Healthy tissue metabolism assessed by [18F]FDG PET/CT as a marker of prognosis and adverse events in advanced Hodgkin lymphoma patients.

The aim of the study was to assess healthy tissue metabolism (HTM) using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) during chemotherapy in Hodgkin lymphoma (HL) and the association of HTM with baseline metabolic tumour volume (MTV), haematological parameters, adverse events (AEs), early response and progression-free survival (PFS). We retrospectively identified 200 patients with advanced HL from the RATHL trial with [18F]FDG-PET/CT before (PET0) and following 2 cycles of chemotherapy (PET2). [18F]FDG-uptake was measured in bone marrow (BM), spleen, liver and mediastinal blood pool (MBP). Deauville score (DS) 1-3 was used to classify responders and DS 4-5, non-responders. [18F]FDG-uptake decreased significantly in BM and spleen and increased in liver and MBP at PET2 (all p < 0.0001), but was not associated with MTV. Higher BM uptake at PET0 was associated with lower baseline haemoglobin and higher absolute neutrophil counts, platelets, and white blood cells. High BM, spleen, and liver uptake at PET0 was associated with neutropenia after cycles 1-2. BM uptake at PET0 was associated with treatment failure at PET2 and non-responders with higher BM uptake at PET2 had significantly inferior PFS (p = 0.023; hazard ratio = 2.31). Based on these results, we concluded that the change in HTM during chemotherapy was most likely a direct impact of chemotherapy rather than a change in MTV. BM uptake has prognostic value in HL.

Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer.

Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.

Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers.

PURPOSE: New total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data. METHODS: Twenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood-brain barrier. RESULTS: CBF ranged from 37 to 69 mL blood min-1 100 mL of tissue-1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively. CONCLUSION: The high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.

Tumor volume definitions in head and neck squamous cell carcinoma - Comparing PET/MRI and histopathology.

BACKGROUND AND PURPOSE: In cancer treatment precise definition of the tumor volume is essential, but despite development in imaging modalities, this remains a challenge. Here, pathological tumor volumes from the surgical specimens were obtained and compared to tumor volumes defined from modern PET/MRI hybrid imaging. The purpose is to evaluate mismatch between the volumes defined from imaging and pathology was estimated and potential clinical impact. METHODS AND MATERIALS: Twenty-five patients with head and neck squamous cell carcinoma were scanned on an integrated PET/MRI system prior to surgery. Three gross tumor volumes (GTVs) from the primary tumor site were delineated defined from MRI (GTVMRI), PET (GTVPET) and one by utilizing both anatomical images and clinical information (GTVONCO). Twenty-five primary tumor specimens were extracted en bloc, scanned with PET/MRI and co-registered to the patient images. Each specimen was sectioned in blocks, sliced and stained with haematoxylin and eosin. All slices were digitalized and tumor delineated by a head and neck pathologist. The pathological tumor areas in all slices were interpolated yielding a pathological 3D tumor volume (GTVPATO). GTVPATOwas compared with the imaging GTV's and potential mismatch was estimated. RESULTS: Thirteen patients were included. The mean volume of GTVONCOwas larger than the GTV's defined from PET or MRI. The mean mismatch of the GTVPATOcompared to the GTVPET, GTVMRIand GTVONCOwas 31.9 %, 54.5 % and 27.9 % respectively, and the entire GTVPATO was only fully encompassed in GTVONCO in 1 of 13 patients. However, after the addition of a clinical 5 mm margin the GTVPATO was fully encompassed in GTVONCO in 11 out of 13 patients. CONCLUSIONS: Despite modern hybrid imaging modalities, a mismatch between imaging and pathological defined tumor volumes was observed in all patients.A 5 mm clinical margin was sufficient to ensure inclusion of the entire pathological volume in 11 out of 13 patients.

New PET Tracers: Current Knowledge and Perspectives in Lung Cancer.

PET/CT with the tracer 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) has improved diagnostic imaging in cancer and is routinely used for diagnosing, staging and treatment planning in lung cancer patients. However, pitfalls of [18F]FDG-PET/CT limit the use in specific settings. Additionally, lung cancer is still the leading cause of cancer associated death and has high risk of recurrence after curative treatment. These circumstances have led to the continuous search for more sensitive and specific PET tracers to optimize lung cancer diagnosis, staging, treatment planning and evaluation. The objective of this review is to present and discuss current knowledge and perspectives of new PET tracers for use in lung cancer. A literature search was performed on PubMed and clinicaltrials.gov, limited to the past decade, excluding case reports, preclinical studies and studies on established tracers such as [18F]FDG and DOTATE. The most relevant papers from the search were evaluated. Several tracers have been developed targeting specific tumor characteristics and hallmarks of cancer. A small number of tracers have been studied extensively and evaluated head-to-head with [18F]FDG-PET/CT, whereas others need further investigation and validation in larger clinical trials. At this moment, none of the tracers can replace [18F]FDG-PET/CT. However, they might serve as supplementary imaging methods to provide more knowledge about biological tumor characteristics and visualize intra- and inter-tumoral heterogeneity.

Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients.

BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. METHODS: Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PETDeep). (2) Dixon MRI using the vendor-provided atlas-based method (PETAtlas). (3) CT, serving as reference (PETCT). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. RESULTS: The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PETDeep and -1.3 ± 21.8% for PETAtlas. The error in mean PET uptake in bone/air was much lower for PETDeep (-4%/12%) than for PETAtlas (-15%/84%) and PETDeep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was -0.6 ± 2.0% for PETDeep and -3.5 ± 4.6% for PETAtlas. CONCLUSION: The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.

Test-retest repeatability and interobserver variation of healthy tissue metabolism using 18F-FDG PET/CT of the thorax among lung cancer patients.

OBJECTIVES: The aim of this study was to assess the test-retest repeatability and interobserver variation in healthy tissue (HT) metabolism using 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) PET/computed tomography (PET/CT) of the thorax in lung cancer patients. METHODS: A retrospective analysis was conducted in 22 patients with non-small cell lung cancer who had two PET/CT scans of the thorax performed 3 days apart with no interval treatment. The maximum, mean and peak standardized uptake values (SUVs) in different HTs were measured by a single observer for the test-retest analysis and two observers for interobserver variation. Bland-Altman plots were used to assess the repeatability and interobserver variation. Intrasubject variability was evaluated using within-subject coefficients of variation (wCV). RESULTS: The wCV of test-retest SUVmean measurements in mediastinal blood pool, bone marrow, skeletal muscles and lungs was less than 20%. The left ventricle (LV) showed higher wCV (>60%) in all SUV parameters with wide limits of repeatability. High interobserver agreement was found with wCV of less than 10% in SUVmean of all HT, but up to 22% was noted in the LV. CONCLUSION: HT metabolism is stable in a test-retest scenario and has high interobserver agreement. SUVmean was the most stable metric in organs with low FDG uptake and SUVpeak in HTs with moderate uptake. Test-retest measurements in LV were highly variable irrespective of the SUV parameters used for measurements.

A case-control evaluation of pulmonary and extrapulmonary findings of incidental asymptomatic COVID-19 infection on FDG PET-CT.

OBJECTIVES: To describe the findings of incidental asymptomatic COVID-19 infection on FDG PET-CT using a case-control design. METHODS: Incidental pulmonary findings suspicious of asymptomatic COVID-19 infection on FDG PET-CT were classified as a confirmed (positive RT-PCR test) or suspected case (no/negative RT-PCR test). Control cases were identified using a 4:1 control:case ratio. Pulmonary findings were re-categorised by two reporters using the BSTI classification. SUV metrics in ground glass opacification (GGO)/consolidation (where present), background lung, intrathoracic nodes, liver, spleen and bone marrow were measured. RESULTS: 7/9 confirmed and 11/15 suspected cases (COVID-19 group) were re-categorised as BSTI 1 (classic/probable COVID-19) or BSTI 2 (indeterminate COVID-19); 0/96 control cases were categorised as BSTI 1. Agreement between two reporters using the BSTI classification was almost perfect (weighted κ = 0.94). SUVmax GGO/consolidation (5.1 vs 2.2; p < 0.0001) and target-to-background ratio, normalised to liver SUVmean (2.4 vs 1.0; p < 0.0001) were higher in the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases. SUVmax GGO/consolidation discriminated between the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases with high accuracy (AUC = 0.93). SUV metrics were higher (p < 0.05) in the COVID-19 group vs control cases in the lungs, intrathoracic nodes and spleen. CONCLUSION: Asymptomatic COVID-19 infection on FDG PET-CT is characterised by bilateral areas of FDG avid (intensity > x2 liver SUVmean) GGO/consolidation and can be identified with high interobserver agreement using the BSTI classification. There is generalised background inflammation within the lungs, intrathoracic nodes and spleen. ADVANCES IN KNOWLEDGE: Incidental asymptomatic COVID-19 infection on FDG PET-CT, characterised by bilateral areas of ground glass opacification and consolidation, can be identified with high reproducibility using the BSTI classification. The intensity of associated FDG uptake (>x2 liver SUVmean) provides high discriminative ability in differentiating such cases from pulmonary findings in a non-COVID-19 pattern. Asymptomatic COVID-19 infection causes a generalised background inflammation within the mid-lower zones of the lungs, hilar and central mediastinal nodal stations, and spleen on FDG PET-CT.

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging-Based Radiation Therapy Planning of Patients With Head and Neck Cancer.

PURPOSE: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI. METHODS AND MATERIALS: Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk. RESULTS: The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort. CONCLUSIONS: We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method.

Intratumor heterogeneity is biomarker specific and challenges the association with heterogeneity in multimodal functional imaging in head and neck squamous cell carcinoma.

RATIONALE: Tumor biopsy cannot detect heterogeneity and an association between heterogeneity in functional imaging and molecular biology will have an impact on both diagnostics and treatment possibilities. PURPOSE: Multiparametric imaging can provide 3D information on functional aspects of a tumor and may be suitable for predicting intratumor heterogeneity. Here, we investigate the correlation between intratumor heterogeneity assessed with multiparametric imaging and multiple-biopsy immunohistochemistry. METHODS: In this prospective study, patients with primary or recurrent head and neck squamous cell carcinoma (HNSCC) underwent PET/MRI scanning prior to surgery. Tumors were removed en bloc and six core biopsies were used for immunohistochemical (IHC) staining with a predefined list of biomarkers: p40, p53, EGFR, Ki-67, GLUT1, VEGF, Bcl-2, CAIX, PD-L1. Intratumor heterogeneity of each IHC biomarker was quantified by calculating the coefficient of variation (CV) in tumor proportion score among the six core biopsies within each tumor lesion. The heterogeneity in the imaging biomarkers was assessed by calculating CV in 18F-fluorodeoxyglucose (FDG)-uptake, diffusion and perfusion. Concordance of the two variance measures was quantified using Spearman's rank correlation RESULTS: Twenty-eight patients with a total of 33 lesions were included. There was considerable heterogeneity in most of the IHC biomarkers especially in GLUT1, PD-L1, Ki-67, CAIX and p53, however we only observed a correlation between the heterogeneity in GLUT1 and p53 and between Ki-67 and EGFR. Heterogeneity in FDG uptake and diffusion correlated with heterogeneity in cell density. CONCLUSION: Considerable heterogeneity of IHC biomarkers was found, however, only few and weak correlations between the studied IHC markers were observed. The studied functional imaging biomarkers showed weak associations with heterogeneity in some of the IHC biomarkers. Thus, biological heterogeneity is not a general tumor characteristic but depends on the specific biomarker or imaging modality.

Effect of Follow-Up Surveillance After Curative-Intent Treatment of NSCLC on Detection of New and Recurrent Disease, Retreatment, and Survival: A Systematic Review and Meta-Analysis.

INTRODUCTION: Patients with NSCLC may be treated with curative intent, yet they remain at high risk of both disease recurrence and second primary lung cancer (SPLC) and increased risk of early death. Guidelines provide recommendations for follow-up, but there is little consensus, and review of available evidence is necessary. The use of a systematic follow-up strategy for the detection of disease recurrence or SPLC after curative-intent treatment of NSCLC may increase the proportion of patients available for retreatment and increase the survival of patients with surveillance detection. METHODS: We performed a systematic review and meta-analysis of prospective studies on follow-up of NSCLC after curative-intent treatment to answer the following three questions: What is the effect of follow-up on detection of recurrence or SPLC? What is the effect of surveillance detection on curative-intent retreatment? What is the survival impact? RESULTS: Recurrence or SPLC was observed in 17.8% to 71% of patients. Scheduled imaging-detected recurrence in 60% to 100% of cases, yet neither computed tomography-based (OR = 2.31, 95% confidence interval [CI]: 0.27-19.49, p = 0.44) nor positron emission tomography-computed tomography-based follow-up (OR = 1.431, 95% CI: 0.92-2.22, p = 0.12) was statistically superior to standard follow-up strategies. Detection of disease recurrence/SPLC significantly increased the odds of curative-intent retreatment (OR = 4.31; 95% CI: 2.10-8.84, p < 0.0001). Curative-intent retreatment prolonged survival in reported studies. CONCLUSIONS: The early detection of disease recurrence/SPLC may increase the likelihood of curative-intent retreatment and prolong survival. There is a clear need for prospective randomized controlled studies of follow-up to confirm effectiveness of available follow-up modalities.

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multiparametric PET/MRI of patients with head and neck cancer (HNC) in RT treatment position. As a step toward planning RT based solely on PET/MRI, a deep learning approach was employed to generate synthetic computed tomography (sCT) from MRI. This was subsequently evaluated for dose calculation and PET attenuation correction (AC). METHODS AND MATERIALS: Eleven patients, including 3 pilot patients referred for RT of HNC, underwent PET/MRI in treatment position after a routine fluorodeoxyglucose-PET/CT planning scan. The PET/MRI scan protocol included multiparametric imaging. A convolutional neural network was trained in a leave-one-out process to predict sCT from the Dixon MRI. The clinical CT-based dose plans were recalculated on sCT, and the plans were compared in terms of relative differences in mean, maximum, near-maximum, and near-minimum absorbed doses for different volumes of interest. Comparisons between PET with sCT-based AC and PET with CT-based AC were assessed based on the relative differences in mean and maximum standardized uptake values (SUVmean and SUVmax) from the PET-positive volumes. RESULTS: All 11 patients underwent PET/MRI in RT treatment position. Apart from the 3 pilots, full multiparametric imaging was completed in 45 minutes for 7 out of 8 patients. One patient terminated the examination after 30 minutes. With the exception of 1 patient with an inserted tracheostomy tube, all dosimetric parameters of the sCT-based dose plans were within ±1% of the CT-based dose plans. For PET, the mean difference was 0.4 ± 1.2% for SUVmean and -0.5 ± 1.0% for SUVmax. CONCLUSIONS: Performing multiparametric PET/MRI of patients with HNC in RT treatment position was clinically feasible. The sCT generation resulted in AC of PET and dose calculations sufficiently accurate for clinical use. These results are an important step toward using multiparametric PET/MRI as a one-stop shop for personalized RT planning.

Does multiparametric imaging with 18F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma?

BACKGROUND: The purpose of this study is to test if functional multiparametric imaging with 18F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy. METHODS: Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and Ktrans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis. RESULTS: Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy. CONCLUSION: We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.

Circulating cell free DNA during definitive chemo-radiotherapy in non-small cell lung cancer patients - initial observations.

BACKGROUND: The overall aim was to investigate the change over time in circulating cell free DNA (cfDNA) in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing concurrent chemo-radiotherapy. Furthermore, to assess the possibility of detecting circulating cell free tumor DNA (ctDNA) using shallow whole genome sequencing (sWGS) and size selection. METHODS: Ten patients were included in a two-phase study. The first four patients had blood samples taken prior to a radiation therapy (RT) dose fraction and at 30 minutes, 1 hour and 2 hours after RT to estimate the short-term dynamics of cfDNA concentration after irradiation. The remaining six patients had one blood sample taken on six treatment days 30 minutes post treatment to measure cfDNA levels. Presence of ctDNA as indicated by chromosomal aberrations was investigated using sWGS. The sensitivity of this method was further enhanced using in silico size selection. RESULTS: cfDNA concentration from baseline to 120 min after therapy was stable within 95% tolerance limits of +/- 2 ng/ml cfDNA. Changes in cfDNA were observed during therapy with an apparent qualitative difference between adenocarcinoma (average increase of 0.69 ng/ml) and squamous cell carcinoma (average increase of 4.0 ng/ml). Tumor shrinkage on daily cone beam computer tomography scans during radiotherapy did not correlate with changes in concentration of cfDNA. CONCLUSION: Concentrations of cfDNA remain stable during the first 2 hours after an RT fraction. However, based on the sWGS profiles, ctDNA represented only a minor fraction of cfDNA in this group of patients. The detection sensitivity of genomic alterations in ctDNA strongly increases by applying size selection.

Preparing data for multiparametric PET/MR imaging: Influence of PET point spread function modelling and EPI distortion correction on the spatial correlation of [18F]FDG-PET and diffusion-weighted MRI in head and neck cancer.

PURPOSE: Multiparametric imaging holds great potential for characterization of disease heterogeneity. For integrated PET/MR imaging, the combination of 18F-flourodeoxyglucose (FDG) PET and diffusion weighted imaging (DWI) has been suggested for the assessment of tumor heterogeneity. However, PET image resolution is limited and DWI is prone to image distortions. The aim of this study was to assess the influence of PET point spread function (PSF) modelling and DWI distortion correction on the voxelwise correlation between FDG-PET and DWI. METHODS: Data were collected from 11 patients with head and neck cancer, each undergoing PET/MR imaging twice. PET reconstructions with and without PSF modelling and DWI with and without distortion correction were derived. Tumor SUV was compared between PET reconstructions by linear regression. Geometric distortions of DWI with and without distortion correction were quantified by voxelwise correlation coefficients to an undistorted anatomical reference. The influence of PSF modelling and DWI distortion correction on a multiparametric analysis was assessed as a change of the voxelwise correlation coefficient between FDG-PET and DWI measured in tumors. RESULTS: The inclusion of PSF modelling in the PET reconstruction affected tumor quantification by a 10-20% increase in SUV. Distortion correction reduced DWI geometric distortions significantly. The impact of PET PSF modelling on the spatial correlation with DWI was insignificant. However, distortion correction of DWI had a significant effect on the spatial correlation with PET. CONCLUSIONS: Proper preparation of the imaging modalities is important for a correct analysis and interpretation of multiparametric PET/MR imaging of head and neck cancer.

Intratumor heterogeneity of PD-L1 expression in head and neck squamous cell carcinoma.

Intratumor heterogeneity may contribute to the ambiguous clinical results on PD-L1 status as a predictor for immunotherapy response in patients with HNSCC. This decreases the utility of PD-L1 expression from single tumour biopsies as a predictive biomarker. In this prospective study, intratumor heterogeneity of PD-L1 expression in HNSCC was investigated with both Tumour Proportion Score (TPS) and Combined Positive Score (CPS). Thirty-three whole surgical specimens from 28 patients with HNSCC were included. PD-L1 expression in six random core biopsies from each surgical specimen was used to assess the concordance between multiple biopsies and the negative predictive value of a single negative core biopsy. With 1% cut off, 36% of the specimens were concordant with TPS and 52% with CPS. With a 50% cut-off value the concordance was 70% with TPS and 55% with CPS. Defining a tumour as positive if just a single-one of the biopsies was positive, the negative predictive value (NPV) of a single negative core biopsy was 38.9 and 0% (1% cut off), and 79.9% and 62.8% (50% cut off) for TPS and CPS, respectively. In conclusion, PD-L1 positivity varies markedly within the tumour, both with TPS and CPS, challenging the utility of this biomarker.

PET/DW-MRI for evaluating treatment in chronic hepatitis C patients.

This feasibility study set out to investigate the use of FDG-PET/DW-MRI in chronic hepatitis C patients to examine changes in local liver inflammation after treatment with direct-acting antivirals (DAA). Twelve patients with chronic hepatitis C were prospectively enrolled, performing FDG-PET/DW-MRI prior to and after DAA treatment. PET/DW-MRI included PET acquisition 60 and 90 min after FDG-injection, DIXON, for attenuation correction, T2- and DW-MRI with 10 b-values between 0-700 s/mm2. The following parameters were measured from fusion of 3 volumes of interest (VOIs) placed in the liver parenchyma: Mean standard uptake value after 60 and 90 minutes (SUVmean60 and SUVmean90), total Apparent Diffusion Coefficient (ADC), perfusion fraction (PF), pseudo-diffusion (D*) and perfusion-free diffusion (D). We found PET/DW-MRI of chronic hepatitis C patients to be feasible. Patients were cooperative, tolerated the scans well and the image quality was acceptable. A total of 10 patients were available for final analysis. All patients achieved sustained virologic response and normalized alanine-aminotransferase (ALAT) levels after treatment with DAA. Perfusion fraction measured by DW-MRI changed significantly after treatment, from mean 0.21 (± 0.04) to 0.26 (± 0.06), P=0.005 and D* from 0.50 (± 0.13) × 10-3 s/mm2 to 0.62 (± 0.15) × 10-3 s/mm2, P=0.028. All other parameters, including FDG-uptake, was unchanged. These results suggest that liver perfusion is changed shortly after DAA treatment, with no significant change in inflammation. The study concludes that PET/DW-MR is feasible in quantifying perfusion and possibly inflammation in chronic hepatitis C patients and may be used to follow treatment.

A clinical prognostic model compared to the newly adopted UICC staging in an independent validation cohort of P16 negative/positive head and neck cancer patients.

OBJECTIVES: A previously published prognostic model in patients with head and neck squamous cell carcinoma (HNSCC) was validated in both a p16-negative and a p16-positive independent patient cohort and the performance was compared with the newly adopted 8th edition of the UICC staging system. MATERIALS AND METHODS: Consecutive patients with HNSCC treated at a single institution from 2005 to 2012 were included. The cohort was divided in three. 1.) Training cohort, patients treated from 2005 to 2009 excluding patients with p16-positive oropharyngeal squamous cell carcinomas (OPSCC); 2.) A p16-negative validation cohort and 3.) A p16-positive validation cohort. A previously published prognostic model (clinical model) with the significant covariates (smoking status, FDG uptake, and tumor volume) was refitted in the training cohort and validated in the two validation cohorts. The clinical model was used to generate four risk groups based on the predicted risk of disease recurrence after 2 years and the performance was compared with UICC staging 8th edition using concordance index. RESULTS: Overall 568 patients were included. Compared to UICC the clinical model had a significantly better concordance index in the p16-negative validation cohort (AUC = 0.63 for UICC and AUC = 0.73 for the clinical model; p = 0.003) and a borderline significantly better concordance index in the p16-positive cohort (AUC = 0.63 for UICC and 0.72 for the clinical model; p = 0.088). CONCLUSION: The validated clinical model provided a better prognostication of risk of disease recurrence than UICC stage in the p16-negative validation cohort, and similar prognostication as the newly adopted 8th edition of the UICC staging in the p16-positive patient cohort.

Effect of Lactobacillus rhamnosus GG Supplementation on Intestinal Inflammation Assessed by PET/MRI Scans and Gut Microbiota Composition in HIV-Infected Individuals.

BACKGROUND: Alterations in the gut microbiome have been associated with inflammation and increased cardiovascular risk in HIV-infected individuals. The aim of this study was to investigate the effects of the probiotic strain Lactobacillus rhamnosus GG (LGG) on intestinal inflammation, gut microbiota composition, and systemic markers of microbial translocation and inflammation in HIV-infected individuals. METHODS: This prospective, clinical interventional trial included 45 individuals [15 combination antiretroviral treatment (cART) naive and 30 cART treated] who ingested LGG twice daily at a dosage of 6 × 109 colony-forming units per capsule for a period of 8 weeks. Intestinal inflammation was assessed using F-2-fluoro-2-deoxy-D-glucose positron emission tomography/magnetic resonance imaging (F-FDG PET/MRI) scans in 15 individuals. Gut microbiota composition (V3-V4 region of the 16s rRNA gene) and markers of microbial translocation and inflammation (lipopolysaccharide, sCD14, sCD163, sCD25, high-sensitive CRP, IL-6, and tumor necrosis factor-alpha) were analyzed at baseline and after intervention. RESULTS: At baseline, evidence of intestinal inflammation was found in 75% of the participants, with no significant differences between cART-naive and cART-treated individuals. After LGG supplementation, a decrease in intestinal inflammation was detected on PET/MRI (-0.3 mean difference in the combined activity grade score from 6 regions, P = 0.006), along with a reduction of Enterobacteriaceae (P = 0.018) and Erysipelotrichaceae (P = 0.037) in the gut microbiome, with reduced Enterobacteriaceae among individuals with decreased F-FDG uptake on PET/MRI (P = 0.048). No changes were observed for soluble markers of microbial translocation and inflammation. CONCLUSIONS: A decrease in intestinal inflammation was found in HIV-infected individuals after ingestion of LGG along with a reduced abundance of Enterobacteriaceae, which may explain the local anti-inflammatory effect in the gut.