A Prospective Evaluation of Chemokine Receptor-4 (CXCR4) Overexpression in High-grade Glioma Using Ga-Pentixafor (Pars-Cixafor™) PET/CT Imaging.

Background: While magnetic resonance imaging (MRI) remains the gold standard for morphological imaging, its ability to differentiate between tumor tissue and treatment-induced changes on the cellular level is insufficient. Notably, glioma cells, particularly glioblastoma multiforme (GBM), demonstrate overexpression of chemokine receptor-4 (CXCR4). This study aims to evaluate the feasibility of non-invasive Ga-Cixafor™ PET/CT as a tool to improve diagnostic accuracy in patients with high-grade glioma.

How much do Ga-, Lu- and I-based radiopharmaceuticals contribute to the global radiation exposure of nuclear medicine staff?

Background: The radiation exposure of nuclear medicine personnel, especially concerning extremity doses, has been a significant focus over the past two decades. This study addresses the evolving practice of NM, particularly with the rise of radionuclide therapy and theranostic procedures, which involve a variety of radionuclides such as Ga, Lu, and I. Traditional studies have concentrated on common radioisotopes like Tc, F, and Y, but there is limited data on these radionuclides, which are more and more frequently used.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Background: Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

Introduction: We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets.

Patients And Methods: Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges.

Explainable AI for automated respiratory misalignment detection in PET/CT imaging.

Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, computed tomography (CT), or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an explainable machine learning approach to detect misalignment artifacts in PET/CT imaging.

In vivo assessment of oxidative stress, neurotoxicity and histological alterations induction in the marine gastropod Stramonita haemastoma exposed to CrO and AlO nanoparticles.

The increased use of nanoparticles (NPs) is expected to raise their presence in the marine ecosystem, which is considered as the final destination of released NPs. This study investigated the toxicity of CrO (42 nm) and AlO (38 nm) NPs (1, 2.5, and 5 mg/L) on the digestive glands of Stramonita haemastoma for 7, 14, and 28 days by oxidative stress biomarkers, neurotoxicity indicator assessment, and histological study.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold.

Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning.

Purpose: Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions.

An efficient dual-domain deep learning network for sparse-view CT reconstruction.

Background And Objective: We develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners.

Methods: We designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively.

Disparities in Surgical Intervention and Health-Related Quality of Life Among Racial/Ethnic Groups With Degenerative Lumbar Spondylolisthesis.

Background And Objectives: Racial and socioeconomic disparities in spine surgery for degenerative lumbar spondylolisthesis persist in the United States, potentially contributing to unequal health-related quality of life (HRQoL) outcomes. This is important as lumbar spondylolisthesis is one of the most common causes of surgical low back pain, and low back pain is the largest disabler of individuals worldwide. Our objective was to assess the relationship between race, socioeconomic factors, treatment utilization, and outcomes in patients with lumbar spondylolisthesis.

Targeting the Complexity of In Vitro Skin Models: A Review of Cutting-Edge Developments.

Skin in vitro models offer much promise for research, testing drugs, cosmetics, and medical devices, reducing animal testing and extensive clinical trials. There are several in vitro approaches to mimicking human skin behavior, ranging from simple cell monolayer to complex organotypic and bioengineered 3-dimensional models. Some have been approved for preclinical studies in cosmetics, pharmaceuticals, and chemicals.

Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs).

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes.

Impact of acidic beverages on composition and surface characteristics of human teeth: scanning electron microscopic, stereomicroscopic and energy dispersive x-ray analyses.

Objectives: The objective of this study was to evaluate the effect of acidic beverages on the surface topography and elemental composition of human teeth.

Methods: A total of five highly acidic beverages (Red Bull, Pepsi, Apple Cidra, Tang Mosambi, and Tang Orange) were investigated. The tooth specimens of experimental groups were submerged in each beverage and incubated at 37 °C for 7 days, whereas, the tooth specimens of control groups were placed in distilled water.

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.

Background: Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized Y glass microspheres SIRT: a preliminary machine learning study.

Background: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing Y selective internal radiation therapy (SIRT).

Materials/methods: This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with Y SIRT.

Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes.

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images.

To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation.