Differentiation of adenocarcinoma in situ with alveolar collapse from minimally invasive adenocarcinoma or invasive adenocarcinoma appearing as part-solid ground-glass nodules (≤ 2 cm) using computed tomography

Prognostic significance of alveolar collapse in lung invasive adenocarcinoma and its relationship with tumor infiltrating lymphocytes

This study aims to investigate the correlations between alveolar collapse, tumor size, and tumor infiltrating lymphocytes (TILs), while also evaluating the prognostic significance of alveolar collapse in invasive lung adenocarcinoma. 355 patients with solitary invasive lung adenocarcinoma were divided into two groups based on the maximum diameter of alveolar collapse: alveolar collapse ≤ 5 mm group and alveolar collapse > 5 mm group. Differences in clinicopathological characteristics, tumor size, TILs, and prognosis were compared between the two groups. The alveolar collapse > 5 mm group had a higher mean age, larger tumor diameter, and increased TILs levels compared to the alveolar collapse ≤ 5 mm group (P < 0.05). A moderate positive correlation was observed between alveolar collapse and tumor size (r = 0.646, P < 0.001). Lung adenocarcinoma with alveolar collapse > 5 mm demonstrated superior 5-year survival and acted as an independent prognostic indicator (HR=0.152, P = 0.004) in multivariate Cox regression analysis, alongside tumor size (HR=10.172, P = 0.034) and lymph node metastasis (HR=2.88, P = 0.017). The size of alveolar collapse is associated with TILs abundance, suggesting that the immune microenvironment may play a crucial role in alveolar collapse formation. Pathologists should take note of alveolar collapse in lung adenocarcinoma.

New trend in artificial intelligence-based assistive technology for thoracic imaging

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.