Search results for " Radiomics"
showing 10 items of 18 documents
Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer
2020
BackgroundEGFR-positive Non-small Cell Lung Cancer (NSCLC) is a dynamic entity and tumor progression and resistance to tyrosine kinase inhibitors (TKIs) arise from the accumulation, over time and across different disease sites, of subclonal genetic mutations. For instance, the occurrence of EGFR T790M is associated with resistance to gefitinib, erlotinib, and afatinib, while EGFR C797S causes osimertinib to lose activity. Sensitive technologies as radiomics and liquid biopsy have great potential to monitor tumor heterogeneity since they are both minimally invasive, easy to perform, and can be repeated over patient’s follow-up, enabling the extraction of valuable information. Yet, to date, t…
Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients.
2022
Abstract Purpose Hepatic encephalopathy (HE) is a potential complication of cirrhosis. Magnetic resonance imaging (MRI) may demonstrate hyperintense T1 signal in the globi pallidi. The purpose of this study was to evaluate the performance of MRI-based radiomic features for diagnosing and grading chronic HE in adult patients affected by cirrhosis. Methods Adult patients with and without cirrhosis underwent brain MRI with identical imaging protocol on a 3T scanner. Patients without history of chronic liver disease were the control population. HE grading was based on underlying liver disease, severity of clinical manifestation, and number of encephalopathic episodes. Texture analysis was perfo…
Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors
2021
AbstractThis study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied…
3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients
2022
Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …
A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features
2022
Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manua…
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Radiomics: A New Biomedical Workflow to Create a Predictive Model
2020
‘Radiomics’ is utilized to improve the prediction of patient overall survival and/or outcome. Target segmentation, feature extraction, feature selection, and classification model are the fundamental blocks of a radiomics workflow. Nevertheless, these blocks can be affected by several issues, i.e. high inter- and intra-observer variability. To overcome these issues obtaining reproducible results, we propose a novel radiomics workflow to identify a relevant prognostic model concerning a real clinical problem. In the specific, we propose an operator-independent segmentation system with the consequent automatic extraction of radiomics features, and a novel feature selection approach to create a…
Chest CT in COVID-19 Pneumonia: Potentials and Limitations of Radiomics and Artificial Intelligence
2021
SARS-CoV-2 epidemics has resulted in an unprecedented global health crisis causing a high number of deaths with pneumonia being the most common manifestation. Chest CT is the best imaging modality to identify pulmonary involvement, but unfortunately there are no pathognomonic features for COVID-19 pneumonia, since many other infectious and non-infectious diseases may cause similar alterations. The adoption of artificial intelligence in biomedical imaging has the potential to revolutionize the identification, management, and the patient’s outcome. If adequately validated, it could be used as a support with predictive and prognostic purposes in symptomatic patients but also as a screening tes…
Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomic…
2022
Featured Application Based on results defined in this study, new investigations might propose morpho-functional-based radiomics algorithms for risk stratification with possible impact on treatment management in colorectal cancer. The aim of this study was to investigate the application of [F-18]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [F-18]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded f…
Gastrointestinal Stromal Tumors: Diagnosis, Follow-up and Role of Radiomics in a Single Center Experience
2023
: Gastrointestinal stromal tumors (GISTs) arise from the interstitial cells of Cajal in the gastrointestinal tract and are the most common intestinal tumors. Usually GISTs are asymptomatic, especially small tumors that may not cause any symptoms and may be found accidentally on abdominal CT scans. Discovering of inhibitor of receptor tyrosine kinases has changed the outcome of patients with high-risk GISTs. This paper will focus on the role of imaging in diagnosis, characterization and follow-up. We shall also report our local experience in radiomics evaluation of GISTs.