Stib, Matthew T.

An accurate differential diagnosis for diffuse lung disease can be confidently established by using a systematic approach to identify the predominant radiographic pattern, determine the distribution of disease, and assess for architectural or volume change.

Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital anomaly affecting around 0.4–0.8% of the population, but limited mortality data exist. We characterized trends and disparities in AAOCA-related mortality in the United States from 1999 to 2023. We queried the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research multiple-cause-of-death database for AAOCA-related deaths among all age groups. Age-adjusted mortality rates (AAMR) per 1,000,000 population with 95% confidence interval, stratified by year, sex, race, regions, and urbanization status, were abstracted. Annual percent changes were computed by Joinpoint regression. From 1999 to 2023, there were 6665 AAOCA-related deaths. Overall AAMR fell insignificantly from 1.1 in 1999 to 0.9 by 2001, rose to 1.1 in 2008, declined significantly to 0.5 by 2020, and then increased to 0.8 by 2023. Male AAMRs exceeded females (1999: 1.3 vs 0.9; 2023: 1.2 vs 0.4). Racially, from 1999–2020 to 2021–2023, non-Hispanic (NH) Blacks exhibited the highest AAMR (1.8 vs 1.5), trailed by NH Whites (0.8 vs 0.6) and Hispanics (0.5 vs 0.6). Regionally, AAMRs from 1999 to 2020 were 0.9 in the Northeast, Midwest, and South, and 0.8 in the West. Moreover, both urban and rural AAMRs were 0.9. Among age groups, <44 years mortality declined, with the steepest drop in 15–44 years, while mortality for >45 years plateaued. Although AAOCA mortality declined overall, especially in children and young adults, it remains high among men, NH Blacks, and Midwest residents. Its recent rebound underscores the need for early detection, risk stratification, and surveillance for timely interventions.

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.

Interlobular septal thickening (ILST) is a feature on chest computed tomography (CT) that is characterized by abnormal widening of the interlobular septa, which are thin connective tissues that separate secondary pulmonary lobules within the lung parenchyma. They contain pulmonary venules and lymphatic vessels. On CT, these septa are normally not visible in healthy patients. However, various pulmonary pathologic conditions can lead to ILST. If present, ILST can be categorized into 3 different patterns based on its morphologic appearance: smooth, nodular, and irregular/fibrotic. Each pattern occurs from different disease processes with little overlap. In this pictorial essay, we review common and rare etiologies for each type of ILST. For each disease, we discuss relevant pathophysiology, epidemiology, risk factors, symptoms, physical exam findings, labs, and management. Subsequently, we present the ILST features along with the distribution (eg, unilateral, bilateral, central, peripheral) and ancillary imaging findings that can support the diagnosis. Finally, characteristic and high-resolution figures are presented.

This essay describes diagnostic criteria for and thoracic imaging appearances of acute and chronic coccidioidal pneumonia, unusual disease manifestations, and disseminated disease appearances.

Education-level or socioeconomic background of patients may dictate their ability to understand medical jargon. Inability to understand primary findings from a radiology report may lead to unnecessary anxiety among patients or missed follow up. We aim to meet this challenge by developing a patient-sensitive summarization model for radiology reports. We selected computed tomography (CT) exams of chest as a use-case and collected 7,000 studies from Mayo Clinic. Summarization model was built on top of the T5 large language model (LLM) as our experiments indicated that its text-to-text transfer architecture was suited for abstractive text summarization, resulting in a model with 0.77B trainable parameters. Noisy ground truth for model training was collected by prompting LLaMA-13B model. We recruited experts (board-certified radiologists) and laymen to manually evaluate model-generated summaries generated by model. Our model rarely missed information as marked by majority opinion of radiologists. Laymen indicated 63% improvement in their understanding by reading model-generated layman summaries. Comparison with zero-shot performance of ChatGPT indicated that the proposed model reduced the rate of hallucination by half and rate of missing important information by fivefold. The proposed model can generate reliable summaries for radiology reports understandable by patients with vastly different levels of medical knowledge.