Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Abstract

Recent advances in medical image analysis have been made to improve our understanding of how disease develops, behaves, and responds to treatment. Magnetic resonance imaging (MRI) and positron emission tomography (PET) advanced imaging strategies provide structural and functional phenotypic biomarkers that correlate with key disease processes. Through radiomics and radiogenomics, ML-medical imaging has opened up new perspectives in high-grade glioma diagnosis. As a result, non-invasive and in vivo biomarkers for patient survival, tumor recurrence, and genomics are identified. Tumor genomic imaging signatures can help identify patients who benefit from targeted therapies. Molecular characterization of gliomas and prediction of their evolution would allow treatment optimization. Radiomics-based biomarkers allow for a more in-depth analysis of pathophysiologic processes and insights into diagnosing better, classifying, stratifying, and prognosticating brain tumors and assessing their response to therapy. Radiomics is a new data-driven approach that can help answer clinical questions like diagnosis, prognosis, and treatment response. With encouraging outcomes in brain tumor patients, radiomics and deep learning are still not widely used in clinical practice, requiring more extensive and practical clinical studies.

Imaging More than Skin-Deep: Radiologic and Dermatologic Presentations of Systemic Disorders

“…A novel machine-learning-based model known as radiomics allows for the translation of the medical images to quantitative data. Radiomic features of 18F-FDG-PET have been investigated in recent studies to predict prognosis in patient with metastatic melanoma before immunotherapy treatment [ 1 , 84 , 85 , 86 , 87 , 88 ].…”

“…KS skin involvement is polymorphous, ranging from violaceous macules and papules ( Figure 21 A) to exophytic tumors with associated lymphedema. AIDS-related KS also frequently affects the upper body, head, and neck [ 88 , 89 , 90 , 91 , 92 , 93 ]. Twenty-two percent of patients manifest oral cavity lesions as the presenting sign.…”

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

“…The recent rapid development of artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7,8], 5G connectivity [9], Blockchain, and Internet of Things (IoT) [10,11] is leading to an exponential growth in data usage across a wide range of engineering and commerce disciplines. Over the last decade, the IoT has been recognized for its remarkable potential in computer science.…”

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

“…Recent rapid growth in artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7], 5G connectivity [8], Blockchain [9,10], and Internet of Things (IoT) [11,12] have resulted in an explosion of data in almost all fields of engineering and commerce. With cloud computing, large-scale applications can be deployed quickly and efficiently, affordably on a per-use basis [13].…”