Search results for "radiomic features"

showing 3 items of 3 documents

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

2022

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, th…

Fluid Flow and Transfer Processesradiomic featuresrobustness analysis; radiomic features; quantization levels; segmentation method agreement; DCE-MRI; breast tumorsbreast tumorDCE-MRIProcess Chemistry and TechnologyGeneral Engineeringbreast tumors; DCE-MRI; quantization levels; radiomic features; robustness analysis; segmentation method agreementComputer Science Applicationsquantization levelsrobustness analysisGeneral Materials Sciencesegmentation method agreementbreast tumorsInstrumentation
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CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease

2023

AbstractThis study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alon…

Predictive modelsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRadiomic featuresCognitive NeuroscienceClinical featuresModel explainabilityComputer Vision and Pattern RecognitionPericoronaric adipose fatCoronary artery diseaseMachine learning classifiersComputer Science ApplicationsCognitive Computation
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Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

2023

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray ima…

chest X-ray imagesradiomic featuresSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniwavelet kernelsRadiology Nuclear Medicine and imagingCOVID-19 prognosisComputer Vision and Pattern RecognitionElectrical and Electronic Engineeringmachine learning modelswavelet-derived featurespredictive capabilityComputer Graphics and Computer-Aided Design
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