Enhancing bone scan image quality: an improved self-supervised denoising approach.

Bone scans play an important role in skeletal lesion assessment, but gamma cameras exhibit challenges with low sensitivity and high noise levels. Deep learning (DL) has emerged as a promising solution to enhance image quality without increasing radiation exposure or scan time. However, existing self-supervised denoising methods, such as Noise2Noise (N2N), may introduce deviations from the clinical standard in bone scans. This study proposes an improved self-supervised denoising technique to minimize discrepancies between DL-based denoising and full scan images. Retrospective analysis of 351 whole-body bone scan data sets was conducted. In this study, we used N2N and Noise2FullCount (N2F) denoising models, along with an interpolated version of N2N (iN2N). Denoising networks were separately trained for each reduced scan time from 5 to 50%, and also trained for mixed training datasets, which include all shortened scans. We performed quantitative analysis and clinical evaluation by nuclear medicine experts. The denoising networks effectively generated images resembling full scans, with N2F revealing distinctive patterns for different scan times, N2N producing smooth textures with slight blurring, and iN2N closely mirroring full scan patterns. Quantitative analysis showed that denoising improved with longer input times and mixed count training outperformed fixed count training. Traditional denoising methods lagged behind DL-based denoising. N2N demonstrated limitations in long-scan images. Clinical evaluation favored N2N and iN2N in resolution, noise, blurriness, and findings, showcasing their potential for enhanced diagnostic performance in quarter-time scans. The improved self-supervised denoising technique presented in this study offers a viable solution to enhance bone scan image quality, minimizing deviations from clinical standards. The method's effectiveness was demonstrated quantitatively and clinically, showing promise for quarter-time scans without compromising diagnostic performance. This approach holds potential for improving bone scan interpretations, aiding in more accurate clinical diagnoses.

Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.

Purpose: Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While 18F-FP-CIT PET offers advantages in spatial resolution and sensitivity over 123I-ß-CIT or 123I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for 18F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images. Methods: The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of 18F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired 18F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets. Results: The proposed AI-based method successfully generated spatially normalized 18F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R 2 and slope ranging 0.96-0.99 and 0.98-1.02 in both internal and external datasets. Conclusion: Our AI-based SN method enables accurate automatic quantification of striatal activity in 18F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders.

How to Harness the Power of GPT for Scientific Research: A Comprehensive Review of Methodologies, Applications, and Ethical Considerations.

The rapid advancements in natural language processing, particularly with the development of Generative Pre-trained Transformer (GPT) models, have opened up new avenues for researchers across various domains. This review article explores the potential of GPT as a research tool, focusing on the core functionalities, key features, and real-world applications of the GPT-4 model. We delve into the concept of prompt engineering, a crucial technique for effectively utilizing GPT, and provide guidelines for designing optimal prompts. Through case studies, we demonstrate how GPT can be applied at various stages of the research process, including literature review, data analysis, and manuscript preparation. The utilization of GPT is expected to enhance research efficiency, stimulate creative thinking, facilitate interdisciplinary collaboration, and increase the impact of research findings. However, it is essential to view GPT as a complementary tool rather than a substitute for human expertise, keeping in mind its limitations and ethical considerations. As GPT continues to evolve, researchers must develop a deep understanding of this technology and leverage its potential to advance their research endeavors while being mindful of its implications.

Evaluation of Fibroblast Activation Protein Expression Using 68Ga-FAPI46 PET in Hypertension-Induced Tissue Changes.

Chronic hypertension leads to injury and fibrosis in major organs. Fibroblast activation protein (FAP) is one of key molecules in tissue fibrosis, and 68Ga-labeled FAP inhibitor-46 (FAPI46) PET is a recently developed method for evaluating FAP. The aim of this study was to evaluate FAP expression and fibrosis in a hypertension model and to test the feasibility of 68Ga-FAPI46 PET in hypertension. Methods: Hypertension was induced in mice by angiotensin II infusion for 4 wk. 68Ga-FAPI46 biodistribution studies and PET scanning were conducted at 1, 2, and 4 wk after hypertension modeling, and uptake in the major organs was measured. The FAP expression and fibrosis formation of the heart and kidney tissues were analyzed and compared with 68Ga-FAPI46 uptake. Subgroups of the hypertension model underwent angiotensin receptor blocker administration and high-dose FAPI46 blocking, for comparison. As a preliminary human study, 68Ga-FAPI46 PET images of lung cancer patients were analyzed and compared between hypertension and control groups. Results: Uptake of 68Ga-FAPI46 in the heart and kidneys was significantly higher in the hypertension group than in the sham group as early as week 1 and decreased after week 2. The uptake was specifically blocked in the high-dose blocking study. Immunohistochemistry also revealed FAP expression in both heart and kidney tissues. However, overt fibrosis was observed in the heart, whereas it was absent from the kidneys. The angiotensin receptor blocker-treated group showed lower uptake in the heart and kidneys than did the hypertension group. In the pilot human study, renal uptake of 68Ga-FAPI46 significantly differed between the hypertension and control groups. Conclusion: In hypertension, FAP expression is increased in the heart and kidneys from the early phases and decreases over time. FAP expression appears to represent fibrosis activity preceding or underlying fibrotic tissue formation. 68Ga-FAPI46 PET has potential as an effective imaging method for evaluating FAP expression in progressive fibrosis by hypertension.

Monoaminergic degeneration, cognition, and autonomic symptom trajectory in early Parkinson's disease.

INTRODUCTION: Parkinson's disease (PD) encompasses a range of non-motor symptoms attributed to deficits in various neurotransmitter systems. This study aimed to investigate the associations between cognitive and autonomic symptoms and the degeneration of brainstem monoaminergic nuclei, particularly the serotonergic and noradrenergic nuclei, in a prospective cohort of early PD patients. METHODS: Twenty-eight early PD patients (with an average disease duration of approximately three years) underwent baseline [18F]FP-CIT positron emission tomography (PET) scans, Montreal Cognitive Assessment (MoCA), and Composite Autonomic Symptom Scale-31 (COMPASS-31) evaluations, followed by repeat MoCA and COMPASS-31 assessments three years later. Regression models were utilized to analyze both cross-sectional and longitudinal changes in non-motor symptoms relative to baseline degeneration of the noradrenergic locus coeruleus (LC) and serotonergic raphe, normalized by striatal dopaminergic terminal loss. RESULTS: Baseline LC and raphe degeneration in early PD was cross-sectionally associated with poorer MoCA performances. Over the three-year follow-up, gastrointestinal symptoms exhibited progression, while cognitive scores remained stable. Profound baseline degeneration of the LC and raphe, relative to nigrostriatal terminal loss, were predictive of subsequent accelerated deterioration in gastrointestinal symptoms. CONCLUSION: Brainstem non-dopaminergic dysfunction in early PD is linked to cognitive dysfunction and predicts progression in gastrointestinal symptoms, offering potential indicators for worsening non-motor trajectories.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Novel insights into paclitaxel's role on tumor-associated macrophages in enhancing PD-1 blockade in breast cancer treatment.

BACKGROUND: Triple-negative breast cancer (TNBC) poses unique challenges due to its complex nature and the need for more effective treatments. Recent studies showed encouraging outcomes from combining paclitaxel (PTX) with programmed cell death protein-1 (PD-1) blockade in treating TNBC, although the exact mechanisms behind the improved results are unclear. METHODS: We employed an integrated approach, analyzing spatial transcriptomics and single-cell RNA sequencing data from TNBC patients to understand why the combination of PTX and PD-1 blockade showed better response in TNBC patients. We focused on toll-like receptor 4 (TLR4), a receptor of PTX, and its role in modulating the cross-presentation signaling pathways in tumor-associated macrophages (TAMs) within the tumor microenvironment. Leveraging insights obtained from patient-derived data, we conducted in vitro experiments using immunosuppressive bone marrow-derived macrophages (iBMDMs) to validate if PTX could augment the cross-presentation and phagocytosis activities. Subsequently, we extended our study to an in vivo murine model of TNBC to ascertain the effects of PTX on the cross-presentation capabilities of TAMs and its downstream impact on CD8+ T cell-mediated immune responses. RESULTS: Data analysis from TNBC patients revealed that the activation of TLR4 and cross-presentation signaling pathways are crucial for the antitumor efficacy of PTX. In vitro studies showed that PTX treatment enhances the cross-presentation ability of iBMDMs. In vivo experiments demonstrated that PTX activates TLR4-dependent cross-presentation in TAMs, improving CD8+ T cell-mediated antitumor responses. The efficacy of PTX in promoting antitumor immunity was elicited when combined with PD-1 blockade, suggesting a complementary interaction. CONCLUSIONS: This study reveals how PTX boosts the effectiveness of PD-1 inhibitors in treating TNBC. We found that PTX activates TLR4 signaling in TAMs. This activation enhances their ability to present antigens, thereby boosting CD8+ T cell antitumor responses. These findings not only shed light on PTX's immunomodulatory role in TNBC but also underscore the potential of targeting TAMs' antigen presentation capabilities in immunotherapy approaches.

Clinical Factors Affecting Discrepancy Between Predicted and Long-term Actual Lung Function Following Surgery.

PURPOSE: Lung cancer surgery outcomes depend heavily on preoperative pulmonary reserve, with forced expiratory volume in 1 second (FEV1) being a critical preoperative evaluation factor. Our study investigates the discrepancies between predicted and long-term actual postoperative lung function, focusing on clinical factors affecting these outcomes. METHODS: This retrospective observational study encompassed lung cancer patients who underwent preoperative lung perfusion SPECT/CT between 2015 and 2021. We evaluated preoperative and postoperative pulmonary function tests, considering factors such as surgery type, resected volume, and patient history including tuberculosis. Predicted postoperative lung function was calculated using SPECT/CT imaging. RESULTS: From 216 patients (men:women, 150:66; age, 67.9 ± 8.7 years), predicted postoperative FEV1% (ppoFEV1%) showed significant correlation with actual postoperative FEV1% (r = 0.667; P < 0.001). Paired t test revealed that ppoFEV1% was significantly lower compared with actual postoperative FEV1% (P < 0.001). The study identified video-assisted thoracic surgery (VATS) (odds ratio [OR], 3.90; 95% confidence interval [CI], 1.98-7.69; P < 0.001) and higher percentage of resected volume (OR per 1% increase, 1.05; 95% CI, 1.01-1.09; P = 0.014) as significant predictors of postsurgical lung function improvement. Conversely, for the decline in lung function postsurgery, significant predictors included lower percentage of resected lung volume (OR per 1% increase, 0.92; 95% CI, 0.86-0.98; P = 0.011), higher preoperative FEV1% (OR, 1.03; 95% CI, 1.01-1.07; P = 0.009), and the presence of tuberculosis (OR, 5.19; 95% CI, 1.48-18.15; P = 0.010). Additionally, in a subgroup of patients with borderline lung function, VATS was related with improvement. CONCLUSIONS: Our findings demonstrate that in more than half of the patients, actual postsurgical lung function exceeded predicted values, particularly following VATS and with higher volume of lung resection. It also identifies lower resected lung volume, higher preoperative FEV1%, and tuberculosis as factors associated with a postsurgical decline in lung function. The study underscores the need for precise preoperative lung function assessment and tailored postoperative management, with particular attention to patients with relevant clinical factors. Future research should focus on validation of clinical factors and exploring tailored approaches to lung cancer surgery and recovery.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.

Purpose: This study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images. Methods: 150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted. Results: Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups. Conclusion: BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model.

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

SpatialSPM: statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets.

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method, SpatialSPM, that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by kidney and mouse olfactory bulb datasets as well as mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. Beyond traditional analyses, SpatialSPM is capable of generating statistical parametric maps, including T-scores and Pearson correlation coefficients. This feature enables the identification of specific regions exhibiting differentially expressed genes across tissue samples, enhancing the depth and specificity of ST studies. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

Symmetric and Profound Monoaminergic Degeneration in Parkinson's Disease with Premotor REM Sleep Behavior Disorder.

Background: Rapid eye movement sleep behavior disorder (RBD) may precede or follow motor symptoms in Parkinson's disease (PD). While over 70% of idiopathic RBD cases phenoconvert within a decade, a small subset develops PD after a more extended period or remains nonconverted. These heterogeneous manifestations of RBD in PD prompt subtype investigations. Premotor RBD may signify "body-first" PD with bottom-up, symmetric synucleinopathy propagation. Objective: Explore brainstem and nigrostriatal monoaminergic degeneration pattern differences based on premotor RBD presence and duration in de novo PD patients. Methods: In a cross-sectional analysis of de novo PD patients (n = 150) undergoing FP-CIT PET and RBD Single-Question Screen, the cohort was categorized into groups with and without premotor RBD (PDRBD +/-), with further classification of PDRBD + based on a 10-year duration of premotor RBD. Analysis of FP-CIT binding in the striatum and pons, striatal asymmetry, and striatum-to-pons ratios compared patterns of nigrostriatal and brainstem monoaminergic degeneration. Results: PDRBD + exhibited more severe and symmetrical striatal dopaminergic denervation compared to PDRBD-, with the difference in severity accentuated in the least-affected hemisphere. The PDRBD +<10Y subgroup displayed the most prominent striatal symmetry, supporting a more homogeneous "body-first" subtype. Pontine uptakes remained lower in PDRBD + even after adjusting for striatal uptake, suggesting early degeneration of pontine monoaminergic nuclei. Conclusions: Premotor RBD in PD is associated with severe, symmetrical nigrostriatal and brainstem monoaminergic degeneration, especially in cases with PD onset within 10 years of RBD. This supports the concept of a "widespread, bottom-up" pathophysiological mechanism associated with premotor RBD in PD.

[18F]FDOPA PET/CT in Solid Pseudopapillary Tumor of the Pancreas: a Recurred Tumor Mimicking Splenosis.

Solid pseudopapillary tumor (SPT) of the pancreas is a neoplasm with low malignant potential. It is often challenging to diagnose SPT due to its nonspecific clinical and radiological features, and [18F]FDOPA is effective in diagnosing SPT, particularly in differentiating SPT from benign conditions such as splenosis. A 55-year-old woman underwent distal pancreatectomy and splenectomy for histologically confirmed SPT. She was also initially diagnosed with splenosis. During follow-up, sizes of multiple nodular lesions were increased, raising the possibility of peritoneal seeding of SPT. For diagnosis, a spleen scan and SPECT/CT were performed using 99mTc-labeled damaged red blood cells, which showed no uptake in the peritoneal nodules. Subsequent [18F]FDOPA PET/CT revealed [18F]FDOPA-avidity of the nodules. The patient underwent tumor resection surgery, and the nodules were pathologically confirmed as SPT.

Preoperative evaluation of mediastinal lymph nodes in non-small cell lung cancer using [68Ga]FAPI-46 PET/CT: a prospective pilot study.

PURPOSE: Mediastinal nodal staging is crucial for surgical candidate selection in non-small cell lung cancer (NSCLC), but conventional imaging has limitations often necessitating invasive staging. We investigated the additive clinical value of fibroblast activation protein inhibitor (FAPI) PET/CT, an imaging technique targeting fibroblast activation protein, for mediastinal nodal staging of NSCLC. METHODS: In this prospective pilot study, we enrolled patients scheduled for surgical resection of NSCLC based on specific criteria designed to align with indications for invasive staging procedures. Patients were included when meeting at least one of the following: (1) presence of FDG-positive N2 lymph nodes, (2) clinical N1 stage, (3) central tumor location or tumor diameter of ≥ 3 cm, and (4) adenocarcinoma exhibiting high FDG uptake. [68Ga]FAPI-46 PET/CT was performed before surgery after a staging workup including [18F]FDG PET/CT. The diagnostic accuracy of [68Ga]FAPI-46 PET/CT for "N2" nodes was assessed through per-patient visual assessment and per-station quantitative analysis using histopathologic results as reference standards. RESULTS: Twenty-three patients with 75 nodal stations were analyzed. Histopathologic examination confirmed that nine patients (39.1%) were N2-positive. In per-patient assessment, [68Ga]FAPI-46 PET/CT successfully identified metastasis in eight patients (sensitivity 0.89 (0.52-1.00)), upstaging three patients compared to [18F]FDG PET/CT. [18F]FDG PET/CT detected FDG-avid nodes in six (42.8%) of 14 N2-negative patients. Among them, five were considered non-metastatic based on calcification and distribution pattern, and one was considered metastatic. In contrast, [68Ga]FAPI-46 PET/CT correctly identified all non-metastatic patients solely based on tracer avidity. In per-station analysis, [68Ga]FAPI-46 PET/CT discriminated metastasis more effectively compared to [18F]FDG PET/CT-based (AUC of ROC curve 0.96 (0.88-0.99) vs. 0.68 (0.56-0.78), P < 0.001). CONCLUSION: [68Ga]FAPI-46 PET/CT holds promise as an imaging tool for preoperative mediastinal nodal staging in NSCLC, with improved sensitivity and the potential to reduce false-positive results, optimizing the need for invasive staging procedures.

The Usefulness of 18F-FDG PET to Differentiate Subtypes of Dementia: The Systematic Review and Meta-Analysis.

Background and Purpose: Dementia subtypes, including Alzheimer's dementia (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD), pose diagnostic challenges. This review examines the effectiveness of 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) in differentiating these subtypes for precise treatment and management. Methods: A systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines was conducted using databases like PubMed and Embase to identify studies on the diagnostic utility of 18F-FDG PET in dementia. The search included studies up to November 16, 2022, focusing on peer-reviewed journals and applying the gold-standard clinical diagnosis for dementia subtypes. Results: From 12,815 articles, 14 were selected for final analysis. For AD versus FTD, the sensitivity was 0.96 (95% confidence interval [CI], 0.88-0.98) and specificity was 0.84 (95% CI, 0.70-0.92). In the case of AD versus DLB, 18F-FDG PET showed a sensitivity of 0.93 (95% CI 0.88-0.98) and specificity of 0.92 (95% CI, 0.70-0.92). Lastly, when differentiating AD from non-AD dementias, the sensitivity was 0.86 (95% CI, 0.80-0.91) and the specificity was 0.88 (95% CI, 0.80-0.91). The studies mostly used case-control designs with visual and quantitative assessments. Conclusions: 18F-FDG PET exhibits high sensitivity and specificity in differentiating dementia subtypes, particularly AD, FTD, and DLB. This method, while not a standalone diagnostic tool, significantly enhances diagnostic accuracy in uncertain cases, complementing clinical assessments and structural imaging.

Comprehensive characterization of early-programmed tumor microenvironment by tumor-associated macrophages reveals galectin-1 as an immune modulatory target in breast cancer.

Background: In recent years, there has been considerable interest in the therapeutic targeting of tumor-associated macrophages (TAMs) to modulate the tumor microenvironment (TME), resulting in antitumoral phenotypes. However, key mediators suitable for TAM-mediated remodeling of the TME remain poorly understood. Methods: In this study, we used single-cell RNA sequencing analyses to analyze the landscape of the TME modulated by TAMs in terms of a protumor microenvironment during early tumor development. Results: Our data revealed that the depletion of TAMs leads to a decreased epithelial-to-mesenchymal transition (EMT) signature in cancer cells and a distinct transcriptional state characterized by CD8+ T cell activation. Moreover, notable alterations in gene expression were observed upon the depletion of TAMs, identifying Galectin-1 (Gal-1) as a crucial molecular factor responsible for the observed effect. Gal-1 inhibition reversed immune suppression via the reinvigoration of CD8+ T cells, impairing tumor growth and potentiating immune checkpoint inhibitors in breast tumor models. Conclusion: These results provide comprehensive insights into TAM-mediated early tumor microenvironments and reveal immune evasion mechanisms that can be targeted by Gal-1 to induce antitumor immune responses.

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD. METHODS: A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes. RESULTS: Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%). CONCLUSION: We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

Spatiotemporal characterization of glial cell activation in an Alzheimer's disease model by spatially resolved transcriptomics.

The molecular changes that occur with the progression of Alzheimer's disease (AD) are well known, but an understanding of the spatiotemporal heterogeneity of changes in the brain is lacking. Here, we investigated the spatially resolved transcriptome in a 5XFAD AD model at different ages to understand regional changes at the molecular level. Spatially resolved transcriptomic data were obtained from 5XFAD AD models and age-matched control mice. Differentially expressed genes were identified using spots clustered by anatomical structures. Gene signatures of activation of microglia and astrocytes were calculated and mapped on the spatially resolved transcriptomic data. We identified early alterations in the white matter (WM) of the AD model before the definite accumulation of amyloid plaques in the gray matter (GM). Changes in the early stage of the disease involved primarily glial cell activation in the WM, whereas the changes in the later stage of pathology were prominent in the GM. We confirmed that disease-associated microglia (DAM) and astrocyte (DAA) signatures also showed initial changes in WM and that activation spreads to GM. Trajectory inference using microglial gene sets revealed the subdivision of DAMs with different spatial patterns. Taken together, these results help to understand the spatiotemporal changes associated with reactive glial cells as a major pathophysiological characteristic of AD. The heterogeneous spatial molecular changes apply to identifying diagnostic and therapeutic targets caused by amyloid accumulation in AD.

Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data.

Purpose: Deep learning (DL) has been widely used in various medical imaging analyses. Because of the difficulty in processing volume data, it is difficult to train a DL model as an end-to-end approach using PET volume as an input for various purposes including diagnostic classification. We suggest an approach employing two maximum intensity projection (MIP) images generated by whole-body FDG PET volume to employ pre-trained models based on 2-D images. Methods: As a retrospective, proof-of-concept study, 562 [18F]FDG PET/CT images and clinicopathological factors of lung cancer patients were collected. MIP images of anterior and lateral views were used as inputs, and image features were extracted by a pre-trained convolutional neural network (CNN) model, ResNet-50. The relationship between the images was depicted on a parametric 2-D axes map using t-distributed stochastic neighborhood embedding (t-SNE), with clinicopathological factors. Results: A DL-based feature map extracted by two MIP images was embedded by t-SNE. According to the visualization of the t-SNE map, PET images were clustered by clinicopathological features. The representative difference between the clusters of PET patterns according to the posture of a patient was visually identified. This map showed a pattern of clustering according to various clinicopathological factors including sex as well as tumor staging. Conclusion: A 2-D image-based pre-trained model could extract image patterns of whole-body FDG PET volume by using anterior and lateral views of MIP images bypassing the direct use of 3-D PET volume that requires large datasets and resources. We suggest that this approach could be implemented as a backbone model for various applications for whole-body PET image analyses.