Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool

Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan

INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar ligament on MRI between male and female. The specific objectives are to assess the prevalence of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and signal homogeneity and to find differences in alar ligament signal intensity between male and female. This study also aims to determine the association between the heights of respondents with alar ligament signal intensity and dimensions. MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar ligament is depicted in 3 planes and the visualization and variability of the ligament courses, shapes and signal intensity characteristics were determined. The alar ligament dimensions were also measured. RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial planes. The orientations were laterally ascending in most of the subjects (60%), predominantly oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar ligament signal intensity between male and female respondents. No significant association was found between the heights of the respondents with alar ligament signal intensity and dimensions. CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as depicted in our data shows that caution needs to be exercised when evaluating alar ligament, especially during circumstances of injury