The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high
interobserver variability, which can compromise the optimal assessment of patient …

Objectives The application of artificial intelligence (AI) to the field of pathology has facilitated
the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to …

This manuscript provides a comprehensive overview of the application of artificial
intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses …

Context.—Intraoperative (frozen section) analysis of lung lesions (nodules, masses, ground-
glass opacities) can occasionally be diagnostically challenging. Objective.—To describe …

A novel histologic grading system for invasive lung adenocarcinomas (LUAD) has been
newly proposed and adopted by the World Health Organization (WHO) classification. We …

Background The histological subtype of lung adenocarcinoma is a major prognostic factor.
We developed a new artificial intelligence model to classify lung adenocarcinoma images …

Lung adenocarcinoma staging and grading were recently updated to reflect the link between
histologic growth patterns and outcomes. The lepidic growth pattern is regarded as “in-situ,” …

Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five
primary histologic growth patterns. The quantity of these patterns can be related to tumor …

Introduction Lepidic growth is considered noninvasive in lung nonmucinous
adenocarcinoma, whereas other patterns are invasive. Considerable interobserver …

Lung adenocarcinoma is the most frequent subtype of thoracic malignancy, which is itself
the largest contributor to cancer mortality. The lepidic subtype is a non-invasive tumor …