Search

Background/Objectives: Radiography is an essential and low-cost diagnostic method in pulmonary medicine that is used for the early detection and monitoring of lung diseases. An adequate and consistent image quality (IQ) is crucial to ensure accurate diagnosis and effective patient management. This pilot study evaluates the feasibility and effectiveness of the International Atomic Energy Agency (IAEA)'s remote and automated quality control (QC) methodology, which has been tested in multiple imaging centers. Methods: The data, collected between April and December 2022, included 47 longitudinal data sets from 22 digital radiographic units. Participants submitted metadata on the radiography setup, exposure parameters, and imaging modes. The database comprised 968 exposures, each representing multiple image quality parameters and metadata of image acquisition parameters. Python scripts were developed to collate, analyze, and visualize image quality data. Results: The pilot survey identified several critical issues affecting the future implementation of the IAEA method, as follows: (1) difficulty in accessing raw images due to manufacturer restrictions, (2) variability in IQ parameters even among identical X-ray systems and image acquisitions, (3) inconsistencies in phantom construction affecting IQ values, (4) vendor-dependent DICOM tag reporting, and (5) large variability in SNR values compared to other IQ metrics, making SNR less reliable for image quality assessment. Conclusions: Cross-comparisons among radiography systems must be taken with cautious because of the dependence on phantom construction and acquisition mode variations. Awareness of these factors will generate reliable and standardized quality control programs, which are crucial for accurate and fair evaluations, especially in high-frequency chest imaging. [ABSTRACT FROM AUTHOR]

Simple Summary: Multiparametric MRI with radiomics features derived from T2WI and ADC maps distinguished non-tumor regions from significant cancer and predicted the Gleason score using support vector machine (SVM) and random forest (RF) classification methods with tuned hyperparameters, as well as recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) feature selection methods. Successful application of a novel approach to machine learning incorporating recursive feature elimination combined with random forest and support vector classifiers allowed stratification of Gleeson scores in clinical cohorts at a sensitivity greater than 0.91. We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92. [ABSTRACT FROM AUTHOR]

The study of biological soft tissue structures at the micron scale details the function of healthy and pathological tissues, which is vital in the diagnosis and treatment of diseases. Speckle based X-ray phase contrast tomographic scans at a nanometer scale have the potential to thoroughly analyse such tissues in a quantitative and qualitative manner. Diamond light source, the UKs national synchrotron facility developed and refined a 1-D X-ray speckle-based imaging technique, referred to as Fly scan mode. This novel image acquisition technique was used to perform a rapid structural composition scan of rodent lung histology samples. The rodent samples were taken from healthy and Staphylococcus aureus induced acute respiratory distress syndrome models. The analysis and cross comparison of the fly scan method, absorption-based tomography and conventional histopathology H&E staining microscopy are discussed in this paper. This analysis and cross comparison outline the ways the speckle-based technique can be of benefit. These advantages include improved soft tissue contrast, 3-D volumetric rendering, segmentation of specific gross tissue structures, quantitative analysis of gross tissue volume. A further advantage is the analysis of cellular distribution throughout the volumetric rendering of the tissue sample. The study also details the current limitations of this technique and points to ways in which future work on this imaging modality may progress. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) occurs in males at a rate of 21.8%, predominantly at the customary primary site. High cure rates are possible through early detection and therapy when the tumor is still restricted to the prostate. These tumors do not grow rapidly, allowing for periods of up to 20 years between diagnosis and death. Multiparametric MRI (mp-MRI) is used as a non-invasive approach to diagnose PCa in subjects. This imaging method uses MR imaging with at least one functional MRI sequence to detect and characterize PCa. The use of multiparametric magnetic resonance imaging has refined the diagnosis of prostate cancer in radiology. Malignancy-modified critical features in tissue composition, such as heterogeneity, are associated with adverse tumor biology. Heterogeneity can be quantified through texture analysis, an effective technique for reviewing tumor images acquired in routine clinical practice. This study focused on identifying and quantifying tumor heterogeneity from prostate mp-MRI utilizing texture analysis. (1) Background: Multiparametric MRI (mp-MRI) is used to manage patients with PCa. Tumor identification via irregular sampling or biopsy is problematic and does not allow the comprehensive detection of the phenotypic and genetic alterations in a tumor. A non-invasive technique to clinically assess tumor heterogeneity is also in demand. We aimed to identify tumor heterogeneity from multiparametric magnetic resonance images using texture analysis (TA). (2) Methods: Eighteen patients with prostate cancer underwent mp-MRI scans before prostatectomy. A single radiologist matched the histopathology report to single axial slices that best depicted tumor and non-tumor regions to generate regions of interest (ROIs). First-order statistics based on the histogram analysis, including skewness, kurtosis, and entropy, were used to quantify tumor heterogeneity. We compared non-tumor regions with significant tumors, employing the two-tailed Mann–Whitney U test. Analysis of the area under the receiver operating characteristic curve (ROC-AUC) was used to determine diagnostic accuracy. (3) Results: ADC skewness for a 6 × 6 px filter was significantly lower with an ROC-AUC of 0.82 (p = 0.001). The skewness of the ADC for a 9 × 9 px filter had the second-highest result, with an ROC-AUC of 0.66; however, this was not statistically significant (p = 0.08). Furthermore, there were no substantial distinctions between pixel filter size groups from the histogram analysis, including entropy and kurtosis. (4) Conclusions: For all filter sizes, there was poor performance in terms of entropy and kurtosis histogram analyses for cancer diagnosis. Significant prostate cancer may be distinguished using a textural feature derived from ADC skewness with a 6 × 6 px filter size. [ABSTRACT FROM AUTHOR]

The temperature dependence of four inorganic scintillation detectors was examined spectrally using the HYPERSCINT Research Platform 200. The detectors were subject to 6 MV photon irradiations from a linear accelerator, varying only the temperature of the detectors. Optical fibre connectorisation was characterised and found to be essential to the repeatability of measurements. The magnitude of the temperature effect on each detector was evaluated by calculating the percentage change in total photon counts for a range of temperatures. Gd 2 O 2 S:Tb and Gd 2 O 2 S:Eu exhibited the lowest change in counts with temperature, 0.12%/°C and −0.10%/°C, respectively, with ZnS:Ag showing the highest change in counts of −0.55%/°C. Gd 2 O 2 S:Pr photon counts changed by −0.25%/°C. All the scintillators demonstrated linearity when the change in photon counts with temperature in the full-width at half maximum of their spectrum are integrated. Establishing the magnitude of the temperature dependence of the materials is critical to decide whether correction factors are required. This is especially true in applications such as brachytherapy, where detectors equilibrise to body temperature. • Temperature effect on scintillators established in a clinical temperature range. • HYPERSCINT RP-200 used with inorganic scintillators using 6 MV photon beam. • Change in photon output up to 0.55%/°C demonstrating scintillator choice is key. • Linearity of effect demonstrated permitting a range correction methods. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare low-dose-rate prostate brachytherapy treatment plans created using three retrospectively applied planning techniques with plans delivered to patients. Treatment plans were created retrospectively on transrectal ultrasound (TRUS) scans for 26 patients. The technique dubbed 4D Brachytherapy was applied, using TRUS and MRI to obtain prostatic measurements required for the associated webBXT online nomogram. Using a patient's MRI scan to create a treatment plan involving loose seeds was also explored. Plans delivered to patients were made using an intraoperative loose seed TRUS-based planning technique. Prostate V 100 (%), prostate V 150 (%), prostate D 90 (Gy), rectum D 0.1cc (Gy), rectum D 2cc (Gy), urethra D 10 (%), urethra D 30 (%), and prostate volumes were measured for each patient. Statistical analysis was used to assess and compare plans. Prostate volumes measured by TRUS and MRI were significantly different. Prostate volumes calculated by the webBXT online nomogram using TRUS- and MRI-based measurements were not significantly different. Compared with delivered plans, TRUS-based 4D Brachytherapy plans showed significantly lower rectum D 0.1cc (Gy) values, MRI-based 4D Brachytherapy plans showed significantly higher prostate V 100 (%) values and significantly lower rectum D 0.1cc (Gy), urethra D 10 (%), and urethra D 30 (%) values, and loose seed MRI-based plans showed significantly lower prostate V 100 (%), prostate D 90 (Gy), rectum D 0.1cc (Gy), rectum D 2cc (Gy), urethra D 10 (%), and urethra D 30 (%) values. TRUS-based 4D Brachytherapy plans showed similar dosimetry to delivered plans; rectal dosimetry was superior. MRI can be integrated into the 4D Brachytherapy workflow. The webBXT online nomogram overestimates the required number of seeds. [ABSTRACT FROM AUTHOR]