Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Simple Summary Diagnosing cancer at an early stage increases the chance of performing
effective treatment in many tumour groups. Key approaches include screening patients who …

PURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective,
although most eligible people are not being screened. Tools that provide personalized …

Research into the potential benefits of artificial intelligence for comprehending the intricate
biology of cancer has grown as a result of the widespread use of deep learning and …

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays
an essential role in each phase of lung cancer management, from detection to assessment …

Introduction Lung cancer ranks second in new cancer cases and first in cancer-related
deaths worldwide. Precision medicine is working on altering treatment approaches and …

Screening programs must balance the benefit of early detection with the cost of
overscreening. Here, we introduce a novel reinforcement learning-based framework for …

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was
updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data …

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of
great importance. While tests are available for reliable diagnosis, accurate identification of …