Lung cancer is a leading cause of cancer death in the United States and globally with the majority of lung cancer cases attributable to cigarette smoking. Given the high societal and personal cost of a diagnosis of lung cancer including that most cases of lung cancer when diagnosed are found at a late stage, work over the past 40 years has aimed to detect lung cancer earlier when curative treatment is possible. Screening trials using chest radiography and sputum failed to show a reduction in lung cancer mortality however multiple studies using low dose CT have shown the ability to detect lung cancer early and a survival benefit to those screened. This review will discuss the history of lung cancer screening, current recommendations and screening guidelines, and implementation and components of a lung cancer screening program.

Tracheobronchomalacia and excessive dynamic airway collapse are underdiagnosed distinct clinical entities that require attention with noninvasive imaging to enable prompt diagnosis and guide proper... Tracheobronchomalacia (TBM) and excessive dynamic airway collapse (EDAC) are airway abnormalities that share a common feature of expiratory narrowing but are distinct pathophysiologic entities. Bot...

The number of implanted devices such as orthopedic hardware and cardiac implantable devices continues to increase with an increase in the age of the patient population, as well as an increase in th...

Imaging plays a central role in improving our understanding of the lymphatic system and in the diagnosis and treatment of lymphatic abnormalities, and radiologists should understand normal and vari... Lymphatic abnormalities encompass a wide range of disorders spanning solitary common cystic lymphatic malformations (LMs) to entities involving multiple organ systems such as lymphangioleiomyomatos...

There is a growing gap between studies describing the capabilities of artificial intelligence (AI) diagnostic systems using deep learning versus efforts to investigate how or when to integrate AI systems into a real-world clinical practice to support physicians and improve diagnosis. To address this gap, we investigate four potential strategies for AI model deployment and physician collaboration to determine their potential impact on diagnostic accuracy. As a case study, we examine an AI model trained to identify findings of the acute respiratory distress syndrome (ARDS) on chest X-ray images. While this model outperforms physicians at identifying findings of ARDS, there are several reasons why fully automated ARDS detection may not be optimal nor feasible in practice. Among several collaboration strategies tested, we find that if the AI model first reviews the chest X-ray and defers to a physician if it is uncertain, this strategy achieves a higher diagnostic accuracy (0.869, 95% CI 0.835–0.903) compared to a strategy where a physician reviews a chest X-ray first and defers to an AI model if uncertain (0.824, 95% CI 0.781–0.862), or strategies where the physician reviews the chest X-ray alone (0.808, 95% CI 0.767–0.85) or the AI model reviews the chest X-ray alone (0.847, 95% CI 0.806–0.887). If the AI model reviews a chest X-ray first, this allows the AI system to make decisions for up to 79% of cases, letting physicians focus on the most challenging subsets of chest X-rays.

The International Society for the Study of Vascular Anomalies (ISSVA) classification is a comprehensive histology-based scheme that was updated in 2018. It is important for cardiothoracic imagers to understand this classification to ensure that accurate terminology is used and that archaic terms are avoided when vascular lesions are described. Knowledge of the various malformations (including common conditions, such as venous malformation, arteriovenous fistula, and arteriovenous malformation) and vascular tumors allows for timely diagnosis and appropriate management. This review describes various vascular anomalies, in accordance with ISSVA classification and terminology; highlights key imaging features associated with each; and discusses the role of different imaging modalities.