The Diagnostic Value of 18F-FDG PET/CT Scan in Characterizing Adrenal Tumors

CONTEXT: Imaging plays an important role in the characterization of adrenal tumors, but findings might be inconclusive. The clinical question is whether 18F fluodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is of diagnostic value in this setting.OBJECTIVE: This meta-analysis was aimed at the diagnostic value of 18F-FDG PET/CT in differentiating benign from malignant adrenal tumors discovered either as adrenal incidentaloma or during staging or follow-up of oncologic patients.DATA SOURCES: PubMed, EMBASE, Web of Science, and Cochrane Library were searched to select articles between 2000 and 2021.STUDY SELECTION: We included studies describing the diagnostic value of 18F-FDG PET/CT in adult patients with an adrenal tumor. Exclusion criteria were 10 or fewer participants, insufficient data on histopathology, clinical follow-up, or PET results. After screening of title and abstract by 2 independent reviewers, 79 studies were retrieved, of which 17 studies met the selection criteria.DATA EXTRACTION: Data extraction using a protocol and quality assessment according to QUADAS-2 was performed independently by at least 2 authors.DATA SYNTHESIS: A bivariate random-effects model was applied using R (version 3.6.2.). Pooled sensitivity and specificity of 18F-FDG PET/CT for identifying malignant adrenal tumors was 87.3% (95% CI, 82.5%-90.9%) and 84.7% (95% CI, 79.3%-88.9%), respectively. The pooled diagnostic odds ratio was 9.20 (95% CI, 5.27-16.08; P &lt; .01). Major sources of heterogeneity (I2, 57.1% [95% CI, 27.5%-74.6%]) were in population characteristics, reference standard, and interpretation criteria of imaging results.CONCLUSIONS: 18F-FDG PET/CT had good diagnostic accuracy for characterization of adrenal tumors. The literature, however, is limited, in particular regarding adrenal incidentalomas. Large prospective studies in well-defined patient populations with application of validated cutoff values are needed.</p

Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma

PURPOSE: Metabolically active tumour volume (MATV) is a potential quantitative positron emission tomography (PET) imaging biomarker in melanoma. Accumulating data indicate that low MATV may predict increased chance of response to immunotherapy and overall survival. However, metastatic melanoma can present with numerous (small) tumour lesions, making manual tumour segmentation time-consuming. The aim of this study was to evaluate multiple semi-automatic segmentation workflows to determine reliability and reproducibility of MATV measurements in patients with metastatic melanoma. METHODS: An existing cohort of 64 adult patients with histologically proven metastatic melanoma was used in this study. 18F-FDG PET/CT diagnostic baseline images were acquired using a European Association of Nuclear Medicine (EANM) Research Limited-accredited Siemens Biograph mCT PET/CT system (Siemens Healthineers, Knoxville, USA). PET data were analysed using manual, gradient-based segmentation and five different semi-automatic methods: three direct PET image-derived delineations (41MAX, A50P and SUV40) and two based on a majority-vote approach (MV2 and MV3), without and with (suffix '+') manual lesion addition. Correlation between the different segmentation methods and their respective associations with overall survival was assessed. RESULTS: Correlation between the MATVs derived by the manual segmentation and semi-automated tumour segmentations ranged from R2 = 0.41 for A50P to R2 = 0.85 for SUV40+ and MV2+, respectively. Manual MATV segmentation did not differ significantly from the semi-automatic methods SUV40 (∆MATV mean ± SD 0.08 ± 0.60 mL, P = 0.303), SUV40+ (∆MATV - 0.10 ± 0.51 mL, P = 0.126), MV2+ (∆MATV - 0.09 ± 0.62 mL, P = 0.252) and MV3+ (∆MATV - 0.03 ± 0.55 mL, P = 0.615). Log-rank tests showed statistically significant overall survival differences between above and below median MATV patients for all segmentation methods with areas under the ROC curves of 0.806 for manual segmentation and between 0.756 [41MAX] and 0.807 [MV3+] for semi-automatic segmentations. CONCLUSIONS: Simple and fast semi-automated FDG PET segmentation workflows yield accurate and reproducible MATV measurements that correlate well with manual segmentation in metastatic melanoma. The most readily applicable and user-friendly SUV40 method allows feasible MATV measurement in prospective multicentre studies required for validation of this potential PET imaging biomarker for clinical use

Use of population input functions for reduced scan duration whole-body Patlak F-18-FDG PET imaging

Abstract: Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated. Methods: Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K 1, k 3, and both K 1 and k 2) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (K i) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in K i was evaluated. Results: Percentage bias in K i observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these K i biases in most cases (287 out of 290) to < 5%. Conclusions: Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible

F-18-FDG PET/CT Scans Can Identify Sub-Groups of NSCLC Patients with High Glucose Uptake in the Majority of Their Tumor Lesions

Background: Reprogrammed glucose metabolism is a hallmark of cancer making it an attractive therapeutic target, especially in cancers with high glucose uptake such as non-small cell lung cancer (NSCLC). Tools to select patients with high glucose uptake in the majority of tumor lesions are essential in the development of anti-cancer drugs targeting glucose metabolism. Type 2 diabetes mellitus (T2DM) patients may have tumors highly dependent on glucose uptake. Surprisingly, this has not been systematically studied. Therefore, we aimed to determine which patient and tumor characteristics, including concurrent T2DM, are related to high glucose uptake in the majority of tumor lesions in NSCLC patients as measured by 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-18-FDG) positron emission tomography (PET)/computed tomography (CT) scans. Methods: Routine primary diagnostic F-18-FDG PET/CT scans of consecutive NSCLC patients were included. Mean standardized uptake value (SUVmean) of F-18-FDG was determined for all evaluable tumor lesions and corrected for serum glucose levels according to the European Association of Nuclear Medicine Research Ltd guidelines. Patient characteristics potentially determining degree of tumor lesion glucose uptake in the majority of tumor lesions per patient were investigated. Results: The cohort consisted of 102 patients, 28 with T2DM and 74 without T2DM. The median SUVmean per patient ranged from 0.8 to 35.2 (median 4.2). T2DM patients had higher median glucose uptake in individual tumor lesions and per patient compared to non-diabetic NSCLC patients (SUVmean 4.3 vs 2.8, P = 1 mL per patient (odds ratio 0.8, 95% confidence interval 0.7-0.9). Conclusions: F-18-FDG PET/CT scans can identify sub-groups of NSCLC patients with high glucose uptake in the majority of their tumor lesions. T2DM patients had higher tumor lesion glucose uptake than non-diabetic patients. However, this was not independent of other factors such as the histological subtype and number of tumor lesions per patient

Convolutional neural networks for automatic image quality control and EARL compliance of PET images

Background: Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and methods: 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using fivefold cross-validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results: In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion: The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by, e.g., adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential

Non-adherence to consensus guidelines on preoperative imaging in surgery for primary hyperparathyroidism

Objective: The aim of this study was to determine the adherence to consensus guidelines on preoperative imaging of patients with primary hyperparathyroidism (pHPT) in real local practice. Methods: This was a retrospective multicenter cohort study of 411 patients undergoing parathyroidectomy for pHPT from 2007 to 2017 in three referral centers. Results: In 286/411 patients (69%) the preoperative imaging workup adhered to guidelines (utilizing ultrasound and parathyroid scintigraphy). In patients in whom guidelines were followed 63% were discharged within one day versus 37% in whom guidelines were not followed (P< .0005). The use of a bimodality imaging workup, starting with ultrasound and parathyroid scintigraphy followed by imaging upscaling aiming for anatomical and functional concordance, was a predictor for the performance of a minimally invasive parathyroidectomy (OR 4.098, 95% CI 2.296-7.315,P< .0005). Conclusion: The level of compliance to preoperative imaging guidelines is suboptimal in this population. Patients in whom adherence was achieved showed a shorter length of stay. More education of physicians is required regarding the appropriate preoperative imaging workup in pHPT

Molecular Imaging in Cancer Drug Development

Developing new oncology drugs has increased since the improved understanding of cancer's complex biology. The oncology field has become the top therapeutic research area for new drugs. However, only a limited number of drugs entering clinical trials will be approved for use as standard of care for cancer patients. Molecular imaging is increasingly perceived as a tool to support go/no-go decisions early during drug development. It encompasses a wide range of techniques including radiolabeling a compound of interest followed by visualization with single photon emission computed tomography or positron emission tomography. Radiolabeling can be performed using a variety of radionuclides that are preferably matched to the compound based on size and half-life. Imaging can provide information on drug behavior in vivo, whole body drug target visualization, and heterogeneity in drug target expression. This review focuses on current applications of molecular imaging in the development of small molecules, antibodies, and anti-hormonal anticancer drugs

Comparison of [18F]DOPA and [68Ga]DOTA-TOC as a PET imaging tracer before peptide receptor radionuclide therapy

BACKGROUND: In treatment of neuroendocrine neoplasms (NENs), confirmation of somatostatin receptor expression with 68Ga-DOTA somatostatin analogues is mandatory to determine eligibility for peptide receptor radionuclide therapy (PRRT). [18F]DOPA can detect additional lesions compared to [68Ga]DOTA-TOC. The aim of this study was to explore differences in tumour detection of both tracers and their relevance for selecting patients for PRRT. We retrospectively studied eight patients with NENs who underwent both [68Ga]DOTA-TOC and carbidopa-enhanced [18F]DOPA PET/CT, before first-time PRRT with [177Lu]DOTA-TATE. Tracer order was influenced due to stock availability or to detect suspected metastases with a second tracer. On CT, disease control was defined as a lesion showing complete response, partial response, or stable disease, according to RECIST 1.1. CRITERIA: RESULTS: Seven patients with in total 89 lesions completed four infusions of 7.4 GBq [177Lu]DOTA-TATE, one patient received only two cycles. Before treatment, [18F]DOPA PET/CT detected significantly more lesions than [68Ga]DOTA-TOC PET/CT (79 vs. 62, p < .001). After treatment, no difference in number of lesions with disease control was found for [18F]DOPA-only (5/27) and [68Ga]DOTA-TOC-only lesions (4/10, p = .25). [18F]DOPA detected more liver metastases (24/27) compared to [68Ga]DOTA-TOC (7/10, p = .006). Six patients showed inpatient heterogeneity in treatment response between [18F]DOPA-only and [68Ga]DOTA-TOC-only lesions. CONCLUSIONS: Response to PRRT with [177Lu]DOTA-TATE was comparable for both [68Ga]DOTA-TOC- and [18F]DOPA-only NEN lesions. [18F]DOPA may be capable of predicting response to PRRT while finding more lesions compared to [68Ga]DOTA-TOC, although these additional lesions are often small of size and undetected by diagnostic CT

Other Radiopharmaceuticals for Imaging GEP‐NET

In GEP‐NETs, especially the catecholamine and serotonin biosynthetic pathways are upregulated. Therefore, increased biosynthesis of these specific amines in GEP‐NETs enables imaging with specific amine precursors. For the catecholamine pathway, 6‐18F ‐l‐3,4‐dihydroxyphenylalanine (18F‐DOPA) is available, while for the serotonin pathway, carbon‐11‐labeled 5‐hydroxy‐l‐tryptophan ([11C]‐5‐HTP) is available as tracer. 11C‐5‐HTP PET and 18F‐DOPA PET are excellent functional imaging techniques for evaluating patients with proven pancreatic islet cell tumors and carcinoids. For both tracers, the combination with CT further improves the detection rate of NET, which shows that performing PET scans with these tracers in PET/CT scanners is beneficial for patients.Since well‐differentiated GEP‐NETs generally have a low glucose metabolism, 18F‐fluorodexyglucose (18F‐FDG) PET scanning has limited value for the primary staging of patients with well‐differentiated GEP‐NETs. However, in patients with rapidly progressive disease, dedifferentiation of GEP‐NET tumors can lead to a higher glucose metabolism in tumor cells. In these patients, 18F‐FDG PET can be of benefit for tumor staging. Also, 18F‐FDG PET can be of value when other malignancies are suspected in patients with GEP‐NETs, since these patients experience a higher incidence of these malignancies compared to the general population.Nowadays, (GEP)‐NETs can also be imaged with 68Ga‐labeled analogues of somatostatin, which are also PET tracers. Advantages of 68Ga‐labeled somatostatin analogues are the relatively easy generator‐based synthesis and the possibility to evaluate whether peptide (somatostatin) receptor radionuclide therapy (PRRT) for NETs can be considered

PET segmentation of bulky tumors:Strategies and workflows to improve inter-observer variability

Background PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction. Methods Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. Results The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value&lt;0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. Conclusions FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.</p