Search Results (113)

Tumor-associated macrophages (TAMs) are pivotal immune cells within the tumor stroma, whose dynamic alterations significantly impact tumor progression and therapeutic responses. Conventional methods for TAM detection, such as biopsy, are invasive and incapable of whole-body dynamic monitoring. In contrast, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) offer a non-invasive imaging approach by targeting TAM-specific biomarkers like CD206, TSPO, and CCR2. This review comprehensively summarizes the advancements in TAM-targeted imaging probes, including cell surface markers, metabolic/functional markers, and multifunctional nanoprobe, while assessing their potential in tumor immune surveillance and tumor targeting therapeutic applications. While current probes, including ⁶⁸Ga-NOTA-anti-CD206 and ⁶⁴Cu-Macrin, have exhibited high specificity and theragnostic potential in preclinical and early clinical trials, challenges such as target heterogeneity, off-target effects, and clinical translation persist. Moving forward, the advancement of multi-target probes, optimization of pharmacokinetics, and incorporation of multimodal imaging technologies are anticipated to further enhance the impact of TAM-targeted imaging in precision medicine and tumor immunotherapy, fostering the refinement of personalized treatment strategies and improving patient outcomes. Full article

Background: The recent introduction of whole-body positron emission tomography/ computed tomography (PET/CT) scanners and multi-bed, multi-time point acquisition technique enable calculating fluorodeoxyglucose (FDG) kinetics in the whole body. However, validating parametric, Patlak-derived data is difficult on phantoms. Methods: This prospective study investigated the effect of quantification methods mean, max, and peak on the metabolic rate (MR-FDG) and distribution volume (DV-FDG) quantification, as well as the diagnostic accuracy of parametric Patlak FDG-PET scans in diagnosing lung lesions and lymph node metastases, using histopathology and follow-up as reference standards. Dynamic whole-body FDG PET was acquired for 80 minutes in 34 patients with indeterminate lung lesions and kinetic parameters extracted from lung lesions and representative mediastinal and hilar lymph nodes. Results: All quantification methods—mean, max, and peak—demonstrated high diagnostic accuracy (AUC: MR-FDG: 0.987–0.991 and 0.893–0.905; DV-FDG: 0.948–0.975 and 0.812–0.825) for differentiating benign from malignant lymph nodes and lung lesions. Differences in the magnitude of MR-FDG (−4.76–14.09) and DV-FDG (−10.64–46.10%) were substantial across methods. Variability was more pronounced in lymph nodes (MR-FDG: 1.37–3.48) than in lung lesions (MR-FDG: 3.31–5.04). The variability was lowest between mean and max quantification, with percentage differences of 40.87 ± 5.69% for MR-FDG and 39.26 ± 7.68% for DV-FDG. Conclusions: The choice of method to measure MR-FDG and DV-FDG greatly influences the results, especially in smaller lesions with large and systematic differences. For lung lesions, a conversion factor between mean and max methods of 40% provides acceptable agreement, facilitating retrospective comparisons of measurements, e.g., in meta-analyses. Full article

Background/Objectives: Positron emission tomography (PET) imaging with radiolabeled amino acids is increasingly used in glioma patients for biopsy planning, tumor delineation, prognostication, and therapy response assessment. This study investigated whether baseline amino acid PET imaging could identify regions at risk of future tumor recurrence. Methods: Retrospective case series of 14 patients with high-grade glioma. Contrast-enhanced magnetic resonance imaging (MRI) data of tumor recurrence and baseline imaging (PET-MRI) were co-registered. Volumes of interest (VOIs) of the high-grade glioma were derived from contrast-enhanced MRI at baseline and follow-up and from amino acid PET at baseline. The Dice similarity coefficient (DSC) was used to assess the overlap between VOIs. Furthermore, dynamic and static PET parameters were compared between the VOIs derived from contrast-enhanced MRI at follow-up and from the region of increased amino acid transport at baseline. Results: Regions of tumor recurrence in high-grade glioma patients overlap significantly more with baseline regions of increased amino acid transport on PET compared to regions of contrast enhancement on baseline MRI (p < 0.001). However, the static and dynamic PET statistics did not differentiate between regions that would later develop tumor recurrence and other areas of increased amino acid transport at baseline. Conclusions: These findings reaffirm the ability of amino acid PET to visualize the infiltrative components of gliomas not detected by contrast-enhanced MRI. Also, this study supports the role of amino acid PET in visualizing glioma infiltration beyond the MRI-visible tumor, but also indicates that accurately predicting the specific regions of recurrence based on baseline PET remains limited. Full article

Background/Objectives: Structural magnetic resonance imaging (MRI) and 18-fluoro-deoxy-glucose positron emission tomography (PET) reveal the structural and functional information of the brain from different dimensions, demonstrating considerable clinical and practical value in the computer-aided diagnosis of Alzheimer’s disease (AD). However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. Methods: In this study, we propose a multi-modal deep domain adaptation (MM-DDA) model that integrates MRI and PET modal data, which aims to maximize the utilization of the complementarity of the multi-modal data and narrow the differences in domain distribution to boost the accuracy of AD classification. Specifically, MM-DDA comprises three primary modules: (1) the feature encoding module, which employs convolutional neural networks (CNNs) to capture detailed and abstract feature representations from MRI and PET images; (2) the multi-head attention feature fusion module, which is used to fuse MRI and PET features, that is, to capture rich semantic information between modes from multiple angles by dynamically adjusting weights, so as to achieve more flexible and efficient feature fusion; and (3) the domain transfer module, which reduces the distributional discrepancies between the source and target domains by employing adversarial learning training. Results: We selected 639 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and considered two transfer learning settings. In ADNI1→ADNI2, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 92.40%, 81.81%, 81.13%, and 85.45%, respectively. In ADNI2→ADNI1, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 94.73%, 81.48%, 85.48%, and 81.69%, respectively. Conclusions: MM-DDA is compared with other deep learning methods on two kinds of transfer learning, and the performance comparison results confirmed the superiority of the proposed method in AD prediction tasks. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. Methods: The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including cerebrospinal fluid (CSF) amyloid-beta 42 (A${\beta }_{42}$), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)—were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF A${\beta }_{42}$ levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework—comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification—on a curated dataset of 307 instances (ages 10–110) from Kaggle, the Alzheimer’s Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. Results: The study highlights the model’s potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Conclusions: Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s disease by providing a scalable computational tool for early risk assessment and intervention planning. Full article

Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [¹¹C]DPA713, which targets the 18-kDa translocator protein (TSPO), offers the ability to quantify neuroinflammation in humans in vivo. The primary objectives of this study were to (1) assess whether uptake of [¹¹C]DPA713, a metric of neuroinflammation, is higher in the neuroforamina and spinal cord of patients with painful cervical radiculopathy compared with that in pain-free participants and (2) assess whether [¹¹C]DPA713 uptake is associated with clinical parameters, such as pain intensity. Methods: Dynamic 60 min [¹¹C]DPA713 PET/CT scans were acquired, and regions of interest were defined for neuroforamina and spinal cord. Resulting time-activity curves were fitted to a single-tissue compartment model using an image-derived input function, corrected for plasma-to-whole blood ratios and parent fractions, to obtain the volume of distribution (V_T) as the primary outcome measure. Secondary neuroinflammation metrics included 1T2k V_T without metabolite correction (1T2k_WB) and Logan V_T. Results: The results indicated elevated levels of 1T2k V_T at the neuroforamina (p < 0.04) but not at the spinal cord (p = 0.16). Neuroforamina and spinal cord 1T2k V_T lack associations with clinical parameters. Secondary neuroinflammatory metrics show associations with clinical parameters such as the likelihood of neuropathic pain. Conclusions: These findings enhance our understanding of painful cervical radiculopathy’s pathophysiology, emphasizing the neuroforamina levels of neuroinflammation as a potential therapeutic target. Full article

Target modules (TMs), intermediate molecules required for UniCAR T-cell therapy, are promising molecules for immunotheranostic approaches. In the current work, we developed TMs containing a monomeric or dimeric form of the antagonist bombesin peptide (BBN2) and assessed their potential for diagnostic imaging using positron emission tomography (PET) as well as immunotherapy in combination with UniCAR T-cells to target and image GRPR expression in prostate cancer. Synthesized monomeric and dimeric BBN2 TMs retained binding to GRPR in vitro. Both BBN2 TMs specifically activated and redirected UniCAR T-cells to eradicate PC3 and LNCaP cancer cells with high efficiency and in a comparable manner. UniCAR T-cells retained a non-exhausted memory phenotype favorable to their persistence and fitness. The ⁶⁸Ga-labeled BBN2 TMs showed proof-of-target towards GRPR in PC3 and LNCaP xenografts with similar uptake profiles for both BBN2 TMs in dynamic PET experiments. Clearance occurred exclusively through renal elimination. A tremendously increased in vivo metabolic stability of the BBN2 TMs was observed compared to their counterparts without E5B9. Both monomeric and dimeric BBN2 TMs represent novel and promising immunotheranostic tools for application in prostate cancer with exceptionally high in vivo metabolic stability. Full article

Vulnerable coronary atherosclerotic plaque involves a dynamic pathophysiologic process within and surrounding an atheromatous plaque in coronary artery intima. The process drastically increases the risk of plaque rupture and is clinically responsible for most cases of acute coronary syndromes, myocardial infarctions, and sudden cardiac deaths. Early detection of vulnerable plaque is crucial for clinicians to implement appropriate risk-mitigation treatment strategies, offer timely interventions, and prevent potentially life-threatening events. There is an imperative clinical need to develop practical diagnostic pathways that utilize non-invasive means to risk-stratify symptomatic patients. Since the early 1990s, the identification of vulnerable plaque in clinical practice has primarily relied on invasive imaging techniques. In the last two decades, CT coronary angiogram (CTCA) has rapidly evolved into the prevalent non-invasive diagnostic modality for assessing coronary anatomy. There are now validated plaque appearances on CTCA correlating with plaque vulnerability. It is worth noting that in clinical practice, most CTCA reports omit mention of vulnerable plaque details because spatial resolution (0.3–0.5 mm) is often insufficient to reliably detect some crucial features of vulnerable plaques, such as thin fibrous caps. Additionally, accurately identifying vulnerable plaque features requires substantial expertise and time, which many cardiologists or radiologists may lack in routine reporting. Cardiac magnetic resonance imaging (cMRI) is also non-invasive and allows simultaneous anatomic and functional assessment of coronary plaques. Despite several decades of research and development, routine clinical application of cMRI in coronary plaque imaging remains hampered by complex imaging protocols, inconsistent image quality, and cost. Molecular imaging with radiotracers, specifically positron emission tomography (PET) with sodium fluoride (Na¹⁸F PET), have demonstrated significant potential as a sensitive and specific imaging procedure for diagnosing vulnerable coronary artery plaque. The study protocol is robust and brief, requiring minimal patient preparation. Compared to CTCA and cMRI, the diagnostic accuracy of this test is less dependent on the experience and expertise of the readers. Furthermore, validated automated quantitative algorithms complement the visual interpretation of the study, enhancing confidence in the diagnosis. This combination of factors makes Na¹⁸F PET a promising tool in cardiology for identifying high-risk coronary plaques. Full article

Background: Anesthesia can significantly impact positron emission tomography (PET) neuroimaging in preclinical studies. Therefore, understanding these effects is crucial for accurate interpretation of the results. In this experiment, we investigate the effect of [¹⁸F]-labeled glucose analog fluorodeoxyglucose ([¹⁸F]FDG) uptake in the brains of rats anesthetized with two commonly used anesthetics for rodents: isoflurane, an inhalation anesthetic, and Hypnorm–Dormicum, a combination injection anesthetic. Materials and Methods: Female adult Sprague Dawley rats were randomly assigned to one of two anesthesia groups: isoflurane or Hypnorm–Dormicum. The rats were submitted to dynamic [¹⁸F]FDG PET scan. The whole brain [¹⁸F]FDG standard uptake value (SUV) and the brain voxel-based analysis were performed. Results: The dynamic [¹⁸F]FDG data revealed that the brain SUV was 38% lower in the isoflurane group after 40 min of image (2.085 ± 0.3563 vs. 3.369 ± 0.5577, p = 0.0008). In voxel-based analysis between groups, the maps collaborate with SUV data, revealing a reduction in [¹⁸F]FDG uptake in the isoflurane group, primarily in the cortical regions, with additional small increases observed in the midbrain and cerebellum. Discussion and Conclusions: The observed differences in [¹⁸F]FDG uptake in the brain may be attributed to variations in metabolic activity. These results underscore the necessity for careful consideration of anesthetic choice and its impact on neuroimaging outcomes in future research. Full article

Patients diagnosed with multiple endocrine neoplasia type-1 (MEN1) often initially present with primary hyperparathyroidism (pHPT), and typically undergo surgical intervention. While laboratory tests are fundamental for diagnosis, imaging is crucial for localizing pathological parathyroids to aid in precise surgical planning. In this pictorial review, we will begin by comprehensively examining key imaging techniques and their established protocols, evaluating their effectiveness in detecting abnormal parathyroid glands. This analysis will emphasize both the advantages and potential limitations within the clinical context of MEN1 patients. Additionally, we will explore integrated imaging approaches that combine multiple modalities to enhance localization accuracy and optimize surgical planning—an essential component of holistic management in MEN1 cases. Various imaging techniques are employed for presurgical localization, including ultrasound (US), multiphase parathyroid computed tomography (CT) scanning (4D CT), magnetic resonance imaging (MRI), and nuclear medicine techniques like single photon emission computed tomography/CT (SPECT/CT) and positron emission tomography/CT (PET/CT). US is non-invasive, readily available, and provides high spatial resolution. However, it is operator-dependent and may have limitations in certain cases, such as intrathyroidal locations, the presence of bulky goiters, thyroid nodules, and previous thyroidectomy. Four-dimensional CT offers dynamic imaging, aiding in the identification of enlarged parathyroid glands, particularly in cases of ectopic or supernumerary glands. Despite concerns about radiation exposure, efforts are underway to optimize protocols and reduce doses, including the use of dual-energy CT. MR imaging offers excellent soft tissue contrast without radiation exposure, potentially providing superior differentiation between parathyroid glands and the surrounding structures. Radionuclide imaging, especially PET/CT using radiopharmaceuticals like [¹⁸F]FCH, shows promising results in localizing parathyroid tumors, particularly in patients with MEN1. [¹⁸F]FCH PET/CT demonstrates high sensitivity and may provide additional information compared to other imaging modalities, especially in cases of recurrent HPT. Full article

In diagnostics, photons are used in basic methods such as computed tomography (CT) and positron emission tomography (PET), which are pivotal tools for high-resolution, non-invasive tumor detection, offering insights into tumor staging and progression. Mentioned techniques facilitate early diagnosis and the planning of therapeutic strategies. However, new methods are emerging, enhancing the precision and detail of diagnostics, such as ultra-weak photon emission (UPE) imagining, two-photon fluorescence imaging, photo acoustic imaging, and others. Therapeutically, external beam radiation therapy (EBRT) uses photons to target cancer cells while minimizing harm to healthy tissue. Photodynamic therapy (PDT), which uses light-sensitive compounds activated by specific wavelengths, represents a photon-based treatment applicable to certain malignancies. Other treatments include photo thermal therapy (PTT), radio dynamic therapy (RDT), intensity-modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), and more. These constantly evolving photon-driven technologies can be used to treat a broad spectrum of cancers, such as pancreatic, prostate, breast, and skin cancers. This review article discusses the latest photon-based methods in oncology, focusing on new possibilities, solutions, perspectives, and the potential disadvantages of these approaches. Full article

The tumor microenvironment (TME) is a critical factor in cancer progression, driving tumor growth, immune evasion, therapeutic resistance, and metastasis. Understanding the dynamic interactions within the TME is essential for advancing cancer management. Molecular imaging provides a non-invasive, real-time, and longitudinal approach to studying the TME, with techniques such as positron emission tomography (PET), magnetic resonance imaging (MRI), and fluorescence imaging offering complementary strengths, including high sensitivity, spatial resolution, and intraoperative precision. Recent advances in imaging probe development have enhanced the ability to target and monitor specific components of the TME, facilitating early cancer diagnosis, therapeutic monitoring, and deeper insights into tumor biology. By integrating these innovations, molecular imaging offers transformative potential for precision oncology, improving diagnostic accuracy and treatment outcomes through a comprehensive assessment of TME dynamics. Full article

The tumor microenvironment (TME) is a dynamic and complex medium that plays a central role in cancer progression, metastasis, and treatment resistance. Among the key elements of the TME, cancer-associated fibroblasts (CAFs) are particularly important for their ability to remodel the extracellular matrix, promote angiogenesis, and suppress anti-tumor immune responses. Fibroblast activation protein (FAP), predominantly expressed by CAFs, has emerged as a promising target in both cancer diagnostics and therapeutics. In nuclear medicine, targeting FAP offers new opportunities for non-invasive imaging using radiolabeled fibroblast activation protein inhibitors (FAPIs). These FAP-specific radiotracers have demonstrated excellent tumor detection properties compared to traditional radiopharmaceuticals such as [¹⁸F]FDG, especially in cancers with low metabolic activity, like liver and biliary tract tumors. The most recent FAPI derivatives not only enhance the accuracy of positron emission tomography (PET) imaging but also hold potential for theranostic applications by delivering targeted radionuclide therapies. This review examines the biological underpinnings of FAP in the TME, the design of FAPI-based imaging agents, and their evolving role in cancer diagnostics, highlighting the potential of FAP as a target for precision oncology. Full article

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability. Full article

In this manuscript, a novel presenilin-2 (PSEN2) mutation, Val226Ala, was found in a 59-year-old Korean patient who exhibited rapid progressive memory dysfunction and hallucinations six months prior to her first visit to the hospital. Her Magnetic Resonance Imaging (MRI) showed brain atrophy, and both amyloid positron emission tomography (PET) and multimer detection system-oligomeric amyloid-beta (Aβ) results were positive. The patient was diagnosed with early onset Alzheimer’s disease. The whole-exome analysis revealed a new PSEN2 Val226Ala mutation with heterozygosity in the 5th transmembrane domain of the PSEN2 protein near the lumen region. Analyses of the structural prediction suggested structural changes in the helix, specifically a loss of a hydrogen bond between Val226 and Gln229, which may lead to elevated helix motion. Multiple PSEN2 mutations were reported in PSEN2 transmembrane-5 (TM5), such as Tyr231Cys, Ile235Phe, Ala237Val, Leu238Phe, Leu238Pro, and Met239Thr, highlighting the dynamic importance of the 5th transmembrane domain of PSEN2. Mutations in TM5 may alter the access tunnel of the Aβ substrate in the membrane to the gamma-secretase active site, indicating a possible influence on enzyme function that increases Aβ production. Interestingly, the current patient with the Val226Ala mutation presented with a combination of hallucinations and memory dysfunction. Although the causal mechanisms of hallucinations in AD remain unclear, it is possible that PSEN2 interacts with other disease risk factors, including Notch Receptor 3 (NOTCH3) or Glucosylceramidase Beta-1 (GBA) variants, enhancing the occurrence of hallucinations. In conclusion, the direct or indirect role of PSEN2 Val226Ala in AD onset cannot be ruled out. Full article