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Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors.
The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients.
Emerging data suggest that radiomics can be used to predict outcomes in SCCHN. At present, only few data are available for pre-treatment MRI.
Prognosis of extremity soft tissue sarcomas (ESTS) and retroperitoneal sarcomas (RPS) is currently estimated on clinical-pathological features, as those incorporated in the Sarculator nomogram.
The rupture of a vulnerable plaque, known as ulceration, is the most common cause of myocardial infarction. It can be recognized by angiographic features, such as prolonged intraluminal filling and delayed clearance of the contrast liquid. The diagnosis of such an event is an open challenge due to the limited angiographic resolution and acquisition frequency.
In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps.
Patients with peripheral artery disease who undergo endovascular treatment are often inflicted by in-stent restenosis. The relation between restenosis and abnormal hemodynamics may be analyzed using patient-specific computational fluid dynamics (CFD) simulations.
The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1-weighted and T2-weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability.
The effect of time of repetition (TR) and time of echo (TE) on radiomic features was evaluated using a virtual phantom. Forty-two T1-weighted MRI images of the same virtual phantom were simulated with TR and TE in a range used in clinical practice.
The health sector has traditionally been one of the early adopters of databases, from the most simple Electronic Health Record (formerly Computer-Based Patient Record) systems in use in general practice, hospitals and intensive care units to big data, multidata based systems used to support diagnosis and care decisions.