This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …

The implementation of low-dose chest CT for lung screening presents a crucial opportunity
to advance lung cancer care through early detection and interception. In addition, millions of …

Simple Summary The present review was performed in order to provide a comprehensive
overview of the existing literature concerning the applications of positron emission …

Purpose Differentiating benign from malignant vertebral compression fractures (VCFs) is a
diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis …

Lung cancer is the most frequent cause of cancer-related death around the world. With the
recent introduction of low-dose lung computed tomography for lung cancer screening, there …

Objectives This study aimed to explore and develop artificial intelligence approaches for
efficient classification of pulmonary nodules based on CT scans. Materials and methods A …

Purpose To compare the diagnostic performance of radiomic analysis with machine learning
(ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial …

Simple Summary In recent years, advances in deep learning have transformed the analysis
of medical imaging, especially in spine oncology. Computed Tomography (CT) imaging is …

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic
surgeons. Although such lesions are usually benign, the risk of malignancy remains …

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a
variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis …