An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Radiomics is a rapidly develo** field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Radiomics research is rapidly growing in recent years, but more concerns on radiomics
reliability are also raised. This review attempts to update and overview the current status of …

No predictive biomarkers can robustly identify patients with non–small cell lung cancer
(NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a …

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 …

Importance For patients with locally advanced esophageal squamous cell carcinoma,
neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the …

Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning,
monitoring, and image-guided interventions of lung cancer patients. Most medical images …