Low incidence of malignancy in patients with suspected polymyalgia rheumatica or giant cell arteritis, examined with FDG-PET/CT.

Introduction: The need to systematically examine patients suspected of polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) for malignancy is controversial. The aim of this study was to assess the frequency of malignancy in patients with suspected PMR and/or GCA who have been referred to a 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography with computed tomography (FDG-PET/CT) as part of the diagnostic investigation. Method: The records of all patients referred to FDG-PET/CT from Center for Rheumatology and Spine Diseases, Rigshospitalet, Glostrup with the suspicion of PMR and/or GCA during a two-year period, were retrospectively reviewed. Data was analyzed with descriptive statistics, and a standard incidence ratio was calculated based on background cancer incidences extracted from the NORDCAN database. Results: 220 patients were included in the study. Findings suspicious of malignancy were found in 19 of the examinations, and in seven cases (3.2%), malignancy was confirmed. In three out of the seven cases the patients were diagnosed with PMR concomitantly with malignancy. The estimated standardized incidence ratio (SIR) for cancer compared to the background incidence of cancer in Denmark was 1.58 (95% CI 0.63-2.97), i.e., not statistically significant. There were no statistically significant differences in characteristics of the patients that were diagnosed with malignancy compared with those that were not. Conclusion: The frequency of malignancy in this cohort of patients with suspected PMR/GCA who underwent PET/CT was low. Our results, though based on a small cohort, do not suggest that all patients with suspected PMR/GCA should systematically be examined with FDG-PET/CT for excluding malignancy.

Use of [18F]Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography after Curative Treatment of Non-Small-Cell Lung Cancer Patients: A Nationwide Cohort Study.

[¹8F]Fluorodeoxyglucose positron emission tomography/computed tomography ([¹8F]FDG PET/CT) is a valuable imaging tool in the post-treatment management of non-small-cell lung cancer (NSCLC). This study aimed to investigate the trends in utilization and factors associated with the use of [¹8F]FDG PET/CT after curative-intent treatment. Data from 13,758 NSCLC patients diagnosed between 2007 and 2020 identified in the Danish Lung Cancer Registry, who underwent curative-intent treatment, were analyzed using multivariable regression. The results showed a significant increase in the use of [¹8F]FDG PET/CT scans, from 10.4 per 100 patients per year in 2007 to 39.6 in 2013, followed by a period of stability. Higher utilization rates were observed in patients who received radiotherapy (22% increase compared to surgical resection) and in patients with stage II-III disease (14% and 20% increase compared to stage I, respectively). Additionally, utilization was increased when other diagnostic procedures were performed, such as MRI, ultrasound, endoscopy, and biopsy. These findings highlight an increasing reliance on [¹8F]FDG PET/CT in post-treatment NSCLC, especially after radiotherapy and in patients with locally advanced disease, where treatment-induced radiographic changes and an increased risk of recurrence present a significant diagnostic challenge.

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images.

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmaxestimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmeanwithin the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmaxand current practices of manually defining a volume of interest in the organ should be considered instead.

Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers.

Recent advancements in PET/CT, including the emergence of long axial field-of-view (LAFOV) PET/CT scanners, have increased PET sensitivity substantially. Consequently, there has been a significant reduction in the required tracer activity, shifting the primary source of patient radiation dose exposure to the attenuation correction (AC) CT scan during PET imaging. This study proposes a parameter-transferred conditional generative adversarial network (PT-cGAN) architecture to generate synthetic CT (sCT) images from non-attenuation corrected (NAC) PET images, with separate networks for [18F]FDG and [15O]H2O tracers. The study includes a total of 1018 subjects (n = 972 [18F]FDG, n = 46 [15O]H2O). Testing was performed on the LAFOV scanner for both datasets. Qualitative analysis found no differences in image quality in 30 out of 36 cases in FDG patients, with minor insignificant differences in the remaining 6 cases. Reduced artifacts due to motion between NAC PET and CT were found. For the selected organs, a mean average error of 0.45% was found for the FDG cohort, and that of 3.12% was found for the H2O cohort. Simulated low-count images were included in testing, which demonstrated good performance down to 45 s scans. These findings show that the AC of total-body PET is feasible across tracers and in low-count studies and might reduce the artifacts due to motion and metal implants.

Potential Clinical Impact of LAFOV PET/CT: A Systematic Evaluation of Image Quality and Lesion Detection.

We performed a systematic evaluation of the diagnostic performance of LAFOV PET/CT with increasing acquisition time. The first 100 oncologic adult patients referred for 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose PET/CT on the Siemens Biograph Vision Quadra were included. A standard imaging protocol of 10 min was used and scans were reconstructed at 30 s, 60 s, 90 s, 180 s, 300 s, and 600 s. Paired comparisons of quantitative image noise, qualitative image quality, lesion detection, and lesion classification were performed. Image noise (n = 50, 34 women) was acceptable according to the current standard of care (coefficient-of-varianceref < 0.15) after 90 s and improved significantly with increasing acquisition time (PB < 0.001). The same was seen in observer rankings (PB < 0.001). Lesion detection (n = 100, 74 women) improved significantly from 30 s to 90 s (PB < 0.001), 90 s to 180 s (PB = 0.001), and 90 s to 300 s (PB = 0.002), while lesion classification improved from 90 s to 180 s (PB < 0.001), 180 s to 300 s (PB = 0.021), and 90 s to 300 s (PB < 0.001). We observed improved image quality, lesion detection, and lesion classification with increasing acquisition time while maintaining a total scan time of less than 5 min, which demonstrates a potential clinical benefit. Based on these results we recommend a standard imaging acquisition protocol for LAFOV PET/CT of minimum 180 s to maximum 300 s after injection of 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose.

Impact of Reduced Image Noise on Deauville Scores in Patients with Lymphoma Scanned on a Long-Axial Field-of-View PET/CT-Scanner.

BACKGROUND: Total body and long-axial field-of-view (LAFOV) PET/CT represent visionary innovations in imaging enabling either improved image quality, reduction in injected activity-dose or decreased acquisition time. An improved image quality may affect visual scoring systems, including the Deauville score (DS), which is used for clinical assessment of patients with lymphoma. The DS compares SUVmax in residual lymphomas with liver parenchyma, and here we investigate the impact of reduced image noise on the DS in patients with lymphomas scanned on a LAFOV PET/CT. METHODS: Sixty-eight patients with lymphoma underwent a whole-body scan on a Biograph Vision Quadra PET/CT-scanner, and images were evaluated visually with regard to DS for three different timeframes of 90, 300, and 600 s. SUVmax and SUVmean were calculated from liver and mediastinal blood pool, in addition to SUVmax from residual lymphomas and measures of noise. RESULTS: SUVmax in liver and in mediastinal blood pool decreased significantly with increasing acquisition time, whereas SUVmean remained stable. In residual tumor, SUVmax was stable during different acquisition times. As a result, the DS was subject to change in three patients. CONCLUSIONS: Attention should be drawn towards the eventual impact of improvements in image quality on visual scoring systems such as the DS.

Innate immune function during antineoplastic treatment is associated with 12-months survival in non-small cell lung cancer.

Introduction: The immune system has proven to be a key player in the progression as well as containment of cancer with new treatment strategies based on immunotherapy targeting this interaction. Assessing immune function could reveal critical information about the immune response to therapeutic interventions, revealing predictive biomarkers for tailored care and precision medicine. Methods: We investigated immune function in 37 patients with inoperable non-small cell lung cancer (NSCLC) undergoing treatment with PD-L1 immune checkpoint inhibitor (ICI), chemotherapy (CT) or chemo-radiotherapy (CT/RT). Blood samples before (day 0) and during therapy (day 7, 21 and 80) were investigated by a standardized immunoassay, TruCulture®. Results: Outcomes revealed a developing innate immune response induced by both immunotherapy and chemotherapy. NSCLC-patients displayed evidence of chronic innate immune activation and exhaustion prior to treatment. This pattern was particularly pronounced during treatment in patients dying within 12-months follow-up. Compared to treatment with CT, ICI demonstrated a higher ex vivo-stimulated release of proinflammatory cytokines. Discussion: These preliminary findings may pave the way for tailored treatment and immune-monitoring.

A deep learning-based whole-body solution for PET/MRI attenuation correction.

BACKGROUND: Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS: Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS: Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS: Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.

Correction: Christensen et al. Impact of [18F]FDG-PET and [18F]FLT-PET-Parameters in Patients with Suspected Relapse of Irradiated Lung Cancer. Diagnostics 2021, 11, 279.

In the original publication [...].

A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.

A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms 'deep learning', 'artificial intelligence', 'medical imaging', 'COVID-19' and 'SARS-CoV-2'. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789-1.00, 0.843-1.00 and 0.850-1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.

Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer.

AIM: The concept of personalized medicine has brought increased awareness to the importance of inter- and intra-tumor heterogeneity for cancer treatment. The aim of this study was to explore simultaneous multi-parametric PET/MRI prior to chemoradiotherapy for cervical cancer for characterization of tumors and tumor heterogeneity. METHODS: Ten patients with histologically proven primary cervical cancer were examined with multi-parametric 68Ga-NODAGA-E[c(RGDyK)]2-PET/MRI for radiation treatment planning after diagnostic 18F-FDG-PET/CT. Standardized uptake values (SUV) of RGD and FDG, diffusion weighted MRI and the derived apparent diffusion coefficient (ADC), and pharmacokinetic maps obtained from dynamic contrast-enhanced MRI with the Tofts model (iAUC60, Ktrans, ve, and kep) were included in the analysis. The spatial relation between functional imaging parameters in tumors was examined by a correlation analysis and joint histograms at the voxel level. The ability of multi-parametric imaging to identify tumor tissue classes was explored using an unsupervised 3D Gaussian mixture model-based cluster analysis. RESULTS: Functional MRI and PET of cervical cancers appeared heterogeneous both between patients and spatially within the tumors, and the relations between parameters varied strongly within the patient cohort. The strongest spatial correlation was observed between FDG uptake and ADC (median r = - 0.7). There was moderate voxel-wise correlation between RGD and FDG uptake, and weak correlations between all other modalities. Distinct relations between the ADC and RGD uptake as well as the ADC and FDG uptake were apparent in joint histograms. A cluster analysis using the combination of ADC, FDG and RGD uptake suggested tissue classes which could potentially relate to tumor sub-volumes. CONCLUSION: A multi-parametric PET/MRI examination of patients with cervical cancer integrated with treatment planning and including estimation of angiogenesis and glucose metabolism as well as MRI diffusion and perfusion parameters is feasible. A combined analysis of functional imaging parameters indicates a potential of multi-parametric PET/MRI to contribute to a better characterization of tumor heterogeneity than the modalities alone. However, the study is based on small patient numbers and further studies are needed prior to the future design of individually adapted treatment approaches based on multi-parametric functional imaging.

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients.

BACKGROUND: Radiotherapy (RT) planning for cervical cancer patients entails the acquisition of both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Further, molecular imaging by Positron Emission Tomography (PET) could contribute to target volume delineation as well as treatment response monitoring. The objective of this study was to investigate the feasibility of a PET/MRI-only RT planning workflow of patients with cervical cancer. This includes attenuation correction (AC) of MRI hardware and dedicated positioning equipment as well as evaluating MRI-derived synthetic CT (sCT) of the pelvic region for positioning verification and dose calculation to enable a PET/MRI-only setup. MATERIAL AND METHODS: 16 patients underwent PET/MRI using a dedicated RT setup after the routine CT (or PET/CT), including eight pilot patients and eight cervical cancer patients who were subsequently referred for RT. Data from 18 patients with gynecological cancer were added for training a deep convolutional neural network to generate sCT from Dixon MRI. The mean absolute difference between the dose distributions calculated on sCT and a reference CT was measured in the RT target volume and organs at risk. PET AC by sCT and a reference CT were compared in the tumor volume. RESULTS: All patients completed the examination. sCT was inferred for each patient in less than 5 s. The dosimetric analysis of the sCT-based dose planning showed a mean absolute error (MAE) of 0.17 ± 0.12 Gy inside the planning target volumes (PTV). PET images reconstructed with sCT and CT had no significant difference in quantification for all patients. CONCLUSIONS: These results suggest that multiparametric PET/MRI can be successfully integrated as a one-stop-shop in the RT workflow of patients with cervical cancer.

Impact of [18F]FDG-PET and [18F]FLT-PET-Parameters in Patients with Suspected Relapse of Irradiated Lung Cancer.

Radiation-induced changes may cause a non-malignant high 2-deoxy-2-[18F]fluoro-d-glucose (FDG)-uptake. The 3'-deoxy-3'-[18F]fluorothymidine (FLT)-PET/CT performs better in the differential diagnosis of inflammatory changes and lung lesions with a higher specificity than FDG-PET/CT. We investigated the association between post-radiotherapy FDG-PET-parameters, FLT-PET-parameters, and outcome. Sixty-one patients suspected for having a relapse after definitive radiotherapy for lung cancer were included. All the patients had FDG-PET/CT and FLT-PET/CT. FDG-PET- and FLT-PET-parameters were collected from within the irradiated high-dose volume (HDV) and from recurrent pulmonary lesions. For associations between PET-parameters and relapse status, respectively, the overall survival was analyzed. Thirty patients had a relapse, of these, 16 patients had a relapse within the HDV. FDG-SUVmax and FLT-SUVmax were higher in relapsed HDVs compared with non-relapsed HDVs (median FDG-SUVmax: 12.8 vs. 4.2; p < 0.001; median FLT-SUVmax 3.9 vs. 2.2; p < 0.001). A relapse within HDV had higher FDG-SUVpeak (median FDG-SUVpeak: 7.1 vs. 3.5; p = 0.014) and was larger (median metabolic tumor volume (MTV50%): 2.5 vs. 0.7; 0.014) than the relapsed lesions outside of HDV. The proliferative tumor volume (PTV50%) was prognostic for the overall survival (hazard ratio: 1.07 pr cm3 [1.01-1.13]; p = 0.014) in the univariate analysis, but not in the multivariate analysis. FDG-SUVmax and FLT-SUVmax may be helpful tools for differentiating the relapse from radiation-induced changes, however, they should not be used definitively for relapse detection.

Prognostic Value of 18F-FDG-PET Parameters in Patients with Small Cell Lung Cancer: A Meta-Analysis and Review of Current Literature.

Many studies have suggested a prognostic value of one or several positron emission tomography (PET) parameters in patients with small cell lung cancer (SCLC). However, studies are often small, and there is a considerable interstudy disagreement about which PET parameters have a prognostic value. The objective of this study was to perform a review and meta-analysis to identify the most promising PET parameter for prognostication. PubMed®, Cochrane, and Embase® were searched for papers addressing the prognostic value of any PET parameter at any treatment phase with any endpoint in patients with SCLC. Pooled hazard ratios (HRs) were calculated by a random effects model for the prognostic value of the baseline maximum standardized uptake value (SUVmax) and metabolic tumor volume (MTV). The qualitative analysis included 38 studies, of these, 19 studies were included in the meta-analyses. The pooled results showed that high baseline MTV was prognostic for overall survival (OS) (HR: 2.83 (95% confidence interval [CI]: 2.00-4.01) and progression-free survival (PFS) (HR: 3.11 (95% CI: 1.99-4.90)). The prognostic value of SUVmax was less pronounced (OS: HR: 1.50 (95% CI: 1.17-1.91); PFS: HR: 1.24 (95% CI: 0.94-1.63)). Baseline MTV is a strong prognosticator for OS and PFS in patients with SCLC. MTV has a prognostic value superior to those of other PET parameters, but whether MTV is superior to other prognosticators of tumor burden needs further investigation.

Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT.

Lung cancer is the leading cause of cancer related death around the world although early diagnosis remains vital to enabling access to curative treatment options. This article briefly describes the current role of imaging, in particular 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET/CT, in lung cancer and specifically the role of artificial intelligence with CT followed by a detailed review of the published studies applying artificial intelligence (ie, machine learning and deep learning), on FDG PET or combined PET/CT images with the purpose of early detection and diagnosis of pulmonary nodules, and characterization of lung tumors and mediastinal lymph nodes. A comprehensive search was performed on Pubmed, Embase, and clinical trial databases. The studies were analyzed with a modified version of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction model Risk Of Bias Assessment Tool (PROBAST) statement. The search resulted in 361 studies; of these 29 were included; all retrospective; none were clinical trials. Twenty-two records evaluated standard machine learning (ML) methods on imaging features (ie, support vector machine), and 7 studies evaluated new ML methods (ie, deep learning) applied directly on PET or PET/CT images. The studies mainly reported positive results regarding the use of ML methods for diagnosing pulmonary nodules, characterizing lung tumors and mediastinal lymph nodes. However, 22 of the 29 studies were lacking a relevant comparator and/or lacking independent testing of the model. Application of ML methods with feature and image input from PET/CT for diagnosing and characterizing lung cancer is a relatively young area of research with great promise. Nevertheless, current published studies are often under-powered and lacking a clinically relevant comparator and/or independent testing.

FDG PET/CT for Detection of Infectious Complications Following Solid Organ Transplantation.

Infectious complications after solid organ transplantation (SOT) are often more severe and remain a diagnostic challenge due to vague and atypical clinical presentations. Diagnostic performance of conventional diagnostic tools is frequently inadequate which may lead to delayed diagnosis with the risk of poorer outcomes. This literature review aimed to investigate the current evidence on the use of 18F-fluoro-deoxy-glucose (FDG) Positron Emission Tomography (PET)/computer tomography (CT) in infectious complications after SOT. Based on search in PubMed, Medline, and Cochrane databases, 13 articles and 46 case reports were included. For inclusion, articles were to include data on patients with infectious complications after SOT, and where FDG PET/CT was part of the work-up. Final searches were conducted on 02 September 2020. Overall, in the absence of initial diagnostic clues, FDG PET/CT should be considered as the imaging technique of choice as it may guide further investigations and eventually reveal the diagnosis in most of the patients. However, the available literature of the role of FDG PET/CT in SOT recipients with infectious complications is scarce and well-designed prospective studies including control groups are warranted to establish the role of FDG PET/C/ in SOT recipients. The main drawback of FDG PET/CT is the lack of ability to differentiate between cancer and infectious diseases which are both highly prevalent in this patient group. Accordingly, the main reasons for "false" results of FDG PET/CT is the misdiagnosis of cancer in benign inflammatory or infectious processes, information which nonetheless can be useful.

18F-FLT PET/CT Adds Value to 18F-FDG PET/CT for Diagnosing Relapse After Definitive Radiotherapy in Patients with Lung Cancer: Results of a Prospective Clinical Trial.

Diagnosing relapse after radiotherapy for lung cancer is challenging. The specificity of both CT and 18F-FDG PET/CT is low because of radiation-induced changes. 3'-deoxy-3'-18F-fluorothymidine (18F-FLT) PET has previously demonstrated higher specificity for malignancy than 18F-FDG PET. We investigated the value of 18F-FLT PET/CT for diagnosing relapse in irradiated lung cancer. Methods: Patients suspected of relapse of lung cancer after definitive radiotherapy (conventional fractionated radiotherapy [cRT] or stereotactic body radiotherapy [SBRT]) were included. Sensitivity and specificity were analyzed both within the irradiated high-dose volume (HDV) and on a patient basis. Marginal differences and interobserver agreement were assessed. Results: Sixty-three patients who had received radiotherapy in 70 HDVs (34 cRT; 36 SBRT) were included. The specificity of 18F-FLT PET/CT was higher than that of 18F-FDG PET/CT (HDV, 96% [95% CI, 87-100] vs. 71% [95% CI, 57-83] [P = 0.0039]; patient-based, 90% [95% CI, 73-98] vs. 55% [95% CI, 36-74] [P = 0.0020]). The difference in specificity between 18F-FLT PET/CT and 18F-FDG PET/CT was higher after cRT than after SBRT. The sensitivity of 18F-FLT PET/CT was lower than that of 18F-FDG PET/CT (HDV, 69% [95% CI, 41-89] vs. 94% [95% CI, 70-100] [P = 0.1250]; patient-based, 70% [95% CI, 51-84] vs. 94% [95% CI, 80-99] [P = 0.0078]). Adding 18F-FLT PET/CT when 18F-FDG PET/CT was positive or inconclusive improved the diagnostic value compared with 18F-FDG PET/CT alone. In cRT HDVs, the probability of malignancy increased from 67% for 18F-FDG PET/CT alone to 100% when both tracers were positive. Conclusion:18F-FLT PET/CT adds diagnostic value to 18F-FDG PET/CT in patients with suspected relapse. The diagnostic impact of 18F-FLT PET/CT was highest after cRT. We suggest adding 18F-FLT PET/CT when 18F-FDG PET/CT is inconclusive or positive within the previously irradiated volume to improve diagnostic value in patients for whom histologic confirmation is not easily obtained.

18F-fluorothymidine (FLT)-PET and diffusion-weighted MRI for early response evaluation in patients with small cell lung cancer: a pilot study.

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive cancer often presenting in an advanced stage and prognosis is poor. Early response evaluation may have impact on the treatment strategy. AIM: We evaluated 18F-fluorothymidine-(FLT)-PET/diffusion-weighted-(DW)-MRI early after treatment start to describe biological changes during therapy, the potential of early response evaluation, and the added value of FLT-PET/DW-MRI. METHODS: Patients with SCLC referred for standard chemotherapy were eligible. FLT-PET/DW-MRI of the chest and brain was acquired within 14 days after treatment start. FLT-PET/DW-MRI was compared with pretreatment FDG-PET/CT. Standardized uptake value (SUV), apparent diffusion coefficient (ADC), and functional tumor volumes were measured. FDG-SUVpeak, FLT-SUVpeak, and ADCmedian; spatial distribution of aggressive areas; and voxel-by-voxel analyses were evaluated to compare the biological information derived from the three functional imaging modalities. FDG-SUVpeak, FLT-SUVpeak, and ADCmedian were also analyzed for ability to predict final treatment response. RESULTS: Twelve patients with SCLC completed FLT-PET/MRI 1-9 days after treatment start. In nine patients, pretreatment FDG-PET/CT was available for comparison. A total of 16 T-sites and 12 N-sites were identified. No brain metastases were detected. FDG-SUVpeak was 2.0-22.7 in T-sites and 5.5-17.3 in N-sites. FLT-SUVpeak was 0.6-11.5 in T-sites and 1.2-2.4 in N-sites. ADCmedian was 0.76-1.74 × 10- 3 mm2/s in T-sites and 0.88-2.09 × 10-3 mm2/s in N-sites. FLT-SUVpeak correlated with FDG-SUVpeak, and voxel-by-voxel correlation was positive, though the hottest regions were dissimilarly distributed in FLT-PET compared to FDG-PET. FLT-SUVpeak was not correlated with ADCmedian, and voxel-by-voxel analyses and spatial distribution of aggressive areas varied with no systematic relation. LT-SUVpeak was significantly lower in responding lesions than non-responding lesions (mean FLT-SUVpeak in T-sites: 1.5 vs. 5.7; p = 0.007, mean FLT-SUVpeak in N-sites: 1.6 vs. 2.2; p = 0.013). CONCLUSIONS: FLT-PET and DW-MRI performed early after treatment start may add biological information in patients with SCLC. Proliferation early after treatment start measured by FLT-PET is a promising predictor for final treatment response that warrants further investigation. TRIAL REGISTRATION: Clinicaltrials.gov, NCT02995902. Registered 11 December 2014 - Retrospectively registered.

Surveillance With PET/CT and Liquid Biopsies of Stage I-III Lung Cancer Patients After Completion of Definitive Therapy: A Randomized Controlled Trial (SUPER).

Despite increased focus on prevention as well as improved treatment possibilities, lung cancer remains among the most frequent and deadliest cancer diagnoses worldwide. Even lung cancer patients treated with curative intent have a high risk of relapse, leading to a dismal prognosis. More knowledge on the efficacy of surveillance with both current and new technologies as well as on the impact on patient treatment, quality of life, and survival are urgently needed. We therefore designed a randomized phase 3 trial. In one arm, every other computed tomography (CT) scan is replaced by positron emission tomography/CT, the other arm is the standard follow-up scheme with CT. The standard arm is identical to the current national Danish follow-up program. The primary endpoint is to compare the number of relapses treatable with curative intent in the 2 arms. We aim to include 750 patients over a 3-year period. Additionally, we will test the feasibility of noninvasive lung cancer diagnostics and surveillance in the form of circulating tumor DNA analysis. For this purpose, blood samples are collected before treatment and at each following control. The blood samples are stored in a biobank for later analysis and will not be used for guiding patient treatment decisions.