Incidental chest findings on coronary CT angiography: a pictorial essay and management proposal.
Erique Guedes PintoDiana PenhaBruno HochheggerColin MonaghanGláucia ZanettiLuís Taborda-BarataKlaus Loureiro IrionPublished in: Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia (2022)
Many health systems have been using coronary CT angiography (CCTA) as a first-line examination for ischaemic heart disease patients in various countries. The rising number of CCTA examinations has led to a significant increase in the number of reported incidental extracardiac findings, mainly in the chest. Pulmonary nodules are the most common incidental findings on CCTA scans, as there is a substantial overlap of risk factors between the population seeking to exclude ischaemic heart disease and those at risk of developing lung cancer (i.e., advanced age and smoking habits). However, most incidental findings are clinically insignificant and actively pursuing them could be cost-prohibitive and submit the patient to unnecessary and potentially harmful examinations. Furthermore, there is little consensus regarding when to report or actively exclude these findings and how to manage them, that is, when to trigger an alert or to immediately refer the patient to a pulmonologist, a thoracic surgeon or a multidisciplinary team. This pictorial essay discusses the current literature on this topic and is illustrated with a review of CCTA scans. We also propose a checklist organised by organ and system, recommending actions to raise awareness of pulmonologists, thoracic surgeons, cardiologists and radiologists regarding the most significant and actionable incidental findings on CCTA scans.
Keyphrases
- computed tomography
- pulmonary hypertension
- risk factors
- coronary artery
- coronary artery disease
- systematic review
- spinal cord
- case report
- end stage renal disease
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- magnetic resonance
- left ventricular
- smoking cessation
- machine learning
- patient reported outcomes
- patient reported