Related papers: Generative Models Improve Radiomics Performance in…
Radiomics is an active area of research in medical image analysis, the low reproducibility of radiomics has limited its applicability to clinical practice. This issue is especially prominent when radiomic features are calculated from noisy…
As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low dose CT scans are still poor, mostly due to noise. Deep…
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radiomic features are…
While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale.…
Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially…
Low-dose computed tomography (LDCT) is the standard modality for lung cancer screening, known for its low radiation dose but high noise levels. While existing literature focuses on denoising LDCT images, comparative research on simulating…
As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past a few years. Recent studies in radiomics aim to investigate the relationship between tumors imaging features and…
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is…
LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which…
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a field of study where images are used instead of invasive…
The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This study presents a comprehensive evaluation of…
In high-quality radiotherapy delivery, precise segmentation of targets and healthy structures is essential. This study proposes Radiomics features as a superior measure for assessing the segmentation ability of physicians and…
Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these…
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural…
Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with…
Purpose: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade. Methods and material: Seventy one ccRCC patients were included in…
Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the…
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of…
As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for diagnosis. In this research, three experiment sets were…
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters…