68Ga-RM2 PET-MRI versus MRI alone for evaluation of patients with biochemical recurrence of prostate cancer: a single-centre, single-arm, phase 2/3 imaging trial.

BACKGROUND: National Comprehensive Cancer Network guidelines include prostate-specific membrane antigen (PSMA)-targeted PET for detection of biochemical recurrence of prostate cancer. However, targeting a single tumour characteristic might not be sufficient to reflect the full extent of disease. Gastrin releasing peptide receptors (GRPR) have been shown to be overexpressed in prostate cancer. In this study, we aimed to evaluate the diagnostic performance of the GRPR-targeting radiopharmaceutical 68Ga-RM2 in patients with biochemical recurrence of prostate cancer. METHODS: This single-centre, single-arm, phase 2/3 trial was done at Stanford University (USA). Adult patients (aged ≥18 years) with biochemical recurrence of prostate cancer, a Karnofsky performance status of 50 or higher, increasing prostate-specific antigen concentration 0·2 ng/mL or more after prostatectomy or 2 ng/mL or more above nadir after radiotherapy, and non-contributory conventional imaging (negative CT or MRI, and bone scan) were eligible. All participants underwent 68Ga-RM2 PET-MRI. The primary outcome was the proportion of patients with PET-positive findings on 68Ga-RM2 PET-MRI compared with MRI alone after initial therapy, at a per-patient and per-lesion level. The primary outcome would be considered met if at least 30% of patients had one or more lesions detected by 68Ga-RM2 PET-MRI and the detection by 68Ga-RM2 PET-MRI was significantly greater than for MRI. Each PET scan was interpreted by three independent masked readers using a standardised evaluation criteria. This study is registered with ClinicalTrials.gov, NCT02624518, and is complete. FINDINGS: Between Dec 12, 2015, and July 27, 2021, 209 men were screened for eligibility, of whom 100 were included in analyses. Median follow-up was 49·3 months (IQR 36·7-59·2). The primary endpoint was met; 68Ga-RM2 PET-MRI was positive in 69 (69%) patients and MRI alone was positive in 40 (40%) patients (p<0·0001). In the per-lesion analysis 68Ga-RM2 PET-MRI showed significantly higher detection rates than MRI alone (143 vs 96 lesions; p<0·0001). No grade 1 or worse events were reported. INTERPRETATION: 68Ga-RM2 PET-MRI showed better diagnostic performance than MRI alone in patients with biochemical recurrence of prostate cancer. Further prospective comparative studies with PSMA-targeted PET are needed to gain a better understanding of GRPR and PSMA expression patterns in these patients. FUNDING: The US Department of Defense.

Same-day post-therapy imaging with a new generation whole-body digital SPECT/CT in assessing treatment response to [177Lu]Lu-PSMA-617 in metastatic castration-resistant prostate cancer.

PURPOSE: Lutetium-177 [177Lu]Lu-PSMA-617 radioligand therapy (RLT) represents a significant advancement for metastatic castration-resistant prostate cancer (mCRPC), demonstrating improvements in radiographic progression free survival (rPFS) and overall survival (OS) with a low rate of associated side effects. Currently, most post-therapy SPECT/CT is conducted at 24 h after infusion. This study examines the clinical utility of a next-generation multi-detector Cadmium-Zinc-Telluride (CZT) SPECT/CT system (StarGuide) in same-day post-infusion assessment and early treatment response to [177Lu]Lu-PSMA-617. METHODS: In this retrospective study, 68 men with progressive mCRPC treated with [177Lu]Lu-PSMA-617 at our center from June 2022 to June 2023 were evaluated. Digital whole-body SPECT/CT imaging was performed after [177Lu]Lu-PSMA-617infusion (mean ± SD: 1.8 ± 0.6 h, range 1.1-4.9 h). Quantitative analysis of [177Lu]Lu-PSMA-617 positive lesions was performed in patients who underwent at least 2 post-therapy SPECT/CT, using liver parenchyma uptake as reference. Metrics including [177Lu]Lu-PSMA-617 positive total tumor volume (Lu-TTV), SUVmax and SUVmean were calculated. These quantitative metrics on post-infusion SPECT/CT images after cycles 1, 2 and 3 were correlated with overall survival (OS), prostate specific antigen-progression free survival (PSA-PFS) as defined by prostate cancer working group 3 (PCWG3), and PSA decrease over 50% (PSA50) response rates. RESULTS: 56 patients (means age 76.2 ± 8.1 years, range: 60-93) who underwent at least 2 post-therapy SPECT/CT were included in the image analysis. The whole-body SPECT/CT scans (~ 12 min per scan) were well tolerated, with 221 same-day scans performed (89%). At a median of 10-months follow-up, 33 (58.9%) patients achieved PSA50 after [177Lu]Lu-PSMA-617 treatment and median PSA-PFS was 5.0 months (range: 1.0-15 months) while median OS was not reached. Quantitative analysis of SPECT/CT images showed that 37 patients (66%) had > 30% reduction in Lu-TTV, associated with significantly improved overall survival (median not reached vs. 6 months, P = 0.008) and PSA-PFS (median 6 months vs. 1 months, P < 0.001). However, changes in SUVmax or SUVmean did not correlate with PSA-PFS or OS. CONCLUSION: We successfully implemented same-day post-therapy SPECT/CT after [177Lu]Lu-PSMA-617 infusions. Quantitation of 1-2 h post-therapy SPECT/CT images is a promising method for assessing treatment response. However, the approach is currently limited by its suboptimal detection of small tumor lesions and the necessity of incorporating a third-cycle SPECT/CT to mitigate the effects of any potential treatment-related flare-up. Further investigation in a larger patient cohort and prospective validation is essential to confirm these findings and to explore the role of SPECT/CT as a potential adjunct to PSMA PET/CT in managing mCRPC.

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

BACKGROUND: 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost. PURPOSE: To generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI. STUDY TYPE: Retrospective. SUBJECTS: Patients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent 18 F-FDG PET and MRI for determining recurrent brain tumor. FIELD STRENGTH/SEQUENCE: 3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, 18 F-FDG PET imaging. ASSESSMENT: Convolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland-Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5-point scale, with score ≥3 as high-quality. They assessed the lesions on a 5-point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET. STATISTICAL TESTS: The agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance. RESULTS: The synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, -31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high-quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall. CONCLUSION: The proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation.

INTRODUCTION: Amyloid positron emission tomography (PET) acquisition timing impacts quantification. METHODS: In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. RESULTS: CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. DISCUSSION: The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. HIGHLIGHTS: Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.

Modified PROMISE criteria for standardized interpretation of gastrin-releasing peptide receptor (GRPR)-targeted PET.

PURPOSE: There are image interpretation criteria to standardize reporting prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET). As up to 10% of prostate cancer (PC) do not express PSMA, other targets such as gastrin-releasing peptide receptor (GRPR) are evaluated. Research on GRPR-targeted imaging has been slowly increasing in usage at staging and biochemical recurrence (BCR) of PC. We therefore propose a modification of the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria (mPROMISE) for GRPR-targeted PET. METHODS: [68 Ga]Ga-RM2 PET data from initially prospective studies performed at our institution were retrospectively reviewed: 44 patients were imaged for staging and 100 patients for BCR PC. Two nuclear medicine physicians independently evaluated PET according to the mPROMISE criteria. A third expert reader served as standard reference. Interreader reliability was computed for GRPR expression, prostate bed (T), lymph node (N), skeleton (Mb), organ (Mc) metastases, and final judgment of the scan. RESULTS: The interrater reliability for GRPR PET at staging was moderate for GRPR expression (0.59; 95% confidence interval [CI] 0.40, 0.78), substantial for T-stage (0.78; 95% CI 0.63, 0.94), and almost perfect for N-stage (0.97; 95% CI 0.92, 1.00) and final judgment (0.92; 95% CI 0.82, 1.00). The interreader agreement at BCR showed substantial agreement for GRPR expression (0.70; 95% CI 0.59, 0.81) and final judgment (0.65; 95% CI 0.53, 0.78), while almost perfect agreement was seen across the major categories (T, N, Mb, Mc). Acceptable performance of the mPROMISE criteria was found for all subsets when compared to the standard reference. CONCLUSION: Interpreting GRPR-targeted PET using the mPROMISE criteria showed its reliability with substantial or almost perfect interrater agreement across all major categories. The proposed modification of the PROMISE criteria will aid clinicians in decreasing the level of uncertainty, and clinical trials to achieve uniform evaluation, reporting, and comparability of GRPR-targeted PET. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT03113617 and NCT02624518.

SPECT at the speed of PET: a feasibility study of CZT-based whole-body SPECT/CT in the post 177Lu-DOTATATE and 177Lu-PSMA617 setting.

PURPOSE: To evaluate the feasibility of using the StarGuide (General Electric Healthcare, Haifa, Israel), a new generation multi-detector cadmium-zinc-telluride (CZT)-based SPECT/CT, for whole-body imaging in the setting of post-therapy imaging of 177Lu-labeled radiopharmaceuticals. METHODS: Thirty-one patients (34-89 years old; mean ± SD, 65.5 ± 12.1) who were treated with either 177Lu-DOTATATE (n=17) or 177Lu-PSMA617 (n=14) as part of standard of care were scanned post-therapy with the StarGuide; some were also scanned with the standard GE Discovery 670 Pro SPECT/CT. All patients had either 64Cu-DOTATATE or 18F-DCFPyL PET/CT prior to first cycle of therapy for eligibility check. The detection/targeting rate (lesion uptake greater than blood pool uptake) of large lesions meeting RECIST 1.1 size criteria on post-therapy StarGuide SPECT/CT was evaluated and compared to the standard design GE Discovery 670 Pro SPECT/CT (when available) and pre-therapy PET by two nuclear medicine physicians with consensus read. RESULTS: This retrospective analysis identified a total of 50 post-therapy scans performed with the new imaging protocol from November 2021 to August 2022. The StarGuide system acquired vertex to mid-thighs post-therapy SPECT/CT scans with 4 bed positions, 3 min/bed and a total scan time of 12 min. In comparison, the standard GE Discovery 670 Pro SPECT/CT system typically acquires images in 2 bed positions covering the chest, abdomen, and pelvis with a total scan time of 32 min. The pre-therapy 64Cu-DOTATATE PET takes 20 min with 4 bed positions on GE Discovery MI PET/CT, and 18F-DCFPyL PET takes 8-10 min with 4-5 bed positions on GE Discovery MI PET/CT. This preliminary evaluation showed that the post-therapy scans acquired with faster scanning time using StarGuide system had comparable detection/targeting rate compared to the Discovery 670 Pro SPECT/CT system and detected large lesions defined by RECIST criteria on the pre-therapy PET scans. CONCLUSION: Fast acquisition of whole-body post-therapy SPECT/CT is feasible with the new StarGuide system. Short scanning time improves the patients' clinical experience and compliance which may lead to increased adoption of post-therapy SPECT. This opens the possibility to offer imaged-based treatment response assessment and personalized dosimetry to patients referred for targeted radionuclide therapies.

Evaluation of Liver and Renal Toxicity in Peptide Receptor Radionuclide Therapy for Somatostatin Receptor Expressing Tumors: A 2-Year Follow-Up.

BACKGROUND: Peptide receptor radionuclide therapy (PRRT) with radiolabeled somatostatin receptor (SSR) analogs is now an established systemic treatment for neuroendocrine tumors (NET). However, more short- and long-term data about renal and hepatotoxicity is needed. Here we present our experience in this clinical scenario. METHODS: Eighty-six patients with progressive SSR-expressing malignancies underwent PRRT with Lu-177 Dotatate and were followed up for up to 2 years. Laboratory tests were done 1 week before each cycle and every 2 months at follow-up. Hepatic and renal toxicity was determined based on NCI CTCAE V5.0. RESULTS: 55/86 (64%) patients completed all 4 cycles of PRRT; 18/86 (20.9%) are currently being treated; 13/86 (15.1%) had to discontinue PRRT: 4/13 (31%) due to hematologic toxicity, 9/13 (69%) due to non-PRRT-related comorbidities. Out of the patients who finished treatment, only transient grade 2 toxicities were observed during PRRT: hypoalbuminemia in 5.5% (3/55), and renal toxicity (serum creatinine and estimated glomerular filtration rate) in 1.8% (1/55). No grade 3 or 4 liver and renal toxicity occurred. Patients presenting with impaired liver or renal function prior to PRRT, either improved or had stable findings. No deterioration was observed. CONCLUSION: Peptide receptor radionuclide therapy does not have a negative impact on liver and renal function, even in patients with pre-existing impaired parameters. No grade 3 or 4 hepatic or renal toxicity was identified. Only transient grade 2 hypoalbuminemia in 5.5% and nephrotoxicity in 1.8% of patients were seen during PRRT.

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.

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 18F-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.

Tau PET imaging with 18F-PI-2620 in aging and neurodegenerative diseases.

PURPOSE: In vivo measurement of the spatial distribution of neurofibrillary tangle pathology is critical for early diagnosis and disease monitoring of Alzheimer's disease (AD). METHODS: Forty-nine participants were scanned with 18F-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+ (Aß+) 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 min post-injection, with the exception of very high binders who continued to show increases over time. Robust elevations in 18F-PI-2620 were observed between HC and Aß+ 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 18F-PI-2620 in patients along the AD trajectory. This work confirms that 18F-PI-2620 holds promise as a tool to visualize tau aggregations in AD.

Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography.

PURPOSE: 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). METHODS: 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. RESULTS: 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 < 0.001). For the case-based approaches using both 2D-CNN and 3D-CNN architectures, a training dataset of 100 images and a test dataset of 28 images were randomly allocated. The sensitivity, specificity, and AUC to discriminate abnormal images by the 2D-CNN and 3D-CNN case-based approaches were 85.7%, 71.4%, and 0.750 (p = 0.013) and 71.4%, 71.4%, and 0.699 (p = 0.053), respectively. CONCLUSION: DL accurately classifies abnormal 18F-fluciclovine PET images of the pelvis in patients with BCR of PC. A DL classifier using single slice prediction had superior performance over case-based prediction.

Prognostic value of volumetric PET parameters at early response evaluation in melanoma patients treated with immunotherapy.

PURPOSE: 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). METHODS: 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. RESULTS: 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 < 0.0001). The median OS in patients with high MTVpost (≥ 23.44) was 16 months (95% CI 12-32 months) as compared with more than 60 months in patients with low MTVpost (< 23.44) (P = 0.0003). A multivariate model including PET/CT parameters and known clinical prognostic factors revealed that MTVpost and the presence of central nervous system lesions were independent prognostic factors for OS (P = 0.0004, 0.0167, respectively). One pseudoprogression case (1.2%) was seen in this population and classified into the high MTVpost group. CONCLUSION: Whole-body metabolic tumor volume from PET scan acquired approximately 3 months following initiation of immunotherapy (MTVpost) is a strong prognostic indicator of OS in melanoma patients. Although the possibility of pseudoprogression must be considered whenever evaluating first restaging PET imaging, it only occurred in 1 patient in our cohort.

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

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: Eighty simultaneous [18F]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. Twenty minutes of list-mode PET data (90-110 min 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.

Human biodistribution and radiation dosimetry of [18F]DASA-23, a PET probe targeting pyruvate kinase M2.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. TRIAL REGISTRATION: ClinicalTrials.gov , NCT03539731 (registered 28 May 2018).

Performance Comparison of Individual and Ensemble CNN Models for the Classification of Brain 18F-FDG-PET Scans.

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.

Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer.

Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.

Prognostic value of somatostatin receptor expressing tumor volume calculated from 68Ga-DOTATATE PET/CT in patients with well-differentiated neuroendocrine tumors.

PURPOSE: 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). METHODS: 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. RESULTS: Univariate analyses revealed significant difference of PFS for WHO tumor grade and ΣSRETV (P < 0.05), while there were no significant differences in age, sex, SUVmax, and ΣTLSRE (P > 0.05). Multivariate analysis identified WHO tumor grade and ΣSRETV as independent predictors of PFS. CONCLUSION: ΣSRETV calculated from 68Ga-DOTATATE PET/CT may have prognostic value of PFS in WD-NET patients.

Bridging the Health Data Divide.

Fundamental quality, safety, and cost problems have not been resolved by the increasing digitization of health care. This digitization has progressed alongside the presence of a persistent divide between clinicians, the domain experts, and the technical experts, such as data scientists. The disconnect between clinicians and data scientists translates into a waste of research and health care resources, slow uptake of innovations, and poorer outcomes than are desirable and achievable. The divide can be narrowed by creating a culture of collaboration between these two disciplines, exemplified by events such as datathons. However, in order to more fully and meaningfully bridge the divide, the infrastructure of medical education, publication, and funding processes must evolve to support and enhance a learning health care system.

Case 207: Hodgkin lymphoma with paraneoplastic hypercalcemic pancreatitis.

A 15-year-old girl presented with a 2-month history of 30-lb (13.6 kg) weight loss, chest and abdominal pain, nausea, bilious emesis, cough, and shortness of breath. Initial blood count (performed at an outside hospital) showed elevated white blood cell and platelet counts but low hemoglobin and hematocrit levels. On examination, she had adenopathy in the left axillary and supraclavicular regions, fullness in the left chest, and abdominal guarding. Ultrasonography (US)-guided fine-needle aspiration biopsy of the left anterior chest wall mass was nondiagnostic, and lumbar puncture and bone marrow biopsies were negative. At that time, the patient underwent several imaging studies-including chest radiography; bone scanning; contrast material-enhanced computed tomography (CT) of the chest, abdomen, and pelvis; and fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT-all performed within 1 week of each other. Pertinent serum laboratory values at the time of these tests were as follows: calcium level, 17 mg/dL (4.25 mmol/L) (normal range, 8.5-10.5 mg/dL [2.1-2.6 mmol/L]); ionized calcium level, 2.3 mmol/L (normal range, 1.1-1.3 mmol/L); lipase level, 2423 U/L (normal level, <300 U/L); amylase level, 1435 U/L (normal level, <140 U/L); lactate dehydrogenase level, 253 U/L (normal level, <240 U/L), albumin level, 2.6 g/dL (26 g/L) (normal level, 3.5-5.0 g/dL [35-50 g/L]), and creatinine level, 1.7 mg/dL (150.3 µmol/L) (normal level, <1.2 mg/dL [<106.1 µmol/L]). A follow-up PET/CT scan was performed approximately 2 months later after initial therapy.

Nuclear Medicine and Molecular Imaging Applications in Gynecologic Malignancies: A Comprehensive Review.

Gynecologic malignancies, consisting of endometrial, cervical, ovarian, vulvar, and vaginal cancers, pose significant diagnostic and management challenges due to their complex anatomic location and potential for rapid progression. These tumors cause substantial morbidity and mortality, often because of their delayed diagnosis and treatment. An estimated 19% of newly diagnosed cancers among women are gynecologic in origin. In recent years, there has been growing evidence supporting the integration of nuclear medicine imaging modalities in the diagnostic work-up and management of gynecologic cancers. The sensitivity of fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) combined with the anatomical specificity of computed tomography (CT) and magnetic resonance imaging (MRI) allows for the hybrid evaluation of metabolic activity and structural abnormalities that has become an indispensable tool in oncologic imaging. Lymphoscintigraphy, using technetium 99m (99mTc) based radiotracers along with single photon emission computed tomography/ computed tomography (SPECT/CT), holds a vital role in the identification of sentinel lymph nodes to minimize the surgical morbidity from extensive lymph node dissections. While not yet standard for gynecologic malignancies, promising therapeutic nuclear medicine agents serve as specialized treatment options for patients with advanced or recurrent disease. This article aims to provide a comprehensive review on the nuclear medicine applications in gynecologic malignancies through the following objectives: 1) To describe the role of nuclear medicine in the initial staging, lymph node mapping, response assessment, and recurrence/surveillance imaging of common gynecologic cancers, 2) To review the limitations of 18F-FDG PET/CT and promising applications of 18F-FDG PET/MRI in gynecologic malignancy, 3) To underscore the promising theragnostic applications of nuclear medicine, 4) To highlight the current role of nuclear medicine imaging in gynecologic cancers as per the National Comprehensive Cancer Network (NCCN), European Society of Surgical Oncology (ESGO), and European Society of Medical Oncology (ESMO) guidelines.