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Abstract This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

Introduction: SPECT is reconstructed using iterative techniques incorporating photon attenuation correction based on the X ray transmission map and scatter correction. Since the quality of image in SPECT scans depends on the imaging parameters that are determined experimentally in the field of nuclear medicine, designing a dedicated scanning method for 99mTc/SPECT is need to improve the accuracy of disease diagnostic. Therefore, in this study with the aims of assessment and evaluation the effect of different filters on image quality, assessment of quality criteria on image before and after optimization and determine optimum algorithm for reconstruction on liver scanning using 99m Tc for SPECT studies. Material and Method: Filtered Back-projection reconstruction method had been used in liver scanning using 99m Tc/EDDA/HYNIC-TOC for SPECT image and effect of different filters on SPECT imaging have been evaluated. Results and Conclusion: Based on results, we suggest the Hamming filter to be used for visual analysis of Liver SPECT because of its ability to produce the high-quality image. Instead, the Butterworth filter is suggested for quantitative analysis because of its ability to balance between image quality and noise.

Background: Gastro-esophageal (GE) junction cancer is the fastest-growing tumor, particularly in the United States (US). Objective: This study aimed to compare dosimetric and radiobiological factors among field-in-field (FIF), three-field (3F), and four-field box (4FB) radiotherapy planning techniques for gastro-esophageal junction cancer.Material and Methods: In this experimental study, thirty patients with GE junction cancer were evaluated, and three planning techniques (field-in-field (FIF), three-field (3F), and four-field box (4FB)) were performed for each patient for a 6-MV photon beam. Dose distribution in the target volume, the monitor units (MUs) required, and the dose delivered to organs at risk (OARs) were compared for these techniques using the paired-sample t-test. Results: A significant difference was measured between the FIF and 3F techniques with respect to conformity index (CI), dose homogeneity index (HI), and tumor control probability (TCP) for the target organ, as well as the Dmean for the heart, kidneys, and liver. For the spinal cord, the FIF technique showed a slight reduction in the maximum dose compared to the other two techniques. In addition, the V20 Gy of the lungs and the normal tissue complication probability (NTCP) of all OARs were reduced with FIF method. Conclusion: The FIF technique showed better performance for treating patients with gastro-esophageal junction tumors, in terms of dose homogeneity in the target, conformity of the radiation field with the target volume, TCP, less dose to healthy organs, and fewer MU.

Objectives: Carbon nanotubes (CNTs) are allotropes of carbon with a length-to-diameter ratio greater than 106 with the potential uses as medical diagnostic or therapeutic agents. In vitro studies have revealed that gadolinium (Gd) nanoparticle-catalyzed single-walled carbon nanotubes (SWCNTs) possess superparamagnetic properties, which enable them to be used as contrast agents in magnetic resonance imaging (MRI). Our study synthesized Gd-CNT for use as MRI contrast agents. Methods: To reduce the toxicity and solubility of CNTs, it was functionalized, and after loading with Gd was coated with polyethylene glycols (PEG). We then synthesized different concentrations of Gdn3+@CNTs-PEG and Gadovist® to be evaluated as MRI contrast agents. Results: The analysis showed that the Gd concentration in Gadovist® was 12.18% higher than synthesized Gdn3+@CNTs-PEG, but the mean signal intensity of the Gdn3+@CNTs-PEG was approximately 3.3% times higher than Gadovist®. Conclusions: Our findings indicate that synthesized Gdn3+@CNTs-PEG has the potential to be used as an MRI contrast agent in vitro, but in vivo assessment is necessary to determine the bio-distribution, kinetic, and signal enhancement characteristics.