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 …

Despite the rapidly evolving treatment landscape in advanced non-small-cell lung cancer
(NSCLC), developments in neoadjuvant and adjuvant treatments have been nascent by …

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 …

While we recognize the prognostic importance of clinicopathological measures and
circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers …

Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …

Substantial progress has been made in using deep learning for cancer detection and
diagnosis in medical images. Yet, there is limited success on prediction of treatment …

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 …

Many decades in the making, immunotherapy has demonstrated its ability to produce
durable responses in several cancer types. In the last decade, immunotherapy has shown …

High-throughput methods for slide-free three-dimensional (3D) pathological analyses of
whole biopsies and surgical specimens offer the promise of modernizing traditional …