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The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging, we constructed a scale-free binary graph for each subject, using internodal time series correlation of regions of interest as a proximity measure. The resulting network could be embedded onto manifolds of various curvatures and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using the 𝕊1/ℍ2 model, we reduced the dimension of the network into two-dimensional hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the hyperbolic discs. Each individual disc revealed relevance with its anatomic counterpart and absence of center-spaced node. Using the hyperbolic distance on the 𝕊1/ℍ2 model, we could detect the anomaly of network in autism spectrum disorder subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.

[177Lu]Ludotadipep, which enables targeted delivery of beta-particle radiation to prostate tumor cells, had been suggested as a promising therapeutic option for mCRPC. From November 2020 to March 2022, a total of 30 patients were enrolled for single dose of [177Lu]Ludotadipep RPT, 6 subjects in each of the 5 different activity groups of 1.9 GBq, 2.8 GBq, 3.7 GBq, 4.6 GBq, and 5.6 GBq. [177Lu]Ludotadipep was administered via venous injection, and patients were hospitalized for three days to monitor for any adverse effects. Serum PSA levels were followed up at weeks 1, 2, 3, 4, 6, 8, and 12, and PSMA PET/CT with [18F]Florastamin was obtained at baseline and again at weeks 4 and 8. The subjects required positive PSMA PET/CT prior to [177Lu]Ludotadipep administration. Among the 29 subjects who received [177Lu]Ludotadipep, 36 treatment emergent adverse events (TEAEs) occurred in 17 subjects (58.6%) and 4 adverse drug reactions (ADRs) in 3 subjects (10.3%). Of the total 24 subjects who had full 12-week follow-up data, 16 (66.7%) showed decrease in PSA of any magnitude, and 9 (37.5%) showed a decrease in PSA by 50% or greater. A total of 5 of the 24 patients (20.8%) showed disease progression (PSA increase of 25% or higher from the baseline) at the 12th week following single dose of [177Lu]Ludotadipep. These data thus far suggest that [177Lu]Ludotadipep could be a promising RPT agent with low toxicity in mCRPC patients who have not been responsive to conventional treatments.

The clinical value of 16α-18F-fluoro-17 β-estradiol (18F-FES) PET in breast cancer has been widely investigated because it can visualize estrogen receptor-expressing lesions. This relatively new radiotracer adds clinical values by characterization of metastasis in double primary cancer. It also has advantage in finding small brain lesions, which has no background brain activity. Here, we present 18F-FES PET findings of brain and leptomeningeal metastases in a patient with breast and lung malignancy.

Background: Co-occurrence of β-amyloid (Aβ) pathology has been reported in Parkinson’s disease (PD), and Aβ deposition in the brain may contribute to cognitive decline in patients with PD. Whether striatal dopamine uptake and cognitive status differ with amyloid deposition has been reported in only a few studies. Objective: The purpose of this study was to investigate the association among striatal dopaminergic availability, Aβ-positivity, and motor and cognitive status in early and non-demented PD. Methods: A total of 98 newly-diagnosed, non-medicated, and non-demented patients with PD were included in this study. Cognitive status was assessed using neuropsychological testing. Patients with mild cognitive impairment (MCI) were stratified into two groups: amnestic MCI (aMCI) and non-amnestic MCI (naMCI). Patient motor status was examined using the Unified Parkinson’s Disease Rating Scale (UPDRS) and positron emission tomography (PET) with 18F-N-(3-fluoropropyl)-2beta-carbon ethoxy-3beta-(4-iodophenyl) nortropane (18F-FP-CIT). All patients also underwent 18F-florbetaben (18F-FBB) PET and were divided based on the results into Aβ-positive and Aβ-negative groups. Results: Eighteen patients had Aβ-positivity in 18F-FBB PET and 67 had MCI. Sixteen of 18 with Aβ-positive patients had MCI. The Aβ-positive group had higher frequency of MCI, especially amnestic-type, and lower dopaminergic activities in the left ventral striatum, but not with UPDRS motor score. Conclusion: Amyloid pathology was associated with MCI, especially amnestic-subtype, in early and non-demented PD patients and with low dopaminergic activities in the left ventral striatum. This finding suggests that PD patients with Aβ-positivity have AD-related cognitive pathophysiology in PD and associated impaired dopaminergic availability in the ventral striatum can affect the pathophysiology in various ways.

PURPOSE: EGFR-mutation (EGFR-mt) is a major oncogenic driver mutation in lung adenocarcinoma (ADC) and is more often observed in Asian population. In lung ADC, some radiomics parameters of FDG PET have been reported to be associated with EGFR-mt. Here, the associations between EGFR-mt and PET parameters, particularly asphericity (ASP), were evaluated in Asian population. METHODS: Lung ADC patients who underwent curative surgical resection as the first treatment were retrospectively enrolled. EGFR mutation was defined as exon 19 deletion and exon 21 point mutation and was evaluated using surgical specimens. On FDG PET, image parameters of maximal standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and ASP were obtained. The parameters were compared between EGFR-mt and wild type (EGFR-wt) groups, and the relationships between these PET parameters and EGFR-mt were evaluated. RESULTS: A total of 64 patients (median age 66 years, M:F = 34:30) were included in the analysis, and 29 (45%) patients showed EGFR-mt. In EGFR-mt group, all the image parameters of SUVmax, MTV, TLG, and ASP were significantly lower than in EGFR-wt group (all adjusted P

INTRODUCTION Symptom laterality is one of the main characteristics of Parkinson disease (PD) and reported to be associated with motor and nonmotor symptom severity and prognosis. This study aimed to evaluate the changes of laterality after deep brain stimulation (DBS) and the association between dopamine transporter SPECT using I FP-CIT (DAT SPECT) and symptom laterality in PD before and after DBS. METHODS Nineteen patients with PD who received bilateral subthalamic nucleus DBS were enrolled. The clinical scores including Unified Parkinson Disease Rating Scale (UPDRS) and Hoehn and Yahr were evaluated at baseline, 6 months, and 1 year after DBS. Also, the patients underwent DAT SPECT before and 6 months and 1 year after DBS. Symptom and DAT laterality indices were determined based on the UPDRS part 3 and DAT SPECT, respectively. The association between DAT and symptom laterality was assessed at baseline and 6 months and 1 year after DBS. RESULTS At baseline, 11, 6, and 2 among 19 patients had left-side-dominant, right-side-dominant, and symmetric motor symptom, respectively. Among 19 patients, there were 10 patients who showed changed symptom laterality within 1 year after DBS. The agreement between symptom laterality and DAT laterality was good to excellent at baseline and 6 months and 1 year after DBS (weighted κ = 0.742, 0.736, and 0.813). Furthermore, symptom and DAT laterality indices showed significant correlation at baseline (r = 0.542, P = 0.02), 6 months (r = 0.579, P = 0.01), and 1 year after DBS (r = 0.689, P = 0.02). Symptom laterality could be determined by DAT laterality index with areas under curve of 0.833 (P = 0.045), 0.982 (P < 0.001), and 1.000 (P < 0.001) at baseline and 6 and 12 months after DBS, respectively. CONCLUSIONS The symptom laterality could be altered after DBS and was well correlated with laterality evaluated by DAT SPECT. An objective evaluation of laterality using DAT SPECT would be helpful for the management of patients with PD especially for adjusting the DBS programming for fine balancing of the asymmetric symptom after DBS. The large-scale study is warranted for validation of this result.

Attention-deficit hyperactivity disorder (ADHD) is a complex brain development disorder characterized by hyperactivity/impulsivity and inattention. A major hypothesis of ADHD is a lag of maturation, which is supported mainly by anatomical studies evaluating cortical thickness. Here, we analyzed changes of topological characteristics of whole-brain metabolic connectivity in twelve SHR rats selected as ADHD-model rats by confirming behavior abnormalities using the marble burying test, open field test, and delay discounting task and 12 Wistar Kyoto rats as the control group, across development from 4 weeks old (childhood) and 6 weeks old (entry of puberty). A topological approach based on graph filtrations revealed a lag in the strengthening of limbic-cortical/subcortical connections in ADHD-model rats. This in turn related to impaired modularization of memory and reward-motivation associated regions. Using mathematical network analysis techniques such as single linkage hierarchical clustering and volume entropy, we observed left-lateralized connectivity in the ADHD-model rats at 6 weeks old. Our findings supported the maturational delay of metabolic connectivity in the SHR model of ADHD, and also suggested the possibility of impaired and compensative reconfiguration of information flow over the brain network.

To evaluate the impact ofEmbase, PubMed, and Cochrane Library were searched up to November 2020 for all studies investigating the role of preoperative FDG PET in patients who underwent neoadjuvant radiotherapy before curative-intent surgery. The proportion of patients whose treatment plan (curative vs. palliative intent) or target definition was changed after FDG PET was analyzed. A random-effects model was used for pooled analysis. The change in target definition was compared between conventional radiological imaging-based target volume [gross tumor volume (GTV) or planning target volume (PTV)] and PET-based target volume (GTV or PTV) using the standardized mean difference (SMD) and 95% confidence interval (CI).A total of 336 patients from twelve studies were included. In eight studies, PET changed either the treatment intent or target definition in 24.8% of patients (95% CI 15.1% to 37.9%, IThe use of FDG PET influences radiotherapy planning in a fourth of patients with rectal cancer. FDG PET can provide additive information for accurate tumor delineation, although PET-based PTV did not significantly change. These findings suggest that FDG PET may be beneficial to patients with rectal cancer before establishing a radiotherapy plan.

Hyperbolic disc embedding and k-core percolation reveal the core structure of the functional connectivity on resting-state fMRI (rsfMRI). Inter-voxel relations were visualized on embedded hyperbolic discs, and their core composition was traced using k-core percolation. Using 180 normal adults’ rsfMRI data from the Human Connectome Project database, scale- free intervoxel connectivity represented by IC-voxels composition, while visualized on hyperbolic discs using model, showed the expected change of the largest component decreasing its size on k-core percolation eventually yielding the core structures of individuals. This kmax-core voxels-ICs composition revealed such stereotypes of individuals as visual network dominant, default mode network dominant, and distributed patterns. Characteristic core structures of resting-state brain connectivity of normal subjects disclosed the distributed or asymmetric contribution of voxels to the kmax-core, which suggests the hierarchical dominance of certain IC subnetworks characteristic to subgroups of individuals at rest.

Reduced uptake of 123I-meta-iodobenzylguanidine (123I-MIBG) and orthostatic hypotension (OH) are independently associated with worse clinical outcomes of Parkinson’s disease (PD). However, their interactive influence on PD has not been studied. The role of 123I-MIBG myocardial uptake, as a biomarker of PD severity, was investigated, conditional on the mediating effects of OH. A total of 227 PD patients were enrolled. Their motor and nonmotor aspects were assessed with standardized tools. Global disease burden was estimated by averaging the scaled z-scores of the assessment tools. Every patient went through 123I-MIBG scan, and OH was evaluated with the head-up tilt-test. The mediating role of orthostatic blood pressure changes (ΔBP) on the association between cardiac sympathetic denervation and disease burden was investigated. Low heart-to-mediastinum (H/M) ratio with less than 1.78 was seen in 69.6% of the patient population, and 22.9% of patients had OH. Low H/M ratio was associated with OH, and these patients had worse disease burden than subjects with normal 123I-MIBG uptake (global composite z-score: normal 123I-MIBG vs. abnormal 123I-MIBG; −0.3 ± 0.5 vs. 0.1 ± 0.7; p

Background Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine. Methods Using variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve. We investigated whether deep learning has additional benefits with experts' visual interpretation to identify abnormal patterns. Findings The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts' visual interpretation was helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without the model. Interpretation We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.

Background: Orthostatic hypotension (OH) may antedate Parkinson’s disease (PD) or be found in early stages of the disease. OH may induce a PD brain to chronic hypotensive insults. 18F-Florbetaben (18F-FBB) tracer has a high first-pass influx rate and can be used with positron emission tomography (PET) as a surrogate marker for early- and late-phase evaluation of cerebral perfusion and cerebral amyloidosis, respectively. Objective: In this study, we evaluated whether 18F-FBB uptake in the early- and late-phases of PD was related to OH. This study manipulated the imaging modality to illustrate the physiology of cerebral flow with OH in PD (PD + OH). Methods: A group of 73 early-stage PD patients was evaluated with a head-up tilt-test and 18F-FBB PET imaging. The cognitive status was assessed by a comprehensive battery of neuropsychological tests. PET images were normalized, and both early- and late-phase standardized uptake value ratios (SUVRs) of pre-specified regions were obtained. The associations between regional SUVRs and OH and cognitive status were analyzed. Results: Twenty (27.4%) participants had OH. Thirteen (17.8%) patients were interpreted as having amyloid pathology based on regional 18F-FBB uptake. Early-phase SUVRs were higher in specific brain regions of PD + OH patients than those without OH. However, late-phase SUVRs did not differ between the groups. The early-phase SUVRs were not influenced by amyloid burden or by interaction between amyloid and orthostatic hypotension. Cognitive functions were not disparate when PD + OH patients were contrasted with non-OH patients in this study. Conclusion: Cerebral blood flow was elevated in patients with early PD + OH. This finding suggests augmented cerebral perfusion in PD + OH might be a compensatory regulation in response to chronic OH.

The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging (rs-fMRI), we constructed a scale-free binary graph for each subject, using internodal time-series correlation of regions-of-interest (ROIs) as a proximity measure. The resulted network could be embedded onto manifolds of various curvature and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Usingmodel, we reduced the dimension of the network into 2-D hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the hyperbolic discs. Each individual disc revealed decentralized nature of information flow and anatomic relevance. Using the hyperbolic distance on themodel, we could detect the anomaly of network in autistic spectrum disorder (ASD) subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.

Background This study investigated changes in brain perfusion and Aβ burden according to the progression of Alzheimer’s disease (AD) by using a dual-phase 18F-florbetaben (FBB) PET protocol. Methods Sixty subjects, including 12 with Aβ-negative normal cognition (Aβ−NC), 32 with Aβ-positive mild cognitive impairment (Aβ+MCI), and 16 with Aβ-positive AD (Aβ+AD), were enrolled. A dynamic PET scan was obtained in the early phase (0–10 min, eFBB) and delayed phase (90–110 min, dFBB), which were then averaged into a single frame, respectively. In addition to the averaged eFBB, an R1 parametric map was calculated from the eFBB scan based on a simplified reference tissue model (SRTM). Between-group regional and voxel-wise analyses of the images were performed. The associations between cognitive profiles and PET-derived parameters were investigated. Results Both the R1 and eFBB perfusion reductions in the cortical regions were not significantly different between the Aβ−NC and Aβ+MCI groups, while they were significantly reduced from the Aβ+MCI to Aβ+AD groups in regional and voxel-wise analyses. However, cortical Aβ depositions on dFBB were not significantly different between the Aβ+MCI and Aβ+AD groups. There were strong positive correlations between the R1 and eFBB images in regional and voxel-wise analyses. Both perfusion components showed significant correlations with general and specific cognitive profiles. Conclusion The results of this study demonstrated the feasibility of dual-phase 18F-FBB PET to evaluate different trajectories of dual biomarkers for neurodegeneration and Aβ burden over the course of AD. In addition, both eFBB and SRTM-based R1 can provide robust indices of brain perfusion.

Background This study investigated changes in brain perfusion and Aβ burden according to the progression of Alzheimer’s disease (AD) by using a dual-phase 18F-florbetaben (FBB) PET protocol. Methods Sixty subjects, including 12 with Aβ-negative normal cognition (Aβ−NC), 32 with Aβ-positive mild cognitive impairment (Aβ+MCI), and 16 with Aβ-positive AD (Aβ+AD), were enrolled. A dynamic PET scan was obtained in the early phase (0–10 min, eFBB) and delayed phase (90–110 min, dFBB), which were then averaged into a single frame, respectively. In addition to the averaged eFBB, an R1 parametric map was calculated from the eFBB scan based on a simplified reference tissue model (SRTM). Between-group regional and voxel-wise analyses of the images were performed. The associations between cognitive profiles and PET-derived parameters were investigated. Results Both the R1 and eFBB perfusion reductions in the cortical regions were not significantly different between the Aβ−NC and Aβ+MCI groups, while they were significantly reduced from the Aβ+MCI to Aβ+AD groups in regional and voxel-wise analyses. However, cortical Aβ depositions on dFBB were not significantly different between the Aβ+MCI and Aβ+AD groups. There were strong positive correlations between the R1 and eFBB images in regional and voxel-wise analyses. Both perfusion components showed significant correlations with general and specific cognitive profiles. Conclusion The results of this study demonstrated the feasibility of dual-phase 18F-FBB PET to evaluate different trajectories of dual biomarkers for neurodegeneration and Aβ burden over the course of AD. In addition, both eFBB and SRTM-based R1 can provide robust indices of brain perfusion.

Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer’s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method’s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and −0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice.

Tumor metabolic phenotype can be assessed with integrated image pattern analysis of 18F-fluoro-deoxy-glucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT), called radiomics. This study was performed to assess the prognostic value of radiomics PET parameters in head and neck squamous cell carcinoma (HNSCC) patients. 18F-fluoro-deoxy-glucose (FDG) PET/CT data of 215 patients from HNSCC collection free database in The Cancer Imaging Archive (TCIA), and 122 patients in Seoul St. Mary’s Hospital with baseline FDG PET/CT for locally advanced HNSCC were reviewed. Data from TCIA database were used as a training cohort, and data from Seoul St. Mary’s Hospital as a validation cohort. With the training cohort, primary tumors were segmented by Nestles’ adaptive thresholding method. Segmental tumors in PET images were preprocessed using relative resampling of 64 bins. Forty-two PET parameters, including conventional parameters and texture parameters, were measured. Binary groups of homogeneous imaging phenotypes, clustered by K-means method, were compared for overall survival (OS) and disease-free survival (DFS) by log-rank test. Selected individual radiomics parameters were tested along with clinical factors, including age and sex, by Cox-regression test for OS and DFS, and the significant parameters were tested with multivariate analysis. Significant parameters on multivariate analysis were again tested with multivariate analysis in the validation cohort. A total of 119 patients, 70 from training, and 49 from validation cohort, were included in the study. The median follow-up period was 62 and 52 months for the training and the validation cohort, respectively. In the training cohort. binary groups with different metabolic radiomics phenotypes showed significant difference in OS (p = 0.036), and borderline difference in DFS (p = 0.086). Gray-Level Non-Uniformity for zone (GLNUGLZLM) was the most significant prognostic factor for both OS (hazard ratio [HR] 3.1, 95% confidence interval [CI] 1.4–7.3, p = 0.008) and DFS (HR 4.5, CI 1.3–16, p = 0.020). Multivariate analysis revealed GLNUGLZLM as an independent prognostic factor for OS (HR 3.7, 95% CI 1.1–7.5, p = 0.032). GLNUGLZLM remained as an independent prognostic factor in the validation cohort (HR 14.8. 95% CI 3.3–66, p

Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD). Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29). Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005). Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.

Additional file 1:. Supplementary fig. 1 Association between prognosis and SST2 expression level. High-SST2 status tended to be a poor prognostic factor when limited to TNBC subjects without statistical significance (p = 0.16) (a). When normal kidney tissue was defined as a reference, thymoma with low SST2 status and glioblastoma with high-SST2 status showed good prognosis (b-c)

Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.

Cardiac neuronal nitric oxide synthase (nNOS) is an important molecule that regulates intracellular Ca2+ homeostasis and contractility of healthy and diseased hearts. Here, we examined the effects of nNOS on fatty acid (FA) regulation of left ventricular (LV) myocyte contraction in sham and angiotensin II (Ang II)-induced hypertensive (HTN) rats. Our results showed that palmitic acid (PA, 100 μM) increased the amplitudes of sarcomere shortening and intracellular ATP in sham but not in HTN despite oxygen consumption rate (OCR) was increased by PA in both groups. Carnitine palmitoyltransferase-1 inhibitor, etomoxir (ETO), reduced OCR and ATP with PA in sham and HTN but prevented PA potentiation of sarcomere shortening only in sham. PA increased nNOS-derived NO only in HTN. Inhibition of nNOS with S-methyl-l-thiocitrulline (SMTC) prevented PA-induced OCR and restored PA potentiation of myocyte contraction in HTN. Mechanistically, PA increased intracellular Ca2+ transient ([Ca2+]i) without changing Ca2+ influx via L-type Ca2+ channel (I-LTCC) and reduced myofilament Ca2+ sensitivity in sham. nNOS inhibition increased [Ca2+]i, I-LTCC and reduced myofilament Ca2+ sensitivity prior to PA supplementation; as such, normalized PA increment of [Ca2+]i. In HTN, PA reduced I-LTCC without affecting [Ca2+]i or myofilament Ca2+ sensitivity. However, PA increased I-LTCC, [Ca2+]i and reduced myofilament Ca2+ sensitivity following nNOS inhibition. Myocardial FA oxidation (18F-fluoro-6-thia-heptadecanoic acid, 18F-FTHA) was comparable between groups, but nNOS inhibition increased it only in HTN. Collectively, PA increases myocyte contraction through stimulating [Ca2+]i and mitochondrial activity in healthy hearts. PA-dependent cardiac inotropy was limited by nNOS in HTN, predominantly due to its modulatory effect on [Ca2+]i handling.

Development of c-Met targeting probes based on specifically designed peptides with high affinity and stability could help enhance diagnostic efficacy and therapeutic effects in c-Met positive cancers. The Random non-standard Peptides Integrated Discovery (RaPID) system for synthesizing natural product-like macrocyclic peptides via in vitro translation-based selection has recently emerged to overcome the shortcomings of traditional peptide synthesis. Here, we developed non-standard macrocyclic peptides specific to c-Met, and examined the cancer-targeting efficiency of fluorescein-labeled (FL) anti-c-Met peptides, referred to as aML5-FL and aMD4-FL, both in vitro and in vivo. The aML5-FL effectively targeted SNU-638 gastric cancer cells with high c-Met expression, compared to the aMD4-FL due to its high affinity. After intravenous administration of aML5-FL in a tumor xenograft mouse model, FL signal intensity in the extracted SNU-638 tumors was higher than that in SNU-216 tumors. This study provides preclinical data for the usefulness of novel non-standard macrocyclic peptides developed by the RaPID system for specific biomarker imaging.

Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies., Highlights • Deep learning-based FP-CIT SPECT interpretation model was developed. • Deep learning-based model could overcome interobserver variability. • Its accuracy for discriminating PD from normal was comparable to the clinical standard. • It also showed high accuracy for differentiating PD from nonparkinsonian tremor. • Clinical follow-up results showed SWEDD could be reclassified to PD by our model.