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Malignant melanoma tends to aggressively metastasize, resulting in it being a potentially lethal form of skin cancer with high mortality rates. The advanced stages of melanoma have a very poor prognosis because of the high tendency for metastasis, and there is therefore, a strong desire to develop efficient technology for the early detection of melanoma. The benzamide structure, which contains aromatic ring and amine group, exhibits a high affinity for melanin, making it a promising agent for targeting melanoma in diagnostic and therapeutic applications. In this study, we synthesized a fluorinated benzamide derivative and chelated it with the radioisotope 68Ga to detect melanoma on positron emission tomography (PET) imaging, and then evaluated its biological properties. We synthesized the new probe 2,2',2,"2"'-(2-(4-(3-(2-((2-(5-fluoropicolinamido)ethyl)(methyl)amino)ethyl)thioureido)benzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl)tetraacetic acid (MI-0202C1) for melanoma imaging from 5-fluoropyridine-2-carboxylic acid and conjugated 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for chelation with 68Ga. The radiochemical yield and radiochemical purity of the 68Ga chelation complex were confirmed by radio-thin layer chromatography (radio TLC). In vitro cellular uptake of 68Ga-MI-0202C1 was verified in B16F10 cells (murine melanoma). In vivo distribution and small animal PET studies were conducted on mice bearing B16F10 xenografts. The MI-0202C1 was chelated with 68Ga at 90°C for 10 min at pH 5, resulting in a radiochemical yield and radiochemical purity of over 95%. The cellular uptake of 68Ga-MI-0202C1 over 60 min was higher in a group treated with L-tyrosine (2 mM) than in a non-treated group, indicating selective uptake of melanin. 68Ga-MI-0202C1 successfully visualized B16F10 xenografts in microPET imaging performed at 30 and 60 min after intravenous injection, suggesting that 68Ga-MI-0202C1 has considerable potential as a diagnostic probe for melanoma. [ABSTRACT FROM AUTHOR]

Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes. By analyzing 30 of the newest publications in this field, we explain how deep learning models produce synthetic nuclear medicine images that closely resemble their real counterparts, significantly enhancing diagnostic accuracy when images are acquired at lower doses than the clinical policies' standard. The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. In summary, our review underscores the significant potential of deep learning in NMI, indicating that synthetic image generation may be essential for addressing the existing limitations of NMI and improving patient outcomes. [ABSTRACT FROM AUTHOR]

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications. [ABSTRACT FROM AUTHOR]

Invasive aspergillosis is a critical complication in immunocompromised patients with hematologic malignancies or with viral pneumonia caused by influenza virus or SARS‑CoV‑2. Although early and accurate diagnosis of invasive aspergillosis can maximize clinical outcomes, current diagnostic methods are time-consuming and poorly sensitive. Here, we assess the ability of 2-deoxy-2-18F-fluorosorbitol (18F-FDS) positron emission tomography (PET) to specifically and noninvasively detect Aspergillus infections. We show that 18F-FDS PET can be used to visualize Aspergillus fumigatus infection of the lungs, brain, and muscles in mouse models. In particular, 18F-FDS can distinguish pulmonary aspergillosis from Staphylococcus aureus infection, both of which induce pulmonary infiltrates in immunocompromised patients. Thus, our results indicate that the combination of 18F-FDS PET and appropriate clinical information may be useful in the differential diagnosis and localization of invasive aspergillosis. Current diagnostic methods for invasive aspergillosis are time-consuming and poorly sensitive. Here, the authors show that positron emission tomography with 2-deoxy-2-18F-fluorosorbitol can visualize Aspergillus fumigatus infection of the lungs, brain and muscles in mouse models, and can distinguish pulmonary aspergillosis from other diseases such as Staphylococcus aureus infection and lung cancer. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to investigate whether the number of metastatic lymph nodes (LNs) could be used as a basis in the radioactive iodine (RAI) dose selection for patients with papillary thyroid carcinoma (PTC). Patients: A total of 595 patients with PTC who received first RAI therapy after total or near‐total thyroidectomy and had no evidence of disease in treatment response assessment were retrospectively enroled from five hospitals. The patients were classified into two subgroups based on the number of metastatic LNs (>5). The multivariate Cox‐proportional hazard model was performed to identify the significant factors for recurrence prediction in each group as well as all enroled patients. Results: Overall, 22 (3.7%) out of 595 patients had the recurrent disease during the follow‐up period. The number of metastatic LNs (>5) was only a significant factor for recurrence prediction in all enroled patients (odds ratio: 7.834, p <.001). In the subgroup with ≤5 metastatic LNs, the presence of extrathyroidal extension was only associated with recurrence (odds ratio: 7.333, p =.024) in multivariate analysis. RAI dose was significantly associated with recurrence rate in which the patients with high‐dose RAI (3.7 GBq or higher) had less incidence of recurrence than those with low‐dose RAI (1.11 GBq) in the subgroup with more than five metastatic LNs (odds ratio: 6.533, p =.026). Conclusions: High‐dose RAI (≥3.7 GBq) therapy significantly lowered the recurrence rate in patients with more than five metastatic LNs. Therefore, RAI dose should be determined based on the number of metastatic LNs as well as conventional risk factors. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the clinical role of F-18 fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) in the identification of the primary site and the selection of the optimal biopsy site in patients with suspected bone metastasis of unknown primary site. Methods: The patients with suspected bone metastasis who underwent PET-CT for evaluation of primary site were enrolled in this study. The primary sites were identified by the histopathologic or imaging studies and were classified according to the FDG uptake positivity of the primary site. To evaluate the guiding capability of PET-CT in biopsy site selection, we statistically analyzed whether the biopsy site could be affected according to the presence of extra-skeletal FDG uptake. Results: Among 74 enrolled patients, 51 patients had a metastatic bone disease. The primary site was identified in 48 of 51 patients (94.1%). Forty-six patients were eligible to test the association of clinical choice of biopsy site with PET positivity of extra-skeletal lesion. The extra-skeletal biopsies were done in 42 out of 43 patients with positive extra-skeletal uptake lesions. Bone biopsies were inevitably performed in the other three patients without extra-skeletal uptake lesions. The association came out to be significant (Fisher's exact test, P < 0.001). Conclusion: F-18 FDG PET-CT significantly contributed not only to identify the primary site but also to suggest optimal biopsy sites in patients with suspected bone metastasis. [ABSTRACT FROM AUTHOR]

Purpose: We investigated whether predictive clinicopathologic factors can be affected by different response criteria and how the clinical usefulness of radioactive iodine (RAI) therapy should be evaluated considering variable factors in patients with differentiated thyroid carcinoma (DTC). Methods: A total of 1563 patients with DTC who underwent first RAI therapy after total or near total thyroidectomy were retrospectively enrolled from 25 hospitals. Response to therapy was evaluated with two different protocols based on combination of biochemical and imaging studies: (1) serum thyroglobulin (Tg) and neck ultrasonography (US) and (2) serum Tg, neck US, and radioiodine scan. The responses to therapy were classified into excellent and non-excellent or acceptable and non-acceptable to minimize the effect of non-specific imaging findings. We investigated which factors were associated with response to therapy depending on the follow-up protocols as well as response classifications. Multivariate logistic regression analysis was performed to identify factors significantly predicting response to therapy. Results: The proportion of patients in the excellent response group significantly decreased from 76.5 to 59.6% when radioiodine scan was added to the follow-up protocol (P < 0.001). Preparation method (recombinant human TSH vs. thyroid hormone withdrawal) was a significant factor for excellent response prediction evaluated with radioiodine scan (OR 2.129; 95% CI 1.687–2.685; P < 0.001) but was not for other types of response classifications. Administered RAI activity, which was classified as low (1.11 GBq) or high (3.7 GBq or higher), significantly predicted both excellent and acceptable responses regardless of the follow-up protocol. Conclusions: The clinical impact of factors related to response prediction differed depending on the follow-up protocol or classification of response criteria. A high administered activity of RAI was a significant factor predicting a favorable response to therapy regardless of the follow-up protocol or classification of response criteria. [ABSTRACT FROM AUTHOR]