motion is considered using PET gating techniques, inaccuracies in standardized uptake values can be caused by inappropriate attenuation correction due to a spatial mismatch between PET and CT. In this study, the effect of breath-hold CT imaging on the spatial match between CT and amplitude-based respiratory-gated PET images is investigated. Methods: Whole-body 18F-FDG PET/CT imaging was performed in 52 patients with 125 lung lesions. 18F-FDG PET was performed using optimized, amplitude-based respiratory gating. For CT, 36 patients were randomly assigned to the free-breathing (FB) group and 16 to the rest-expiratory breath-hold (BH) group. Spatial mismatch between the PET and CT images was quantified by measuring the distance between the centroids of PET and CT lesions and calculating the Jaccard similarity coefficient (JSC). Results: In the upper lobes, the average distance between the centroids of the PET and CT lesions was 4.7 ± 3.1 and 6.0 ± 3.0 mm for the FB and BH groups, respectively (P = 0.11). For the middle and lower lobes, the distances were 5.8 ± 4.3 and 5.1 ± 2.9 mm (P = 0.70), respectively, and for the central region 4.8 ± 4.6 and 5.6 ± 2.0 mm (P = 0.24), respectively. The JSC for the upper lobes was 0.28 ± 0.17 and 0.28 ± 0.19, for the FB and the BH group, respectively (P = 0.83). For the middle and lower lobes, the JSC was 0.22 ± 0.16 and 0.28 ± 0.18 (P = 0.20), respectively, and for the central region 0.39 ± 0.17 and 0.13 ± 0.04 (P = 0.04), respectively. Conclusion: Providing breathing instructions to the patients during the CT acquisition did not improve the spatial alignment between the respiratory-gated PET images and the CT images. The difficulty experienced in using this clinical protocol, such as patient compliance and operator dependence, emphasizes the need for other strategies.

Performance of 3DOSEM and MAP algorithms for reconstructing low count SPECT acquisitions

PURPOSE Low count single photon emission computed tomography (SPECT) is becoming more important in view of whole body SPECT and reduction of radiation dose. In this study, we investigated the performance of several 3D ordered subset expectation maximization (3DOSEM) and maximum a posteriori (MAP) algorithms for reconstructing low count SPECT images. MATERIALS AND METHODS Phantom experiments were conducted using the National Electrical Manufacturers Association (NEMA) NU2 image quality (IQ) phantom. The background compartment of the phantom was filled with varying concentrations of pertechnetate and indiumchloride, simulating various clinical imaging conditions. Images were acquired using a hybrid SPECT/CT scanner and reconstructed with 3DOSEM and MAP reconstruction algorithms implemented in Siemens Syngo MI.SPECT (Flash3D) and Hermes Hybrid Recon Oncology (Hyrid Recon 3DOSEM and MAP). Image analysis was performed by calculating the contrast recovery coefficient (CRC),percentage background variability (N%), and contrast-to-noise ratio (CNR), defined as the ratio between CRC and N%. Furthermore, image distortion is characterized by calculating the aspect ratio (AR) of ellipses fitted to the hot spheres. Additionally, the performance of these algorithms to reconstruct clinical images was investigated. RESULTS Images reconstructed with 3DOSEM algorithms demonstrated superior image quality in terms of contrast and resolution recovery when compared to images reconstructed with filtered-back-projection (FBP), OSEM and 2DOSEM. However, occurrence of correlated noise patterns and image distortions significantly deteriorated the quality of 3DOSEM reconstructed images. The mean AR for the 37, 28, 22, and 17mm spheres was 1.3, 1.3, 1.6, and 1.7 respectively. The mean N% increase in high and low count Flash3D and Hybrid Recon 3DOSEM from 5.9% and 4.0% to 11.1% and 9.0%, respectively. Similarly, the mean CNR decreased in high and low count Flash3D and Hybrid Recon 3DOSEM from 8.7 and 8.8 to 3.6 and 4.2, respectively. Regularization with smoothing priors could suppress these noise patterns at the cost of reduced image contrast. The mean N% was 6.4% and 6.8% for low count QSP and MRP MAP reconstructed images. Alternatively, regularization with an anatomical Bowhser prior resulted in sharp images with high contrast, limited image distortion, and low N% of 8.3% in low count images, although some image artifacts did occur. Analysis of clinical images suggested that the same effects occur in clinical imaging. CONCLUSION Image quality of low count SPECT acquisitions reconstructed with modern 3DOSEM algorithms is deteriorated by the occurrence of correlated noise patterns and image distortions. The artifacts observed in the phantom experiments can also occur in clinical imaging.

Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy

BACKGROUND AND PURPOSE This study evaluated the use of total lesion glycolysis (TLG) determined by different automatic segmentation algorithms, for early response monitoring in non-small cell lung cancer (NSCLC) patients during concomitant chemoradiotherapy. MATERIALS AND METHODS Twenty-seven patients with locally advanced NSCLC treated with concomitant chemoradiotherapy underwent (18)F-fluorodeoxyglucose (FDG) PET/CT imaging before and in the second week of treatment. Segmentation of the primary tumours and lymph nodes was performed using fixed threshold segmentation at (i) 40% SUVmax (T40), (ii) 50% SUVmax (T50), (iii) relative-threshold-level (RTL), (iv) signal-to-background ratio (SBR), and (v) fuzzy locally adaptive Bayesian (FLAB) segmentation. Association of primary tumour TLG (TLGT), lymph node TLG (TLGLN), summed TLG (TLGS=TLGT+TLGLN), and relative TLG decrease (ΔTLG) with overall-survival (OS) and progression-free survival (PFS) was determined using univariate Cox regression models. RESULTS Pretreatment TLGT was predictive for PFS and OS, irrespective of the segmentation method used. Inclusion of TLGLN improved disease and early response assessment, with pretreatment TLGS more strongly associated with PFS and OS than TLGT for all segmentation algorithms. This was also the case for ΔTLGS, which was significantly associated with PFS and OS, with the exception of RTL and T40. CONCLUSIONS ΔTLGS was significantly associated with PFS and OS, except for RTL and T40. Inclusion of TLGLN improves early treatment response monitoring during concomitant chemoradiotherapy with FDG-PET.

Radiomics in vulvar cancer: first clinical experience using18F-FDG PET/CT images

This study investigated whether radiomic features derived from preoperative PET images could predict both tumor biology and prognosis in women with invasive squamous cell carcinoma of the vulva. Methods: Patients were retrospectively included if they had a unifocal primary cancer at least 2.6 cm in diameter, received a preoperative 18F-FDG PET/CT scan followed by surgery, and had at least 6 mo of follow-up data. 18F-FDG PET images were analyzed by semiautomatically drawing a volume of interest on the primary tumor in each PET image, followed by extraction of 83 radiomic features. Unique radiomic features were identified by principal-component analysis (PCA), after which they were compared with histopathology using nonpairwise 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. Results: Forty women were included. PCA revealed 4 unique radiomic features, which were not associated with histopathologic characteristics such as grade, 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, correlated with OS (P = 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. Conclusion: 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.