Radiomics for Alzheimer's Disease: Fundamental Principles and Clinical Applications.

Alzheimer's disease is a neurodegenerative disease with a huge impact on people's quality of life, life expectancy, and morbidity. The ongoing prevalence of the disease, in conjunction with an increased financial burden to healthcare services, necessitates the development of new technologies to be employed in this field. Hence, advanced computational methods have been developed to facilitate early and accurate diagnosis of the disease and improve all health outcomes. Artificial intelligence is now deeply involved in the fight against this disease, with many clinical applications in the field of medical imaging. Deep learning approaches have been tested for use in this domain, while radiomics, an emerging quantitative method, are already being evaluated to be used in various medical imaging modalities. This chapter aims to provide an insight into the fundamental principles behind radiomics, discuss the most common techniques alongside their strengths and weaknesses, and suggest ways forward for future research standardization and reproducibility.

Exploring radiographers' perceptions and knowledge about patient lead shielding: a cross-sectional study in Greece and Cyprus.

The present study aimed to explore radiographers' knowledge, clinical practice and perceptions regarding the use of patient lead shielding in Greece and Cyprus. Qualitative data were analyzed using conceptual content analysis and through the classification of findings into themes and categories. A total of 216 valid responses were received. Most respondents reported not being aware of the patient shielding recommendations issued by the American Association of Physicists in Medicine (67%) or the guidance issued by the British Institute of Radiology (69%). Shielding-related training was generally not provided by radiography departments (74%). Most of them (85%) reported that they need specific guidance on lead shielding practices. Also, 82% of the respondents said that lead shielding should continue to be used outside the pelvic area when imaging pregnant patients. Pediatric patients are the most common patient category to which lead shielding was applied. Significant gaps in relevant training have been identified among radiographers in Greece and Cyprus, highlighting the need for new protocols and provision of adequate training on lead shielding practices. Radiography departments should invest in appropriate shielding equipment and adequately train their staff.

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features.

Introduction: Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD. Methods: In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated. Results: The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively. Discussion: These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

Breast MRI: Where are we currently standing?

Breast cancer is the most frequently occurring malignancy among women, having a great impact on society, economy, and healthcare. It is therefore vital to develop effective imaging methods to perform breast screening, diagnosis, and treatment follow-up. Breast MRI is the most efficient method for screening high-risk patients, for breast lesion differentiation and characterization, and for the assessment of response to treatment. Some novel MRI imaging techniques, such as Diffusion Kurtosis Imaging, perfusion imaging, MR Spectroscopy, hybrid PET/MRI imaging, fMRI and ultra-high field MRI imaging offer the capacity to improve the diagnostic accuracy of breast MRI, while reducing unnecessary biopsies. However, any techniques used in breast MRI should be treated with caution, and after a thoughtful consideration of its main strengths and weaknesses. Fast, unenhanced MRI protocols will benefit our patients, improving their overall MRI experience and avoiding the potential risks of contrast media administration. The implementation of AI-based algorithms, using Deep Learning, Convolutional Neural Networks and Radiomics, will certainly increase the superiority of breast MRI and improve patient outcomes, as they can facilitate lesion differentiation, predict response to treatment, reduce unnecessary biopsies, and also reduce scan times and artefacts.

Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods.

BACKGROUND: Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings. PURPOSE: To explore the feasibility of applying four different transfer learning methods to identify the presence of cardiomegaly in chest X-rays and to compare their diagnostic performance using the radiologists' report as the gold standard. MATERIAL AND METHODS: Two thousand chest X-rays were utilized in the current study: 1000 were normal and 1000 had confirmed cardiomegaly. Of these exams, 80% were used for training and 20% as a holdout test dataset. A total of 2048 deep features were extracted using Google's Inception V3, VGG16, VGG19, and SqueezeNet networks. A logistic regression algorithm optimized in regularization terms was used to classify chest X-rays into those with presence or absence of cardiomegaly. RESULTS: Diagnostic accuracy is reported by means of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with the VGG19 network providing the best values of sensitivity (84%), specificity (83%), PPV (83%), NPV (84%), and overall accuracy (84,5%). The other networks presented sensitivity at 64.1%-82%, specificity at 77.1%-81.1%, PPV at 74%-81.4%, NPV at 68%-82%, and overall accuracy at 71%-81.3%. CONCLUSION: Deep learning using transfer learning methods based on VGG19 network can be used for the automatic detection of cardiomegaly on chest X-ray images. However, further validation and training of each method is required before application to clinical cases.