A5048607623- Lung Cancer Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Lung Cancer Treatments and Mutations
- Colorectal and Anal Carcinomas
- Advanced X-ray and CT Imaging
- Gastric Cancer Management and Outcomes
- COVID-19 diagnosis using AI
- Legal and Policy Issues
- Colorectal Cancer Screening and Detection
Emory University
2023
University of South Florida
2016-2021
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification prediction tasks in radiology oncology. Quantitative Imaging Network members are developing radiomic "feature" sets characterize tumors, general, the size, shape, texture, intensity, margin, other aspects imaging features nodules lesions. Efforts ongoing for an ontology describe lung nodules, with main classes consisting local global shape descriptors, texture-based features, which based on...
Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite life-saving benefit by LDCT, there are many limitations this imaging modality including high rates indeterminate pulmonary nodules. Radiomics is process extracting and analyzing image-based, quantitative features from region-of-interest which then can be analyzed to develop decision support tools that improve cancer screening. Although prior published research has shown delta radiomics...
Recent efforts have demonstrated that radiomic features extracted from the peritumoral region, area surrounding tumor parenchyma, clinical utility in various cancer types. However, as like any features, could also be unstable and/or nonreproducible. Hence, purpose of this study was to assess stability and reproducibility computed tomography (CT) regions lung lesions where defined consistency a feature by different segmentations, image acquisitions.Stability measured utilizing "moist run"...
Abstract We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures were used to test the hypothesis that can be as predictor nodule malignancy and patient survival. To do this, we use National Lung Screening Trial (NLST) dataset determine if represent differences between in non-lung patients. 253 participants are training set 207 set. discriminate cancerous from non-cancerous at time diagnosis, a...
Lung cancer causes more deaths globally than any other type of cancer. To determine the best treatment, detecting EGFR and KRAS mutations is interest. However, non-invasive ways to obtain this information are not available. Furthermore, many times there a lack big enough relevant public datasets, so performance single classifiers outstanding. In paper, an ensemble approach applied increase mutation prediction using small dataset. A new voting scheme, Selective Class Average Voting (SCAV),...
Abstract Background Current guidelines for lung cancer screening increased a positive scan threshold to 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens performed size‐specific analyses predict incidence using three nodule size classes (<6 [small], 6‐16 [intermediate], ≥16 [large]). Methods 219 (T0) nodules delta which are the change T0 first (T1). Nodules were identified 160 cases diagnosed with at T1 or second screen (T2) 307 nodule‐positive...
Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size a nodule has high predictive power for malignancy. In literature, convolutional neural networks (CNNs) have become widely used medical image analysis. We study ability CNN to capture computed tomography images after are resized input. For our experiments, we National Lung Screening Trial data set. Nodules were labeled into 2 categories (small/large) based on original...
Lung cancer has high mortality and occurrence worldwide. Radiomics is a method for extracting quantitative features from medical images that can be used predictive analysis. been applied quite successfully lung nodule malignancy prediction. Along with traditional radiomics, Convolutional Neural Networks (CNN) are now effectively Texture provides information about variation in pixel intensity regions. nodules/tumors possess noticeable texture pattern. That's why radiomics to construct models...
Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided (CAD) models, designed for the classification malignant benign nodules, use image features, selected by feature selectors, making a decision. In this paper, we investigate automated selection different features informed nodule size ranges to increase overall accuracy classification. The NLST dataset one largest available datasets on CT screening NSCLC. We 261...
Lung cancer is a leading cause of cancer-related death worldwide and in the USA. Low Dose Computed tomography (LDCT) primary method detection diagnosis lung cancers. Radiomics provides further analysis using LDCT scans which provide an opportunity for early The convolutional neural network (CNN), powerful image classification recognition, has opened alternative path tumor identification from scans. Nodules have different shapes, boundaries or patterns. In this study, we created feature...
Radiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate outcome, and predict treatment response. However, variance CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values.
The article highlights the key social factors of corruption in today’s Russian society. In study, it is stated that legislation has provided a comprehensive legal framework aimed at combating corruption; law enforcement practices have been improving this direction, too. But, despite this, due to lasting, permanent work formational, domain-related and structural determine both resilience constant reproduction conditions favourable corruption, combat against gives no palpable results. It noted...