73 research outputs found
Radiomics in esophageal and gastric cancer.
No full textEsophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multi-modality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease
No full textRapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease
Reduction of false positives by extracting fuzzy rules from data for polyp detection in CTC scans
No full textSimultaneous feature selection and classification based on genetic algorithms: an application to colonic polyp detection
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Quantitative PET Imaging Using 18F Sodium Fluoride in the Assessment of Metabolic Bone Diseases and the Monitoring of Their Response to Therapy
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Site specific measurements of bone formation using [18F] sodium fluoride PET/CT
No full textDynamic positron emission tomography (PET) imaging with fluorine-18 labelled sodium fluoride ([18F]NaF) allows the quantitative assessment of regional bone formation by measuring the plasma clearance of fluoride to bone at any site in the skeleton. Today, hybrid PET and computed tomography (CT) dual-modality systems (PET/CT) are widely available, and [18F]NaF PET/CT offers a convenient non-invasive method of studying bone formation at the important osteoporotic fracture sites at the hip and spine, as well as sites of pure cortical or trabecular bone. The technique complements conventional measurements of bone turnover using biochemical markers or bone biopsy as a tool to investigate new therapies for osteoporosis, and has a potential role as an early biomarker of treatment efficacy in clinical trials. This article reviews methods of acquiring and analyzing dynamic [18F]NaF PET/CT scan data, and outlines a simplified approach combining venous blood sampling with a series of short (3- to 5-minute) static PET/CT scans acquired at different bed positions to estimate [18F]NaF plasma clearance at multiple sites in the skeleton with just a single injection of tracer
Shape-based ct lung nodule segmentation using five-dimensional mean shift clustering and MEM with shape information
No full textThis paper presents a joint spatial-intensity-shape (JSIS) feature-based method for the segmentation of CT lung nodules. First, a volumetric shape index (SI) feature based on the second-order partial derivatives of the CT image is calculated. Next, the SI feature is combined with spatial and intensity features to form a five-dimensional feature vectors, which are then clustered using mean shift to produce intensity and shape mode maps. Finally, a modified expectation-maximization (MEM) algorithm is applied on the mean shift intensity mode map to merge the neighboring modes with spatial and shape mode maps as priors.The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 80 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.81 with standard deviation of 0.05. Most of the nodules, including challenging juxta-vascular and juxta-pleural nodules, can be properly separated from adjoining tissues.</p
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