Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the
mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human …

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 …

Lung cancer screening using computed tomography (CT) has increased the detection rate of
small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …

Background Radiomics can provide in-depth characterization of cancers for treatment
outcome prediction. Conventional radiomics rely on extraction of image features within a pre …

Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of
various urological conditions. Urolithiasis is a common benign condition with a high …

Simple Summary As the incidence and survival of patients with brain metastases improve,
the burden of treatment-related neurotoxicities will increase for patients and healthcare …

Simple Summary The early detection of lung nodules is important for patient treatment and
follow-up. Many researchers are investigating deep-learning-based lung nodule detection to …

Simple Summary This manuscript aims to address the diagnostic challenges of mediastinal
bulky lymphomas with the baseline value of 18F-FDG PET/CT metabolic, volumetric and …

Foundation models represent a recent paradigm shift in deep learning, where a single large-
scale model trained on vast amounts of data can serve as the foundation for various …