HealthManagement, Volume 23 - Issue 1, 2023

img PRINT OPTIMISED
Radiomics may lead to personalised management and treatment of cardiovascular diseases, which could impact patients’ prognosis.
 

Key Points

  • In recent years, multiple radiomics-based tools have been investigated in the field of cardiovascular imaging.
  • Radiomics may predict outcomes of cardiac diseases without the need of invasive procedures.
  • Cardiac CT radiomics may enhance the assessment of advanced atheromatous plaques, improving risk stratification and treatment in patients with CAD.
  • Radiomics may improve cardiac MRI prognostic value and its well-known capability of myocardial characterization even without the use of contrast agents.
 

Radiomics represents a promising image analysis‏ technique that aims to improve the diagnosis, the‏ characterisation, and the prognosis of diseases by‏ extracting objective quantitative features that may‏ be missed by human eye (Sollini et al. 2019). While‏ mainly developed through oncologic research to obtain‏ information on the characteristics of tumours (Gillies‏ et al. 2016), there is an increasing interest in the use‏ of radiomics for cardiac purposes (Ashrafinia et al.‏ 2021). As cardiovascular diseases represent the main‏ cause of morbidity and mortality worldwide (Virani et‏ al. 2020), there is an ever increasing clinical request for‏ non-invasive diagnostic approaches (Selvanayagam‏ 2016). Therefore, radiomics biomarkers detected by data‏ extraction from cardiac computed tomography (CT) and‏ cardiac magnetic resonance imaging (CMR) may be‏ a valuable tool to assess several cardiac pathologies,‏ such as atherosclerotic coronary artery disease (CAD),‏ myocardial viability, and cardiomyopathies (Kumar et al.‏ 2012; Raisi-Estabragh et al. 2020). Recently, machine‏ learning (ML) and deep learning (DL) algorithms have‏ provided even more options, allowing to better evaluate‏ the characteristics of cardiac disease (Langs et al. 2018).‏

 

Radiomics in Cardiac CT

Cardiac CT angiography (CCTA) has gained a pivotal‏ role in assessing CAD and plays a critical part in‏ evaluating cardiac structures (Hoffmann et al. 2012).‏ CCTA can noninvasively visualise coronary arteries and‏ plaque morphology, representing an invaluable tool for‏ risk stratification, and guide treatment plans in patients‏ with CAD (Douglas et al. 2015).‏

 

Recently, some exploratory papers have evaluated‏ the feasibility and diagnostic performance of cardiac‏ CT radiomics analysis (Kolossváry et al. 2019; Mannil‏ et al. 2019).‏

 

In the last few years, some authors developed a‏ radiomics-based ML model that proved to be superior‏ to conventional evaluation of CCTA in the assessment‏ of advanced atheromatous plaques. In particular, the‏ model performed better than radiologists in measuring‏ low attenuation areas and average Hounsfield units‏ of the plaque resulting in a more accurate evaluation‏ of high-risk atherosclerotic lesions, facilitating risk‏ stratification of patients (Kolossváry et al. 2019).‏

 

Peri-coronary adipose tissue inflammation is another‏ critical element in the development of atherosclerotic‏ plaque, progression, and rupture. Radiomics features‏ extracted from cardiac CT images have demonstrated‏ potential in evaluating the association between‏ atherosclerotic plaques and perivascular adipose tissue‏ inflammation, fibrosis, and vascularity, more precisely‏ than mean attenuation alone (Oikonomou et al. 2019).‏ These results highlighted that the texture phenotype of‏ adipose tissue might provide a non-invasive approach‏ for identifying microvascular adipose tissue remodelling‏ (Antonopoulos et al. 2017; Oikonomou et al. 2019). In‏ some authors’ opinion, this may represent a gamechanger‏ to distinguish patients with acute myocardial‏ infarction from those with stable CAD or to predict‏ patients with a high risk of major adverse cardiac events‏ (MACE) (Lin et al. 2020; Oikonomou et al. 2018).‏

 

Myocardial tissue characterisation has always been‏ a prerogative of CMR, but with recent technological‏ improvements, even CT scanners can play their part.‏ With the introduction of texture analysis in 2016, CT‏ imaging could discern between healthy and scarred‏ myocardium (Antunes et al. 2016). Soon afterwards, the‏ advent of radiomics analysis increased the capability‏ of cardiac CT in differentiating healthy myocardial‏ tissue from infarcted myocardium. Hinzpeter et al.‏ found that cardiac CT texture analysis was helpful in‏ determining healthy and infarcted myocardial tissue‏ with good reproducibility and accuracy (Hinzpeter et al.‏ 2017). Mannil et al. also demonstrated the capability of‏ radiomics and ML in detecting myocardial infarction on‏ non-contrast CT images acquired for calcium scoring‏ (Mannil et al. 2018).‏

 

Ventricular arrhythmias (VA) represent an essential‏ prognostic factor in patients with cardiovascular‏ diseases. Researchers have also explored the capability‏ of radiomic features to predict recurrent VA in patients‏ with different remodelling patterns sustained by various‏ cardiomyopathies, such as patients with high arrhythmic‏ risk for left ventricular hypertrophy (Esposito et al. 2018;‏ Kay et al. 2020).‏

 

Cardiac CT is also essential for the differential‏ diagnosis of cardiac masses in order to establish‏ optimal treatment strategies. However, differentiation is‏ challenging due to the nonspecific clinical and imaging‏ appearances of many cardiac masses (Poterucha et‏ al. 2019). Nam et al. explored the role of CT radiomic‏ features to differentiate pannus from thrombus and‏ vegetation, showing that the algorithms were superior‏ to radiologists in identifying pannus from non-pannus‏ (Nam et al. 2019). Chun et al. compared the capability‏ of radiomics and CT attenuation values in differentiating‏ left atrial appendage thrombus from circulatory stasis‏ and found that the addition of radiomics features‏ represented an added value to help radiologists to‏ identify thrombus in a single early-phase scan (Chun et‏ al. 2021).‏

 

Despite these exciting applications, they represent‏ only preliminary explorations.‏

 

These findings, do however, indicate that cardiac‏ CT radiomics may become the next tool to detect‏ image biomarkers more precisely, facilitating improved‏ identification of vulnerable patients.

Radiomics in Cardiac MRI

CMR is pivotal in qualitatively and quantitively assessing‏ cardiac structure and function. However, quantitative‏ measures are limited by technical factors and poor‏ discriminatory power due to the overlap of similar‏ appearances of different pathologies. This sometimes‏ makes it challenging to distinguish among similar‏ morphological patterns, such as hypertensive heart‏ disease, hypertrophic cardiomyopathy (HCM) or athletic‏ cardiac remodelling, whose distinctions are critical‏ to guide the clinical assessment, management, and‏ therapy of these patients. Furthermore, CMR likely plays‏ a fundamental role in predicting prognosis in many‏ different clinical settings but its ability is still limited‏ (Moss et al. 2002; Stecker et al. 2006). CMR-based‏ radiomics is emerging as a valid option to help defining‏ image phenotypes and improve diagnosis, prognosis‏ and treatment selection.‏ 

 

‏Several papers have recently explored the feasibility‏ analysis with CMR.

Some authors have evaluated the ability of CMR‏ radiomics analysis applied to non-contrast cine images‏ to accurately differentiate between myocardial disease‏ states and healthy, suggesting that radiomics features‏ may be capable of highlighting myocardium alteration‏ at a tissue level (Baessler et al. 2018). In particular,‏ a recent study evaluated the ability of radiomics to‏ identify distortions in myocardial architecture that are not‏ detectable by the human eye. These signatures could‏ discriminate accurately between the hearts of individuals‏ with hypertension (morphologically normal at CMR) and‏ those who are normotensive (Cetin et al. 2020).‏

 

Furthermore, Baessler et al. demonstrated that‏ radiomic texture analysis applied to T1 and T2 maps‏ was superior to mean T1, mean T2, and Lake Louise‏ diagnostic criteria in discriminating infarct-like acute‏ myocarditis (Baessler et al. 2019). The same group also‏ demonstrated the possibility of accurately discerning‏ patients with myocardial infarction from healthy controls‏ through radiomics and texture analysis on CMR cine‏ images without using an intravenous contrast agent,‏ which could be a fundamental achievement for CMR‏ imaging in terms of time-saving and safety (Baessler et‏ al. 2018).‏

 

In addition, a recently published article stated that‏ radiomics analysis of late gadolinium enhanced‏ (LGE) CMR images is capable of distinguishing acute‏ myocardial infarction from chronic myocardial infarction‏ (Larroza et al. 2017).‏

 

It is important to remember that the assessment of‏ myocardial infarction and myocardial viability are two of‏ the most frequent requests for which clinicians refer to‏ CMR, and these new radiomic tools could be invaluable‏ in offering better clinical support.‏

 

As to prognosis and prediction of clinical outcomes, a‏ few studies have been recently published. In a study on‏ patients with chronic myocardial infarction, Kotu et his‏ group demonstrated that radiomics features extracted‏ from LGE scar were superior to scar size and location‏ in determining the risk of dangerous arrhythmias (Kotu‏ et al. 2015). Similarly, Amano et al. showed that different‏ textural features extracted from LGE images could be‏ useful to predict VA in HCM patients (Amano et al. 2018).‏

 

In summary then, CMR radiomics has the potential‏ to improve myocardial disease classification and‏ prognosis. However, the literature about CMR radiomics‏ is still limited, and further efforts are needed to confirm‏ these preliminary results and to facilitate the radiomics‏ transition from academic to clinical settings.

Conclusion

Through specific, quantitative insights provided at a‏ microstructural level, radiomics can favour a better‏ understanding of the physiologic mechanisms of cardiac‏ disease that can be of great value in developing tailored‏ cardiovascular medicine therapy. And thus, in future,‏ be gradually introduced into the clinical physicians’‏ workflow.‏

 

However, even if most of the recently published papers‏ have shown promising results applying radiomics to‏ cardiac imaging, the current research is still far from‏ supporting clinical decision-making. Radiomics-based‏ cardiac imaging studies have been proved to show an‏ overall insufficient methodological quality (Lambin et‏ al. 2017; Ponsiglione et al. 2022). A more standardised‏ methodology in the radiomics workflow is needed to‏ cross the translational line between an exploratory‏ investigation method and a standardised added value to‏ precision medicine workflows.‏

 

Conflict of Interest

 

None.

 

References:

Amano Y et al (2018) Relationship between Extension or Texture Features of Late Gadolinium Enhancement and Ventricular Tachyarrhythmias in Hypertrophic Cardiomyopathy. Biomed Res. Int.


Antonopoulos AS et al (2017) Detecting human coronary inflammation by imaging perivascular fat. Sci. Transl. Med. 9.


Antunes S et al (2016) Characterization of normal and scarred myocardium based on texture analysis of  cardiac computed tomography images. Annu. Int. Conf. IEEE Eng. Med. Biol.  Soc. IEEE Eng. Med. Biol. Soc. Annu. Int. Conf. 4161–4164 (2016).


Ashrafinia S et al (2021) Radiomics Analysis of Clinical Myocardial Perfusion SPECT to Predict Coronary Artery Calcification. J. Nucl. Med. 59, 512.


Baessler B et al (2018) Texture analysis and machine learning of non-contrast T1-weighted MR images in  patients with hypertrophic cardiomyopathy-Preliminary results. Eur. J. Radiol. 102, 61–67.


Baessler B et al (2018). Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. Radiology 286, 103–112.


Baessler B et al (2019) Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology 292, 608–617.


Cetin I et al (2020) Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Front. Cardiovasc. Med. 7, 640–643.


Chun SH et al (2021) Differentiation of left atrial appendage thrombus from circulatory stasis using  cardiac CT radiomics in patients with valvular heart disease. Eur. Radiol. 31, 1130–1139.


Douglas PS et al (2015) Outcomes of anatomical versus functional testing for coronary artery disease. N. Engl. J. Med. 372, 1291–1300.


Esposito A et al (2018) 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. 20, 816–825.


Gillies RJ et al (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology. 278, 563–577.


Hinzpeter R et al (2017) Texture analysis of acute myocardial infarction with CT: First experience study. PLoS One 12, 1–16.


Hoffmann U et al (2012) Coronary CT angiography versus standard evaluation in acute chest pain. N. Engl. J. Med. 367, 299–308.


Kay FU et al (2020) Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring  Cardiac Computed Tomography Scans: Validation in the DHS. Circ. Cardiovasc. Imaging. 13, e009678.


Kolossváry M et al (2019) Radiomics versus Visual and Histogram-based Assessment to Identify Atheromatous  Lesions at Coronary CT Angiography: An ex Vivo Study. Radiology 293, 89–96.


Kotu LP et al (2015) Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artif. Intell. Med. 64, 205–215.


Kumar V et al (2012) Radiomics: the process and the challenges. Magn. Reson. Imaging.30, 1234–1248.


Lambin P et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762.


Langs G. et al (2018) Machine learning: from radiomics to discovery and routine. Radiologe 58, 1–6.


Larroza A et al (2017) Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. Eur. J. Radiol. 92, 78–83.


Lin A et al (2020) Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc. Imaging 13, 2371–2383.


Mannil M et al (2018) Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible. Invest. Radiol. 53, 338–343.


Mannil M et al (2019) Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction. Eur. J. Radiol. 113, 245–250.


Moss AJ et al (2002) Prophylactic implantation of a defibrillator in patients with myocardial  infarction and reduced ejection fraction. N. Engl. J. Med. 346, 877–883.


Nam K et al (2019) Value of Computed Tomography Radiomic Features for Differentiation of Periprosthetic Mass in Patients With Suspected Prosthetic Valve Obstruction. Circ. Cardiovasc. Imaging. 12, e009496.


Oikonomou EK et al (2018) Non-invasive detection of coronary inflammation using computed tomography and  prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet (London, England) 392, 929–939.


Oikonomo EK et al (2019) A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CTangiography. Eur. Heart J. 40, 3529–3543.


Ponsiglione A et al (2022) Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur. Radiol. 32, 2629–2638.


Poterucha TJ et al (2019) Cardiac Tumors: Clinical Presentation, Diagnosis, and Management. Curr. Treat. Options Oncol. 20, 66.


Raisi-Estabragh Z et al (2020) Cardiac magnetic resonance radiomics: Basic principles and clinical perspectives. Eur. Heart J. Cardiovasc. Imaging 21, 349–356.


Selvanayagam JB (2016) Non-Invasive Cardiac Imaging: Past, Present and Future. Hear. Lung Circ. 25, 755–756.


Sollini M et al (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur. J. Nucl. Med. Mol. Imaging. 46, 2656–2672.


Stecker EC et al (2006) Population-based analysis of sudden cardiac death with and without left  ventricular systolic dysfunction: two-year findings from the Oregon Sudden Unexpected Death Study. J. Am. Coll. Cardiol. 47, 1161–1166.


Virani SS et al (2020) Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation. 141.