Diagnostic Reference Levels in Mammography in the Asian Context

Background: Breast cancer is the most frequent cancer among the female population globally. Therefore, early detection is helpful for effective treatments and to reduce the mortality rate. Mammography is a radiological examination done with low-energy X-rays to detect abnormalities in breast tissue. This study aims to review the literature to evaluate the techniques, protocols, and conversion factors used to determine the diagnostic reference levels (DRLs); within the Asian continent using both phantom- and patient-based data. Methods: Related articles were systematically reviewed via Pub Med, Google scholar, and freehand search with the aid of relevant terms. Related abstracts in English were screened, and suitable articles were selected after reviewing the full-text. Four hundred and thirty abstracts were screened for relevance, and 12 articles were selected. Results: The study comprises four phantom-based and eight patient-based studies. The studies varied between the types of test subjects, conversion factors, breast compression thickness, and dose calculation protocols. This obstructs continuing the DRLs with the updates and comparisons among countries. Establishments of DRLs in Asian countries are less than the rest of the world. DRLs should be measured continuously, and should be updated based on other clinical parameters of the patients. Conclusion: DRLs in mammography were measured from time to time in different geographical locations in Asia by following various techniques. But when compared with the other regions of the world, there is less consideration for establishing DRLs in Asia. There should be standard protocols and updated conversion factors according to the advancements of the technology to ensure radiation protection with optimal absorbed dose with appropriate image quality

A comparison of erect weight-bearing and non-weight-bearing radiography of the cervical spine in non-trauma patients

Introduction: Various positioning techniques are utilized to enhance the visualization of lower cervical vertebrae on lateral radiographs. However, the effectiveness of these techniques still remains unclear. This study was conducted to determine the effect of the weight-bearing (WB) technique in visualizing lower cervical vertebrae and cervicothoracic junction (C7-T1) on standing lateral cervical radiographs of adult non-trauma patients. The study was conducted using both computed radiography (CR) and digital radiography (DR) systems. Methods: Forty-four CR (29 WB and 15 non-WB – NWB) and 61 DR (26 WB and 35 NWB) lateral C-spine radiographs were prospectively evaluated to assess the visible number of cervical vertebral bodies and C7-T1 junction. The instructions given by the radiographer to the patient for the imaging procedure were also assessed on the Likert scale (very good, good, fair, poor, very poor). Results: There was no significant difference (p > 0.05) in the visualization of the number of vertebral bodies between the two techniques of WB and NWB for CR or DR. Further, no significant relationship (p > 0.05) was observed between the WB technique and the visualization of C7-T1 junction in DR systems. However, a significant difference was identified for CR (p = 0.012). The instruction given to the patient significantly correlated with the visibility of the lower C-spine region within each group of WB and NWB in both imaging systems. Conclusions: The visibility of the number of vertebral bodies in the lower C-spine region in either CR or DR systems did not demonstrate any enhancement with the WB technique. Regardless of the imaging system or techniques used, adequate instructions given to the patient before and during the imaging procedure of C-spine lateral radiography demonstrated a significant improvement in visualizing the lower C-spine region. In this preliminary study, the application of erect WB radiography technique in evaluating the lower cervical region of adult non-trauma patients gives limited advantage

Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy

IntroductionGliomas 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.Methods105 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.ResultsTumor 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.DiscussionNonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

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

Attitude of Sri Lankan radiography undergraduates towards artificial intelligence used in medical imaging

This study was conducted to evaluate the attitude of radiography un-dergraduates in Sri Lanka towards artificial intelligence (AI) on medical imaging. An electronic questionnaire designed by Google forms survey administration software was used for data collection. The questionnaire consisted of different sections to evaluate demographic status of the participants, attitude, practice and knowledge related to AI on medical imaging. A total of 168 students responded to the questionnaire. The majority of them (64.3%) were female. Most of the respondents (92.3%) stated that they have practiced plain radiography imaging mo-dality during their clinical training. Around 67.9% respondents were aware about the AI based applications used in medical imaging. How-ever, the majority of respondents (51.17%) opined that AI will drastical-ly change and revolutionize medical imaging tools and methods in a foreseeable future. Most of the respondents (64.29%) believed that the use of AI based applications will make a radiographer's duties more technical in the next 5-10 years. More than two thirds of the respond-ents (73.8%) stated their interest to involve any research on AI based techniques. In sub group analysis, there is a significant difference (p>0.05) of attitude between male and female respondents while no significant difference (p [J Med Allied Sci. 2021; 11(2):163-171

DataSheet_1_Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy.docx

IntroductionGliomas 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.Methods105 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.ResultsTumor 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.DiscussionNonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.</p

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Abstract 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

GLCM Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques

All the data was obtained from the National Hospital of Sri Lanka (NHSL) and the Teaching Hospital Anuradhapura under the supervision of the Ethical Review Board of NHSL and the Faculty of Medicine, University of peradeniya.  </p