Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment

Table 4 Cardiac radiomics applications relevant to perioperative risk assessment

Application	Imaging modality	Key radiomic features	Diagnostic performance	Perioperative relevance
Coronary plaque vulnerability	CCTA	Texture features (GLCM, GLRLM)	AUC 0.73 vs 0.65 (visual)	Identification of high-risk plaques; perioperative MI risk
Acute MI prediction	Cardiac CT	Kurtosis, short-run high gray-level emphasis	AUC 0.90	Perioperative cardiac event prediction
Myocardial pathology classification	Cardiac CT	Multiple texture features	Sensitivity 86%, specificity 81%	Subclinical cardiomyopathy detection
Cardiac amyloidosis detection	Cardiac CT	Radiomic signature	AUC 0.91 (external validation)	Restrictive physiology identification; fluid management
POAF prediction (CABG)	Non-contrast CT (EAT)	EAT texture and shape	Improved combined model	Postoperative arrhythmia risk; anticoagulation planning
POAF prediction (aortic stenosis)	CT (EAT)	Maximum grey-level, heterogeneity	AUC 0.80	Rate/rhythm control prophylaxis
MACE prediction	CCTA (PVAT)	Fat radiomic profile	ΔC-statistic 0.126	Long-term cardiovascular risk stratification
Acute MI vs stable CAD	CCTA (PCAT)	Pericoronary fat radiomics	AUC 0.87 vs 0.77 (attenuation)	Coronary inflammation assessment

CCTA: Coronary computed tomography angiography; AUC: Area under the curve; MI: Myocardial infarction; EAT: Epicardial adipose tissue; POAF: Postoperative atrial fibrillation; CABG: Coronary artery bypass grafting; PVAT: Perivascular adipose tissue; PCAT: Pericoronary adipose tissue; MACE: Major adverse cardiovascular events; CAD: Coronary artery disease.