Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

Objective Robust differentiation between infarcted and normal tissue is important for clinical
diagnosis and precision medicine. The aim of this work is to investigate the radiomic …

Background We aimed to analyze the prognostic power of CT-based radiomics models
using data of 14,339 COVID-19 patients. Methods Whole lung segmentations were …

Objective To develop a two-step machine learning (ML) based model to diagnose and
predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT …

A U-shaped contraction pattern was shown to be associated with a better Cardiac
resynchronization therapy (CRT) response. The main goal of this study is to automatically …

We aimed to construct a prediction model based on computed tomography (CT) radiomics
features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A …

To derive and validate an effective machine learning and radiomics‐based model to
differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric …

Radiomic features are powerful tools for characterizing intra-tumor heterogeneity and
prognostic modeling. Yet, the lack of robustness evaluation poses difficulties in their …

We aimed to predict recurrence in lung cancer patients using PET radiomics features and
machine learning algorithms. In this work, 136 non-small cell lung cancer (NSCLC) patients …

The application of radiomic features for predicting and diagnosing lung cancer has been
established in previous studies. However, radiomic features might be weak in terms of …