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Clinical Characteristics of patients imaged with [18F]DASA-23

Imaging of IC-2

Purpose:Pyruvate kinase M2 (PKM2) catalyzes the final step in glycolysis, a key process of cancer metabolism. PKM2 is preferentially expressed by glioblastoma (GBM) cells with minimal expression in healthy brain. We describe the development, validation, and translation of a novel PET tracer to study PKM2 in GBM. We evaluated 1-((2-fluoro-6-[18F]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([18F]DASA-23) in cell culture, mouse models of GBM, healthy human volunteers, and patients with GBM.Experimental Design:[18F]DASA-23 was synthesized with a molar activity of 100.47 ± 29.58 GBq/μmol and radiochemical purity >95%. We performed initial testing of [18F]DASA-23 in GBM cell culture and human GBM xenografts implanted orthotopically into mice. Next, we produced [18F]DASA-23 under FDA oversight, and evaluated it in healthy volunteers and a pilot cohort of patients with glioma.Results:In mouse imaging studies, [18F]DASA-23 clearly delineated the U87 GBM from surrounding healthy brain tissue and had a tumor-to-brain ratio of 3.6 ± 0.5. In human volunteers, [18F]DASA-23 crossed the intact blood–brain barrier and was rapidly cleared. In patients with GBM, [18F]DASA-23 successfully outlined tumors visible on contrast-enhanced MRI. The uptake of [18F]DASA-23 was markedly elevated in GBMs compared with normal brain, and it identified a metabolic nonresponder within 1 week of treatment initiation.Conclusions:We developed and translated [18F]DASA-23 as a new tracer that demonstrated the visualization of aberrantly expressed PKM2 for the first time in human subjects. These results warrant further clinical evaluation of [18F]DASA-23 to assess its utility for imaging therapy–induced normalization of aberrant cancer metabolism.

PDF file - 1.4MB, Supplement 1: External data set cohort characteristics Supplement 2: Study cohort metagenes significantly associated with FDG uptake features Supplement 3: DAVID and GSEA enrichment analysis for metagenes in study cohort Supplement 4: Predicted FDG uptake features gene enrichment analysis Supplement 5: Kaplan Meier curves for single genes associated with FDG uptake features and survival Supplement 6: Survival analysis incorporating imaging features with known variables of prognosis in non-small cell lung cancer Supplement 7: IPA networks for SUVmax and the compound model Supplement 8: SUVmax associations with selected glycolytic genes by SAM!

Purpose: Pyruvate kinase M2 (PKM2) catalyzes the final step in glycolysis, a key process of cancer metabolism. PKM2 is preferentially expressed by glioblastoma (GBM) cells with minimal expression in healthy brain. We describe the development, validation, and translation of a novel PET tracer to study PKM2 in GBM. We evaluated 1-((2-fluoro-6-[18F]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([18F]DASA-23) in cell culture, mouse models of GBM, healthy human volunteers, and patients with GBM. Experimental Design: [18F]DASA-23 was synthesized with a molar activity of 100.47 ± 29.58 GBq/μmol and radiochemical purity >95%. We performed initial testing of [18F]DASA-23 in GBM cell culture and human GBM xenografts implanted orthotopically into mice. Next, we produced [18F]DASA-23 under FDA oversight, and evaluated it in healthy volunteers and a pilot cohort of patients with glioma. Results: In mouse imaging studies, [18F]DASA-23 clearly delineated the U87 GBM from surrounding healthy brain tissue and had a tumor-to-brain ratio of 3.6 ± 0.5. In human volunteers, [18F]DASA-23 crossed the intact blood–brain barrier and was rapidly cleared. In patients with GBM, [18F]DASA-23 successfully outlined tumors visible on contrast-enhanced MRI. The uptake of [18F]DASA-23 was markedly elevated in GBMs compared with normal brain, and it identified a metabolic nonresponder within 1 week of treatment initiation. Conclusions: We developed and translated [18F]DASA-23 as a new tracer that demonstrated the visualization of aberrantly expressed PKM2 for the first time in human subjects. These results warrant further clinical evaluation of [18F]DASA-23 to assess its utility for imaging therapy–induced normalization of aberrant cancer metabolism.

This paper summarizes the 2020 Diversity in Radiology and Molecular Imaging: What We Need to Know Conference, a three-day virtual conference held September 9–11, 2020. The World Molecular Imaging Society (WMIS) and Stanford University jointly organized this event to provide a forum for WMIS members and affiliates worldwide to openly discuss issues pertaining to diversity in science, technology, engineering, and mathematics (STEM). The participants discussed three main conference themes, “racial diversity in STEM,” “women in STEM,” and “global health,” which were discussed through seven plenary lectures, twelve scientific presentations, and nine roundtable discussions, respectively. Breakout sessions were designed to flip the classroom and seek input from attendees on important topics such as increasing the representation of underrepresented minority (URM) members and women in STEM, generating pipeline programs in the fields of molecular imaging, supporting existing URM and women members in their career pursuits, developing mechanisms to effectively address microaggressions, providing leadership opportunities for URM and women STEM members, improving global health research, and developing strategies to advance culturally competent healthcare. Supplementary Information The online version contains supplementary material available at 10.1007/s11307-021-01610-3.

Rationale: Novel radiopharmaceuticals for positron emission tomography (PET) are evaluated for the diagnosis of biochemically recurrent prostate cancer (BCR PC). Here, we compare the gastrin releasing peptide receptors (GRPR) - targeting 68Ga-RM2 with the prostate specific membrane antigen (PSMA) – targeting 68Ga-PSMA11 and 18F-DCFPyL. Methods: Fifty patients had both 68Ga-RM2 PET/MRI and 68Ga-PSMA11 PET/CT (n = 23) or 18F-DCFPyL PET/CT (n = 27) at an interval ranging from 1 to 60 days (mean±SD: 15.8±17.7). Maximum standardized uptake values (SUVmax) were collected for all lesions. Results: RM2 PET was positive in 35 and negative in 15 of the 50 patients. PSMA PET was positive in 37 and negative in 13 of the 50 patients. Both scans detected 70 lesions in 32 patients. Forty-three lesions in 18 patients were identified only on one scan: 68Ga-RM2 detected 7 more lesions in 4 patients, while PSMA detected 36 more lesions in 13 patients. Conclusion:68Ga-RM2 remains a valuable radiopharmaceutical even when compared with the more widely used 68Ga-PSMA11/18F-DCFPyL in the evaluation of BCR PC. Larger studies are needed to verify that identifying patients for whom these two classes of radiopharmaceuticals are complementary may ultimately allow for personalized medicine.

Purpose: To investigate the prognostic value of 18F-FDG PET/CT parameters in melanoma patients before beginning anti-PD-1 therapy. Methods: Imaging parameters including SUVmax, metabolic tumor volume (MTV), and bone marrow to liver SUVmean ratio (BLR) were measured from baseline PET/CT in 92 patients before the start of anti-PD-1 therapy. Association with survival and imaging parameters combined with clinical factors was evaluated. Clinical and laboratory data between high (> median) and low (≤ median) BLR groups were compared. Results: Multivariate analyses demonstrated that BLR was an independent prognostic factor for PFS and OS (P = 0.017, P = 0.011, respectively). The high BLR group had higher levels of white blood cell count/neutrophil count and C-Reactive Protein than the low BLR group (P < 0.05). Conclusion: Patients with high BLR were associated with poor PFS and OS, potentially explained by evidence of systemic inflammation known to be associated with immunosuppression.

To evaluate the diagnostic performance and clinical utility of 18F-fluciclovine PET/CT in patients with biochemical recurrence (BCR) of prostate cancer (PC). 18F-Fluciclovine scans of 165 consecutive men with BCR after primary definitive treatment with prostatectomy (n = 102) or radiotherapy (n = 63) were retrospectively evaluated. Seventy patients had concurrent imaging with at least one other conventional modality (CT (n = 31), MRI (n = 31), or bone scan (n = 26)). Findings from 18F-fluciclovine PET were compared with those from conventional imaging modalities. The positivity rate and impact of 18F-fluciclovine PET on patient management were recorded. In 33 patients who underwent at least one other PET imaging (18F-NaF PET/CT (n = 12), 68Ga-PSMA11 PET/CT (n = 5), 18F-DCFPyL PET/CT (n = 20), and 68Ga-RM2 PET/MRI (n = 5)), additional findings were evaluated. The overall positivity rate of 18F-fluciclovine PET was 67 %, which, as expected, increased with higher prostate-specific antigen (PSA) levels (ng/ml): 15 % (PSA

PURPOSE: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI. METHODS: Eighteen participants underwent two separate (18)F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies. RESULTS: The deep learning–enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%). CONCLUSION: Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.

Highlights • 68Ga-PSMA11 PET/CT showed high detection rates for recurrent prostate cancer. • Standard and new generation PET/CT performed equally on a per-patient basis. • Delayed imaging revealed no additional lesions. • SiPM-based PET/CT identified more prostate cancer lesions. • PSMA positivity rate increased with higher PSA levels and higher PSA velocity., Objectives 68Ga-PSMA11 PET/CT is excellent for evaluating biochemically recurrent prostate cancer (BCR PC). Here, we compared the positivity rates of dual-time point imaging using a PET/CT scanner (DMI) with silicon photomultiplier (SiPM) detectors and a PET/CT scanner (D690) with photomultiplier tubes (PMT), in patients with BCR PC. Methods Fifty-eight patients were prospectively recruited and randomized to receive scans on DMI followed by D690 or vice-versa. Images from DMI were reconstructed using the block sequential regularized expectation maximization (BSREM) algorithm and images from D690 were reconstructed using ordered subset expectation maximization (OSEM), according to the vendor's recommendations. Two readers independently reviewed all images in randomized order, recorded the number and location of lesions, as well as standardized uptake value (SUV) measurements. Results Twenty-eight patients (group A) had DMI as first scanner followed by D690, while 30 patients (group B) underwent scans in reversed order. Mean PSA was 30±112.9 (range 0.3–600.66) ng/mL for group A and 41.5 ± 213.2 (range 0.21–1170) ng/mL for group B (P = 0.796). The positivity rate in group A was 78.6% (22/28 patients) vs. 73.3% (22/30 patients) in group B. Although the performance of the two scanners was equivalent on a per-patient basis, DMI identified 5 additional sites of suspected recurrent disease when used as first scanner. The second scan time point did not reveal additional abnormal uptake. Conclusions The delayed time point in 68Ga-PSMA11 PET/CT did not show a higher positivity rate. SiPM-based PET/CT identified additional lesions. Further studies with larger cohorts are needed to confirm these results.

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomography (PET) is currently regarded as the gold standard for the measurement of CBF in the human brain. PET imaging, however, is not widely available because of its prohibitive costs, use of ionizing radiation, and logistical challenges, which require a co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope. Magnetic resonance imaging (MRI), in contrast, is more readily available and does not involve ionizing radiation. In this study, we propose a multi-task learning framework for brain MRI-to-PET translation and disease diagnosis. The proposed framework comprises two prime networks: (1) an attention-based 3D encoder-decoder convolutional neural network (CNN) that synthesizes high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale 3D CNN that identifies the brain disease corresponding to the input MRI images. Our multi-task framework yields promising results on the task of MRI-to-PET translation, achieving an average structural similarity index (SSIM) of 0.94 and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In addition, we show that integrating multiple MRI modalities can improve the clinical diagnosis of brain diseases., Comment: 7 pages, 6 figures

We investigated the ability of baseline 2-deoxy-2-[18F]fluoro-d-glucose PET/CT parameters, acquired before the start of immunotherapy, to predict development of hyperprogressive disease (HPD) in melanoma patients. We also evaluated the diagnostic performances of ratios of baseline and first restaging PET/CT parameters to diagnose HPD without information of the tumor growth kinetic ratio (TGKR) that requires pre-baseline imaging before baseline imaging (3 timepoint imaging). Seventy-six patients who underwent PET/CT before and approximately 3 months following initiation of immunotherapy were included. PET/CT parameters, including metabolic tumor volume (MTV) for all melanoma lesions and total measured tumor burden (TMTB) based on irRECIST, were measured from baseline PET/CT (MTVbase and TMTBbase) and first restaging PET/CT (MTVpost and TMTBpost). The ratios of MTV (MTVpost/MTVbase, MTVr) and TMTB (TMTBpost/TMTBbase, TMTBr) were calculated. MTVbase of HPD patients (n = 9, TGKR ≥ 2) was larger than that of non-HPD (n = 67, TGKR

To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal 18F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR). A total of 251 consecutive 18F-fluciclovine PET scans were acquired between September 2017 and June 2019 in 233 PC patients with BCR (18 patients had 2 scans). PET images were labeled as normal or abnormal using clinical reports as the ground truth. Convolutional neural network (CNN) models were trained using two different architectures, a 2D-CNN (ResNet-50) using single slices (slice-based approach) and the same 2D-CNN and a 3D-CNN (ResNet-14) using a hundred slices per PET image (case-based approach). Models’ performances were evaluated on independent test datasets. For the 2D-CNN slice-based approach, 6800 and 536 slices were used for training and test datasets, respectively. The sensitivity and specificity of this model were 90.7% and 95.1%, and the area under the curve (AUC) of receiver operating characteristic curve was 0.971 (p

BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imaging data and specialized segmentation software. We investigated the use of deep learning to automatically quantify standardized uptake value ratio and used this for classification. MATERIALS AND METHODS: Using the Alzheimer’s Disease Neuroimaging Initiative dataset, we identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. We trained convolutional neural networks (ResNet-50 and ResNet-152) to predict standardized uptake value ratio and classify amyloid status. We assessed performance based on network depth, number of PET input slices, and use of ImageNet pretraining. We also assessed human performance with 3 readers in a subset of 100 randomly selected cases. RESULTS: We have found that 48% of cases were amyloid positive. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-square error of 0.054, corresponding to 95.1% correct amyloid status prediction. Using more than 3 slices did not improve performance, but ImageNet initialization did. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively). CONCLUSIONS: Deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans. Such methods have promise to automate this laborious calculation, enabling quantitative measurements rapidly and in settings without extensive image processing manpower and expertise.

To assess the safety, biodistribution, and radiation dosimetry of the novel positron emission tomography (PET) radiopharmaceutical 1-((2-fluoro-6-[[18F]]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([18F]DASA-23) in healthy volunteers. We recruited 5 healthy volunteers who provided a written informed consent. Volunteers were injected with 295.0 ± 8.2 MBq of [18F]DASA-23 intravenously. Immediately following injection, a dynamic scan of the brain was acquired for 15 min. This was followed by serial whole-body PET/MRI scans acquired up to 3 h post-injection. Blood samples were collected at regular intervals, and vital signs monitored pre- and post-radiotracer administration. Regions of interest were drawn around multiple organs, time-activity curves were calculated, and organ uptake and dosimetry were estimated with OLINDA/EXM (version 1.1) software. All subjects tolerated the PET/MRI examination, without adverse reactions to [18F]DASA-23. [18F]DASA-23 passively crossed the blood-brain barrier, followed by rapid clearance from the brain. High accumulation of [18F]DASA-23 was noted in organs such as the gallbladder, liver, small intestine, and urinary bladder, suggesting hepatobiliary and urinary clearance. The effective dose of [18F]DASA-23 was 23.5 ± 5.8 μSv/MBq. We successfully completed a pilot first-in-human study of [18F]DASA-23. Our results indicate that [18F]DASA-23 can be used safely in humans to evaluate pyruvate kinase M2 levels. Ongoing studies are evaluating the ability of [18F]DASA-23 to visualize intracranial malignancies, NCT03539731. ClinicalTrials.gov , NCT03539731 (registered 28 May 2018)

The high-background glucose metabolism of normal gray matter on [18F]-fluoro-2-D-deoxyglucose (FDG) positron emission tomography (PET) of the brain results in a low signal-to-background ratio, potentially increasing the possibility of missing important findings in patients with intracranial malignancies. To explore the strategy of using a deep learning classifier to aid in distinguishing normal versus abnormal findings on PET brain images, this study evaluated the performance of a two-dimensional convolutional neural network (2D-CNN) to classify FDG PET brain scans as normal (N) or abnormal (A). Methods: Two hundred eighty-nine brain FDG-PET scans (N; n = 150, A; n = 139) resulting in a total of 68,260 images were included. Nine individual 2D-CNN models with three different window settings for axial, coronal, and sagittal axes were trained and validated. The performance of these individual and ensemble models was evaluated and compared using a test dataset. Odds ratio, Akaike’s information criterion (AIC), and area under curve (AUC) on receiver-operative-characteristic curve, accuracy, and standard deviation (SD) were calculated. Results: An optimal window setting to classify normal and abnormal scans was different for each axis of the individual models. An ensembled model using different axes with an optimized window setting (window-triad) showed better performance than ensembled models using the same axis and different windows settings (axis-triad). Increase in odds ratio and decrease in SD were observed in both axis-triad and window-triad models compared with individual models, whereas improvements of AUC and AIC were seen in window-triad models. An overall model averaging the probabilities of all individual models showed the best accuracy of 82.0%. Conclusions: Data ensemble using different window settings and axes was effective to improve 2D-CNN performance parameters for the classification of brain FDG-PET scans. If prospectively validated with a larger cohort of patients, similar models could provide decision support in a clinical setting.

18F-DCFPyL (2-(3-{1-carboxy-5-[(6-18F-fluoropyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid) is a promising PET radiopharmaceutical targeting prostate-specific membrane antigen (PSMA). We present our experience with this single-academic-center prospective study evaluating the positivity rate of 18F-DCFPyL PET/CT in patients with biochemical recurrence (BCR) of prostate cancer (PC). Methods: We prospectively enrolled 72 men (52–91 y old; mean ± SD, 71.5 ± 7.2) with BCR after primary definitive treatment with prostatectomy (n = 42) or radiotherapy (n = 30). The presence of lesions compatible with PC was evaluated by 2 independent readers. Fifty-nine patients had scans concurrent with at least one other conventional scan: bone scanning (24), CT (21), MR (20), 18F-fluciclovine PET/CT (18), or 18F-NaF PET (14). Findings from 18F-DCFPyL PET/CT were compared with those from other modalities. Impact on patient management based on 18F-DCFPyL PET/CT was recorded from clinical chart review. Results:18F-DCFPyL PET/CT had an overall positivity rate of 85%, which increased with higher prostate-specific antigen (PSA) levels (ng/mL): 50% (PSA

More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p 0.05) and overall diagnostic confidence (p 0.001) independent of the underlying PET scanner used. Lesion detection for the low-count-enhanced scans had a high patient-level sensitivity of 0.94 (0.83-0.99) and specificity of 0.98 (0.95-0.99). Interscan kappa agreement of 0.85 was comparable to intrareader (0.88) and pairwise inter-reader agreements (maximum of 0.72). SUV quantification was comparable in the reference regions and lesions (lowest p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.

More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p p p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.

PURPOSE: In vivo measurement of the spatial distribution of neurofibrillary tangle pathology is critical for early diagnosis and disease monitoring in Alzheimer’s disease (AD). METHODS: Forty-nine participants were scanned with (18)F-PI-2620 PET to examine the distribution of this novel PET ligand throughout the course of AD: 36 older healthy controls (HC) (age range 61 to 86), 11 beta-amyloid+ (Aβ+) participants with cognitive impairment (CI; clinical diagnosis of either mild cognitive impairment or AD dementia, age range 57 to 86), and 2 participants with semantic variant primary progressive aphasia (svPPA, age 66 and 78). Group differences in brain regions relevant in AD (medial temporal lobe, posterior cingulate cortex, and lateral parietal cortex) were examined using standardized uptake value ratios (SUVRs) normalized to the inferior gray matter of the cerebellum. RESULTS: SUVRs in target regions were relatively stable 60 to 90 minutes post-injection, with the exception of very high binders who continued to show increases over time. Robust elevations in (18)F-PI-2620 were observed between HC and Aβ+ CI across all AD regions. Within the HC group, older age was associated with subtle elevations in target regions. Mildly elevated focal uptake was observed in the anterior temporal pole in one svPPA patient. CONCLUSION: Preliminary results suggest strong differences in the medial temporal lobe and cortical regions known to be impacted in AD using (18)F-PI-2620 in patients along the AD trajectory. This work confirms that (18)F-PI-2620 holds promise as a tool to visualize Tau aggregations in AD.

Lutetium-177 ( 177 Lu)-DOTATATE is an effective systemic therapy for metastatic somatostatin receptor positive neuroendocrine tumors (NETs). Here we report our experience with the use of pre-therapy 68 Ga-DOTATATE PET as prognostic marker for short-term clinical outcomes of 177 Lu-DOTATATE therapy in patients with advanced NETs. Materials and methods: We retrospectively reviewed patients who received at least one dose of 177 Lu-DOTATATE between Dec. 2016 and July 2019 at our institution. 50 patients (63.6 ± 10.0 years) with advanced gastroenteropancreatic neuroendocrine tumors (GEP-NETs) who had pre-therapy 68 Ga-DOTATATE PET were included in the analysis. 68 Ga-DOTATATE avid tumor volumes were determined automatically using an SUV thresholding approach. Total and extrahepatic 68 Ga-DOTATATE avid tumor volumes were measured and dichotomized into large and small tumor volume groups. Association with progression free survival (PFS) and overall survival (OS) were determined at median follow up of 32 months by Kaplan-Meier survival analysis with Log-Rank test. Results: During follow up, 38 patients (76%) had disease progression and 15 patients (30%) died. Kaplan-Meier analysis of PFS in GEP-NETs patients showed that smaller extrahepatic 68 Ga-DOTATATE avid tumor volume ( 1000 mL) tumor volumes, no statistically significant difference in PFS is observed, P = 0.19. The accuracy of extrahepatic 68 Ga-DOTATATE avid tumor volume as prognostic marker for PFS and OS at 32 months are moderate at 58% and 72%. Conclusions: Smaller extrahepatic 68 Ga-DOTATATE avid tumor volumes are associated with longer PFS and OS following 177 Lu-DOTATATE treatment in patients with advanced GEP-NETs. The accuracy of extrahepatic 68 Ga-DOTATATE avid tumor volume as prognostic marker for PFS and OS at 32 months are moderate, which may limit its clinical application.

Fungal endocarditis is a rare subtype of infective endocarditis that often presents with nonspecific symptoms in patients with complex medical histories, making diagnosis challenging. Patients with a history of ALL may present with congestive heart failure, chemo-induced cardiomyopathy, acute coronary syndrome, cardiac lymphomatous metastasis, or infections. We present the case of a patient with a history of ALL who presented with acute coronary syndrome and imaging concerning for primary cardiac lymphoma, when in fact the patient ended up suffering from culture proven fungal endocarditis. Keywords: Fungal endocarditis, Primary cardiac lymphoma

PET using radiolabeled prostate-specific membrane antigen (PSMA) is now being more widely adopted as a valuable tool to evaluate patients with prostate cancer (PC). Recently, 3 different criteria for interpretation of PSMA PET were published: the European Association of Nuclear Medicine (EANM) criteria, the Prostate Cancer Molecular Imaging Standardized Evaluation criteria, and the PSMA Reporting and Data System. We compared these 3 criteria in terms of interreader, intrareader, and intercriteria agreement. Methods: Data from 104 patients prospectively enrolled in research protocols at our institution were retrospectively reviewed. The cohort consisted of 2 groups: 47 patients (mean age, 64.2 y old) who underwent Glu-NH-CO-NH-Lys-(Ahx)-[68Ga(HBED-CC)] (68Ga-PSMA11) PET/MRI for initial staging of biopsy-proven intermediate- or high-risk PC, and 57 patients (mean age, 70.5 y old) who underwent 68Ga-PSMA11 PET/CT because of biochemically recurrent PC. Three nuclear medicine physicians independently evaluated all 68Ga-PSMA11 PET/MRI and PET/CT studies according to the 3 interpretation criteria. Two of them reevaluated all studies 6 mo later in the same manner and masked to the initial reading. The Gwet agreement coefficient was calculated to evaluate interreader, intrareader, and intercriteria agreement based on the following sites: local lesion (primary tumor or prostate bed after radical prostatectomy), lymph node metastases, and other metastases. Results: In the PET/MRI group, interreader, intrareader, and intercriteria agreement ranged from substantial to almost perfect for any site according to all 3 criteria. In the PET/CT group, interreader agreement ranged from substantial to almost perfect except for judgment of distant metastases based on the PSMA Reporting and Data System (Gwet agreement coefficient, 0.57; moderate agreement), in which the most frequent cause of disagreement was lung nodules. Intrareader agreement ranged from substantial to almost perfect for any site according to all 3 criteria. Intercriteria agreement for each site was also substantial to almost perfect. Conclusion: Although the 3 published criteria have good interreader and intrareader reproducibility in evaluating 68Ga-PSMA11 PET, there are some factors causing interreader disagreement. Further work is needed to address this issue.

To evaluate the prognostic value of volumetric parameters calculated from 68Ga-1,4,7,10-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid (DOTA)-Thr3-octreotate (68Ga-DOTATATE) positron emission tomography/computed tomography (PET/CT) in patients with well-differentiated neuroendocrine tumor (WD-NET). Ninety-two patients (44 men and 48 women, mean age of 59.5-year-old) with pathologically confirmed WD-NET (grades 1 or 2) were enrolled in a prospective expanded access protocol. Selected data was analyzed retrospectively for this project. Maximum standardized uptake value (SUVmax) in the lesion with the highest 68Ga-DOTATATE uptake was measured and recorded for each patient. In addition, two volumetric parameters, namely, somatostatin receptor expressing tumor volume (SRETV) and total lesion somatostatin receptor expression (TLSRE), were calculated in each 68Ga-DOTATATE-avid lesion. SRETV was defined as tumor volume with higher 68Ga-DOTATATE uptake than the 50% of SUVmax within the volume of interest (VOI) for each lesion. TLSRE was calculated by multiplying SRETV and mean SUV within the same VOI. Thereafter, the sum of SRETV (ΣSRETV) and TLSRE (ΣTLSRE) for all detected lesions per patient were calculated. Progression-free survival (PFS) was set as primary endpoint. Kaplan-Meier survival analysis, log-rank test, and Cox’s proportional hazard model were used for statistical analysis. Univariate analyses revealed significant difference of PFS for WHO tumor grade and ΣSRETV (P 0.05). Multivariate analysis identified WHO tumor grade and ΣSRETV as independent predictors of PFS. ΣSRETV calculated from 68Ga-DOTATATE PET/CT may have prognostic value of PFS in WD-NET patients.

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation., Computational decision support systems could provide clinical value in whole-body FDG PET/CT workflows, but labeled data is scarce and PET/CT imaging exams are cumbersome. Here, the authors describe a weak supervision framework that extracts regional abnormality labels from free-text radiology reports.

PURPOSE: Pyruvate kinase M2 (PKM2) catalyzes the final step in glycolysis, a key process of cancer metabolism. PKM2 is preferentially expressed by glioblastoma (GBM) cells with minimal expression in healthy brain. We describe the development, validation, and translation of a novel positron emission tomography (PET) tracer to study PKM2 in GBM. We evaluated 1-((2-fluoro-6-[(18)F]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([(18)F]DASA-23) in cell culture, mouse models of GBM, healthy human volunteers, and GBM patients. EXPERIMENTAL DESIGN: [(18)F]DASA-23 was synthesized with a molar activity of 100.47 ± 29.58 GBq/μmol and radiochemical purity >95%. We performed initial testing of [(18)F]DASA-23 in GBM cell culture and human GBM xenografts implanted orthotopically into mice. Next we produced [(18)F]DASA-23 under FDA oversight, and evaluated it in healthy volunteers, and a pilot cohort of glioma patients. RESULTS: In mouse imaging studies, [(18)F]DASA-23 clearly delineated the U87 GBM from surrounding healthy brain tissue and had a tumor-to-brain ratio (TBR) of 3.6 ± 0.5. In human volunteers, [(18)F]DASA-23 crossed the intact blood-brain barrier and was rapidly cleared. In GBM patients, [(18)F]DASA-23 successfully outlined tumors visible on contrast-enhanced magnetic resonance imaging (MRI). The uptake of [(18)F]DASA-23 was markedly elevated in GBMs compared to normal brain, and it identified a metabolic non-responder within 1-week of treatment initiation. CONCLUSION: We developed and translated [(18)F]DASA-23 as a new tracer that demonstrated the visualization of aberrantly expressed PKM2 for the first time in human subjects. These results warrant further clinical evaluation of [(18)F]DASA-23 to assess its utility for imaging therapy-induced normalization of aberrant cancer metabolism.

Artificial intelligence (AI) has recently attracted much attention for its potential use in healthcare applications. The use of AI to improve and extract more information out of medical images, given their parallels with natural images and the immense progress in the area of computer vision, has been at the forefront of these advances. This is due to a convergence of factors, including the increasing numbers of scans performed, the availability of open source AI tools, and decreases in the costs of hardware required to implement these technologies. In this article, we review the progress in the use of AI toward optimizing PET/CT and PET/MRI studies. These two methods, which combine molecular information with structural and (in the case of MRI) functional imaging, are extremely valuable for a wide range of clinical indications. They are also tremendously data-rich modalities and as such are highly amenable to data-driven technologies such as AI. The first half of the article will focus on methods to improve PET reconstruction and image quality, which has multiple benefits including faster image acquisition, image reconstruction, and lower or even "zero" radiation dose imaging. It will also address the value of AI-driven methods to perform MR-based attenuation correction. The second half will address how some of these advances can be used to perform to optimize diagnosis from the acquired images, with examples given for whole-body oncology, cardiology, and neurology indications. Overall, it is likely that the use of AI will markedly improve both the quality and safety of PET/CT and PET/MRI as well as enhance our ability to interpret the scans and follow lesions over time. This will hopefully lead to expanded clinical use cases for these valuable technologies leading to better patient care.

To evaluate the diagnostic performance and clinical utility ofThe overall positivity rate of

Our purpose was to investigate the prognostic value of

ObjectiveTo determine whether memory tasks with demonstrated sensitivity to hippocampal function can detect variance related to preclinical Alzheimer disease (AD) biomarkers, we examined associations between performance in 3 memory tasks and CSF β-amyloid (Aβ)42/Aβ40 and phosopho-tau181 (p-tau181) in cognitively unimpaired older adults (CU).MethodsCU enrolled in the Stanford Aging and Memory Study (n = 153; age 68.78 ± 5.81 years; 94 female) completed a lumbar puncture and memory assessments. CSF Aβ42, Aβ40, and p-tau181 were measured with the automated Lumipulse G system in a single-batch analysis. Episodic memory was assayed using a standardized delayed recall composite, paired associate (word–picture) cued recall, and a mnemonic discrimination task that involves discrimination between studied “target” objects, novel “foil” objects, and perceptually similar “lure” objects. Analyses examined cross-sectional relationships among memory performance, age, and CSF measures, controlling for sex and education.ResultsAge and lower Aβ42/Aβ40 were independently associated with elevated p-tau181. Age, Aβ42/Aβ40, and p-tau181 were each associated with (1) poorer associative memory and (2) diminished improvement in mnemonic discrimination performance across levels of decreased task difficulty (i.e., target–lure similarity). P-tau mediated the effect of Aβ42/Aβ40 on memory. Relationships between CSF proteins and delayed recall were similar but nonsignificant. CSF Aβ42 was not significantly associated with p-tau181 or memory.ConclusionsTests designed to tax hippocampal function are sensitive to subtle individual differences in memory among CU and correlate with early AD-associated biomarker changes in CSF. These tests may offer utility for identifying CU with preclinical AD pathology.

We investigated the ability of baseline 2-deoxy-2-[Seventy-six patients who underwent PET/CT before and approximately 3 months following initiation of immunotherapy were included. PET/CT parameters, including metabolic tumor volume (MTV) for all melanoma lesions and total measured tumor burden (TMTB) based on irRECIST, were measured from baseline PET/CT (MTVMTVPatients at high risk of developing HPD could not be accurately identified based on baseline PET/CT parameters. The ratios of baseline and first restaging PET/CT parameters may be helpful to diagnose HPD, when patients do not undergo pre-baseline imaging.

Introduction:68Ga-RM2 is a synthetic bombesin receptor antagonist targeting gastrin-releasing peptide receptors (GRPr) that are overexpressed in several human tumors, including prostate cancer (PC)...

PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols. METHODS: 80 simultaneous [(18)F]florbetaben PET/MRI studies were acquired, split equally between two sites (Site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67±8 years, 23 females; Site 2: mMR, Siemens Healthineers, 64±11 years, 23 females) with different MRI protocols. 20 minutes of list-mode PET data (90–110 minutes post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (Site 1) or the first minute of PET acquisition (Site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with Site 1 data and either (A) directly applied or (B) trained further on Site 2 data using transfer learning. Networks were also trained from scratch based on (C) Site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/−) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful Site 2 FreeSurfer segmentations were analyzed. RESULTS: All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B-D scored similarly or better than the ground-truth images. CONCLUSIONS: Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization.

The purpose of this study was to investigate the prognostic value of whole-body metabolic tumor volume (MTV) and other metabolic tumor parameters, obtained from baseline and first restaging 18F-FDG PET/CT scans in melanoma patients treated with immune checkpoint inhibitors (ICIs). Eighty-five consecutive melanoma patients (M, 57; F, 28) treated with ICIs who underwent PET/CT scans before and approximately 3 months after the start of immunotherapy were retrospectively enrolled. Metabolic tumor parameters including MTV for all melanoma lesions were measured on each scan. A Cox proportional hazards model was used for univariate and multivariate analyses of metabolic parameters combined with known clinical prognostic factors associated with overall survival (OS). Kaplan–Meier curves for patients dichotomized based on median values of imaging parameters were generated. The median OS time in all patients was 45 months (95% CI 24–45 months). Univariate analysis demonstrated that MTV obtained from first restaging PET/CT scans (MTVpost) was the strongest prognostic factor for OS among PET/CT parameters (P

PURPOSE A PET/computed tomography (CT) that uses silicon photomultiplier (SiPM) technology was installed at our institution. Here, we report the initial use of the new scanner and evaluate the image quality in comparison to standard PET/CT scanners. PROCEDURES Seventy-two patients were scanned first using standard PET/CT followed immediately by the new PET/CT system. Images from the new PET/CT system were reconstructed using a conventional [non time-of-flight (TOF)] algorithm, TOF alone and TOF in combination with BSREM. Images from standard PET/CT were reconstructed using clinical standard-of-care settings. Three blinded readers randomly reviewed four datasets (standard, non-TOF, TOF alone, TOF+BSREM) per patient for image quality using a five-point Likert scale. SUV measurements for the single most avid lesion on each dataset were also recorded. RESULTS Datasets from the new scanner had higher image quality (P