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Introduction: Cancer is a leading cause of premature death worldwide. Especially cancers like soft tissue sarcomas of extremities (STSE) pose a challenge in oncologic management. Thus, the assessment of prognosis in patients with such cancers is important to select proper management strategies. Radiomics is a promising approach that has shown a wide range of potential applications including predicting prognosis. This study focused on finding out whether the morphometry-based radiomics features could be used to predict the prognosis of patients with STSE following radiotherapy., Methods: The deidentified images, contours and clinical data from The Cancer Imaging Archive (TCIA) were used to evaluate thirty patients with histologically proven STSE following radiotherapy. Twenty-nine three dimensional (3D) morphometric features were extracted for each patient and the two-sample t-test (one-tailed) with the 95% confidence level was used to determine whether there was a significant difference between the patients who developed recurrence or metastasis (RM) and patients who were recurrence or metastasis-free (RMF) following radiotherapy for each morphometric feature., Results: According to the findings, only surface-to-volume ratio demonstrated a significant difference (p-value of 0.029) between the RM and RMF after receiving radiotherapy for STSE., Conclusion: Only surface-to-volume ratio could be utilized as a predictor for assessing the prognosis of patients with STSE following radiotherapy., Implications for Practice: The ability to predict the response after radiotherapy can facilitate the decision-making process, which will ultimately improve patient outcomes, especially considering the challenges in the management of STSE. This study provides insight that the integration of morphometry-based radiomics features into radiotherapy practice could be useful to evaluate the prognosis of patients who received radiotherapy for STSE., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. B. S. Weerakoon, a co-author, represents the International Advisory Board of the Radiography journal. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

Introduction: Sufficient knowledge and adherence to infection control measures are essential for healthcare workers to effectively combat healthcare-associated infections (HCAIs). Radiography undergraduates should stay updated and proficient in their infection control practice. This study aimed to assess knowledge and practice of infection control measures during radiological procedures among radiography undergraduates in Sri Lanka., Method: A web-based, descriptive, self-administered, cross-sectional study was conducted. The questionnaire collected data on demographic, knowledge and practice related to infection control measures from radiography undergraduates., Results: A total of 167 radiography undergraduates returned the completed questionnaire. The majority (88%) have never participated in infection control programs. The mean knowledge and practice scores were 58% and 41.1%, indicating moderate and poor levels, respectively. A significant difference was observed in the overall knowledge of infection control when considering the academic year (p = 0.001) and the duration of clinical exposure (p = 0.01). There was no significant difference between practice and gender, age, ethnicity, academic year and duration of clinical exposure (p > 0.05)., Conclusions: The results of this study indicate that knowledge of nosocomial infection was moderate and practice was inadequate among most of the radiography students surveyed. This highlights the need to reassess the educational components of radiography programs to incorporate practical and comprehensive teaching, learning and monitoring of infection control measures., (Copyright © 2023. Published by Elsevier Inc.)

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment., (© 2023. Springer Nature Limited.)

Introduction: Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy., Methods: 105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated., Results: Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features., Discussion: Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sherminie, Jayatilake, Hewavithana, Weerakoon and Vijithananda.)

Introduction: The COVID-19 pandemic has severely impacted education and other aspects of life, causing psychological distress. The current study aims to identify anxiety, depression, and stress among radiography undergraduates during the COVID-19 pandemic., Method: A descriptive, cross-sectional study was conducted between November and December 2021 on a sample of 140 radiography undergraduates at the Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya. An online survey with two sections: demographic characteristics and a psychometric scale (DASS-42) was used for data collection., Results: A total of 107 undergraduates responded to the questionnaire giving a response rate of 76.2%. The results revealed that the majority of radiography undergraduate students suffered from mild to extremely severe depression (87.85%), anxiety (92.52%), and stress (73.83%) levels. In addition, more than two-thirds of the students (>73% of participants) reported at least one symptom of depression, anxiety, or stress to varying degrees. Scores for depression, anxiety, and stress did not differ significantly across gender and academic year. However, a significant difference was observed between the two age groups, 23-26 years and > 27 years, regarding depression. The older students reported severe depression, whereas younger students reported moderate depression., Conclusion: A high prevalence of negative psychological impact was observed among radiography undergraduates during the COVID-19 pandemic. This necessitates taking proactive steps to address, safeguard, and nurture undergraduates' mental health and well-being during the current and future pandemic crises to mitigate the negative impacts., (Copyright © 2022. Published by Elsevier Inc.)

Introduction: Understanding the current ICT-related experience is essential for planning and effectively implementing quality healthcare services. Hence, this study aims to assess the knowledge and utilisation of ICT among radiographers in Sri Lanka., Methods: A descriptive cross-sectional study was conducted among 590 practicing radiographers in Sri Lanka. Data was collected through a postal survey using a structured self-administered questionnaire. The questionnaire consisted of three sections: socio-demographic characteristics, existing knowledge of ICT, and utilisation of ICT applications and facilities., Results: A total of 416 radiographers returned the questionnaire giving a response rate of 70.5%. Considering the overall ICT knowledge, 24.0% of the respondents possessed good knowledge, while 54.3% and 21.6% reported having fair and poor knowledge, respectively. The knowledge of ICT was significantly associated with gender, age, level of education, duration of service, and previous ICT training experience (p < 0.05). Digital radiography and electronic patient record (EPR) systems were used by 8% and 17.8% of respondents, respectively. Inadequate ICT facilities (56.7%) were identified as the most significant challenge for radiographers to use ICT., Conclusions: The majority of the respondents in this study had a fair knowledge of ICT, and this knowledge was significantly associated with certain demographic factors. Further, it was found that access to certain ICT applications, such as digital radiography and EPR systems, is limited. Hence, this study highlighted the importance of providing systematic, comprehensive and regular ICT training programmes and improving access to ICT facilities for radiographers., Implications of Practice: The study provides insight into the significance of improving ICT literacy among radiographers in the field. In addition, the findings may draw policymakers' attention to improving radiographers' access to the latest technologies., Competing Interests: Conflict of interest None, (Copyright © 2022 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors., Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed., Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process., Conclusions: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies., (© 2022. The Author(s).)

Introduction: The purpose of this study was to determine the perceptions of Allied Health Sciences undergraduates towards online learning during the COVID 19 outbreak., Methods: A cross-sectional study was conducted with undergraduates of the Faculty of Allied Health Sciences, University of Peradeniya, Sri Lanka. A self-administered online questionnaire consisted of four sections to evaluate demographic information; details of online learning; perspectives and challenges on online learning were used for data collection., Results: A total of 518 responses were received from the five disciplines of radiography (32.8%), nursing (24.9%), medical laboratory sciences (18.2%), pharmacy (14.5%), and physiotherapy (9.7%), resulting in a 76.4% response rate. The majority preferred smartphones (73.2%) for online access, and Zoom is the most utilized online communicating platform (72.8%). The overall respondent's perception score ranged from 9 to 27 (Positive ≥ 18, Neutral = 18, Negative ≤ 18) with a mean (SD) of 20.4 (4.0). Even though the majority (59.7%) agreed that online learning is more comfortable to communicate than conventional learning, most respondents (48.3%) have a negative perception towards offering practical and clinical-based subjects online. Poor internet connections (67.0%) and the lack of electronic devices (53.3%) were the most significant challenges encountered during online learning., Conclusion: The majority of the students have a positive perception towards online learning. Online learning appears to be an efficient learning strategy when students have equal access to online facilities., Implications for Practice: Although the allied health undergraduates faced several challenges, they demonstrated their versatility and acceptance of the online learning strategy during the COVID-19 pandemic. Therefore a well-structured online learning programme will be beneficial for students to continue their studies during a pandemic., Competing Interests: Conflict of interest statement The authors declare that they have no competing interests., (Copyright © 2021 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

The observation of molecular diffusion by means of magnetic resonance imaging (MRI) is significant in the evaluation of the metabolic activity of living tissues. Series of MRI examinations were conducted on a diffusion model to study the behaviour of the diffusion process of different-molecular-weight (MW) paramagnetic MRI contrast agents in an isotropic agar hydrogel medium. The model consisted of a solidified 1 % agar gel with an initial concentration of 0.5 mmol/L contrast solution layered on top of the gel. The diffusion process was monitored at pre-determined time intervals of immediately, 1, 6, 9, 23, and 48 h after introduction of the contrast agents onto the agar gel with a T1-weighted spin-echo (SE) pulse sequence. Three types of paramagnetic contrast agents, Gd-DTPA with a MW of 547.57 g/mol, Prohance with a MW of 558.69 g/mol and MnCl 2 with a MW of 125.84 g/mol, resulted in an approximate average diffusional displacement ratio of 1:1:2 per hour, respectively, within 48 h of the experiment. Therefore, the results of this study supported the hypothesis that the rate of the diffusion process of MRI contrast agents in the agar hydrogel medium is inversely related to their MWs. However, more repetitions are necessary under various types of experimental conditions and also with various types of contrast media of different MWs for further confirmation and validation of these results.