Subscribe to RSS
DOI: 10.1055/a-2499-3122
Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future
Article in several languages: English | deutsch
Abstract
Background
The need for effective early detection and optimal therapy monitoring of cardiovascular diseases as the leading cause of death has led to an adaptation of the guidelines with a focus on cardiac computed tomography (CCTA) in patients with a low to intermediate risk of coronary heart disease (CHD). In particular, the introduction of photon-counting computed tomography (PCCT) in CT diagnostics promises significant advances through higher temporal and spatial resolution, and also enables advanced texture analysis, known as radiomics analysis. Originally developed in oncological imaging, radiomics analysis is increasingly being used in cardiac imaging and research. The aim is to generate imaging biomarkers that improve the early detection of cardiovascular diseases and therapy monitoring.
Method
The present study summarizes the current developments in cardiac CT texture analysis with a particular focus on evaluations of PCCT data sets in different regions, including the myocardium, coronary plaques, and pericoronary/epicardial fat tissue.
Conclusion
These developments could revolutionize the diagnosis and treatment of cardiovascular diseases and significantly improve patient prognoses worldwide. The aim of this review article is to shed light on the current state of radiomics research in cardiovascular imaging and to identify opportunities for establishing it in clinical routine in the future.
Key Points
-
Radiomics: Enables deeper, objective analysis of cardiovascular structures via feature quantification.
-
PCCT: Provides a higher quality image, improving stability and reproducibility in cardiac CT.
-
Early detection: PCCT and radiomics enhance cardiovascular disease detection and management.
-
Challenges: Technical and standardization issues hinder widespread clinical application.
-
Future: Advancing PCCT technologies could soon integrate radiomics in routine practice.
Citation Format
-
Ayx I, Bauer R, Schönberg SO et al. Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future. Rofo 2025; DOI 10.1055/a-2499-3122
Keywords
cardiac - cardiac risk stratification - myocardial texture analysis - radiomics - photon counting computed tomographyPublication History
Received: 19 August 2024
Accepted after revision: 04 December 2024
Article published online:
23 January 2025
© 2025. Thieme. All rights reserved.
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References
- 1 Sidelnikov E, Dornstauder E, Jacob C. et al. Healthcare resource utilization and costs of cardiovascular events in patients with atherosclerotic cardiovascular disease in Germany – results of a claims database study. Journal of Medical Economics 2022; 25: 1199-1206
- 2 Robert Koch-Institut. Welche Auswirkungen hat der demografische Wandel auf Gesundheit und Gesundheitsversorgung?. 2015
- 3 Deutsche Herzstiftung (Hg.)/Deutscher Herzbericht – Update 2024.
- 4 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278: 563-577
- 5 Reimer RP, Reimer P, Mahnken AH. Assessment of Therapy Response to Transarterial Radioembolization for Liver Metastases by Means of Post-treatment MRI-Based Texture Analysis. Cardiovasc Intervent Radiol 2018; 41: 1545-1556
- 6 Ayx I, Froelich MF, Baumann S. et al. Radiomics in Cardiac Computed Tomography. Diagnostics 2023; 13: 307
- 7 Hertel A, Tharmaseelan H, Rotkopf LT. et al. Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT. Eur Radiol 2023; 33: 4905-4914
- 8 Knuuti J, Wijns W, Saraste A. et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. European Heart Journal 2020; 41: 407-477
- 9 Danzi GB, Piccolo R. CT or Invasive Coronary Angiography in Stable Chest Pain. N Engl J Med 2022; 387: 376-380
- 10 Richtlinie Methoden vertragsärztliche Versorgung: Computertomographie-Koronarangiographie bei Verdacht auf eine chronische koronare Herzkrankheit, Gemeinsamer Bundesausschuss, BAnz AT 26.04.2024 B2.
- 11 Zwanenburg A, Vallières M, Abdalah MA. et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020; 295: 328-338
- 12 Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Review of Precision Medicine and Drug Development 2016; 1: 207-226
- 13 Mayerhoefer ME, Materka A, Langs G. et al. Introduction to Radiomics. J Nucl Med 2020; 61: 488-495
- 14 Reuzé S, Schernberg A, Orlhac F. et al. Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges. International Journal of Radiation Oncology*Biology*Physics 2018; 102: 1117-1142
- 15 Polidori T, De Santis D, Rucci C. et al. Radiomics applications in cardiac imaging: a comprehensive review. Radiol med 2023; 128: 922-933
- 16 Antunes S, Esposito A, Palmisanov A. et al. Characterization of normal and scarred myocardium based on texture analysis of cardiac computed tomography images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando: IEEE; 2016: 4161-4164
- 17 Hinzpeter R, Wagner MW, Wurnig MC. et al. Texture analysis of acute myocardial infarction with CT: First experience study. PLoS ONE 2017; 12: e0186876
- 18 Mannil M, Von Spiczak J, Manka R. et al. Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible. Invest Radiol 2018; 53: 338-343
- 19 Ayx I, Tharmaseelan H, Hertel A. et al. Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT. Diagnostics (Basel) 2022; 12
- 20 Wolf EV, Müller L, Schoepf UJ. et al. Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium. Eur Radiol Exp 2023; 7: 59
- 21 Ayx I, Tharmaseelan H, Hertel A. et al. Myocardial Radiomics Texture Features Associated with Increased Coronary Calcium Score-First Results of a Photon-Counting CT. Diagnostics (Basel) 2022; 12
- 22 Esposito A, Palmisano A, Antunes S. et al. Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images. Mol Imaging Biol 2018; 20: 816-825
- 23 Shu Z-Y, Cui S-J, Zhang Y-Q. et al. Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram. Journal of Nuclear Cardiology 2022; 29: 262-274
- 24 Kay FU, Abbara S, Joshi PH. et al. Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans: Validation in the DHS. Circ: Cardiovascular Imaging 2020; 13: e009678
- 25 Cavallo AU, Troisi J, Muscogiuri E. et al. Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension. Diagnostics 2022; 12: 322
- 26 Oikonomou EK, Williams MC, Kotanidis CP. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019; 40: 3529-3543
- 27 Lin A, Kolossváry M, Yuvaraj J. et al. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype. JACC: Cardiovascular Imaging 2020; 13: 2371-2383
- 28 Kahmann J, Nörenberg D, Papavassiliu T. et al. Combined conventional factors and the radiomics signature of coronary plaque texture could improve cardiac risk prediction. Insights Imaging 2024; 15: 170
- 29 Mundt P, Hertel A, Tharmaseelan H. et al. Analysis of Epicardial Adipose Tissue Texture in Relation to Coronary Artery Calcification in PCCT: The EAT Signature!. Diagnostics 2024; 14: 277
- 30 Agnese M, Toia P, Sollami G. et al. Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT. Heliyon 2023; 9: e15984
- 31 Shang J, Ma S, Guo Y. et al. Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol 2022; 32: 1256-1266
- 32 Cundari G, Marchitelli L, Pambianchi G. et al. Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. Radiol med 2024; 129: 380-400
- 33 Szabo L, Salih A, Pujadas ER. et al. Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction. Eur Radiol 2023; 34: 4113-4126
- 34 Kim JN, Gomez-Perez L, Zimin VN. et al. Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques. Bioengineering 2023; 10: 360
- 35 Tharmaseelan H, Froelich MF, Nörenberg D. et al. Influence of local aortic calcification on periaortic adipose tissue radiomics texture features – a primary analysis on PCCT. Int J Cardiovasc Imaging 2022; 38: 2459-2467
- 36 Mundt P, Tharmaseelan H, Hertel A. et al. Periaortic adipose radiomics texture features associated with increased coronary calcium score – first results on a photon-counting-CT. BMC Med Imaging 2023; 23: 97
- 37 Williams MC, Moss AJ, Dweck M. et al. Coronary Artery Plaque Characteristics Associated With Adverse Outcomes in the SCOT-HEART Study. Journal of the American College of Cardiology 2019; 73: 291-301
- 38 Puchner SB, Liu T, Mayrhofer T. et al. High-Risk Plaque Detected on Coronary CT Angiography Predicts Acute Coronary Syndromes Independent of Significant Stenosis in Acute Chest Pain. Journal of the American College of Cardiology 2014; 64: 684-692
- 39 Yoon YE, Lim T-H. Current Roles and Future Applications of Cardiac CT: Risk Stratification of Coronary Artery Disease. Korean J Radiol 2014; 15: 4
- 40 Kolossváry M, Karády J, Szilveszter B. et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ: Cardiovascular Imaging 2017; 10: e006843
- 41 Dunning CA, Rajiah P, Hsieh S. et al. Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images. In: Iftekharuddin KM, Chen W (eds) Medical Imaging 2023: Computer-Aided Diagnosis. San Diego: SPIE; 2023: 102
- 42 Kolossváry M, Park J, Bang J-I. et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. European Heart Journal – Cardiovascular Imaging 2019; 20: 1250-1258
- 43 Li L, Hu X, Tao X. et al. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. European Journal of Radiology 2021; 140: 109769
- 44 Chen Q, Pan T, Wang YN. et al. A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events. Radiology 2023; 307: e221693
- 45 Zhu L, Dong H, Sun J. et al. Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study. Eur Radiol 2024;
- 46 Koçak B. Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics. Diagn Interv Radiol 2022; 28: 450-462
- 47 Park JE, Park SY, Kim HJ. et al. Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol 2019; 20: 1124-1137
- 48 Otsuka K, Fukuda S, Tanaka A. et al. Napkin-Ring Sign on Coronary CT Angiography for the Prediction of Acute Coronary Syndrome. JACC: Cardiovascular Imaging 2013; 6: 448-457