Cardiac Computed Tomography Radiomics: Comparison
Please note this is a comparison between Version 2 by Conner Chen and Version 1 by Kevin Cheng.

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

  • machine learning
  • radiomics
  • coronary computed tomography angiography
  • pericoronary adipose tissue
  • acute coronary syndrome
  • atherosclerosis
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References

  1. Roth, G.A.; Abate, D.; Abate, K.H.; Abay, S.M.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; Abdela, J.; Abdelalim, A. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1736–1788.
  2. Libby, P.; Ridker, P.M.; Maseri, A. Inflammation and atherosclerosis. Circulation 2002, 105, 1135–1143.
  3. Ridker, P.M. How common is residual inflammatory risk? Circ. Res. 2017, 120, 617–619.
  4. Ridker, P.M.; Everett, B.M.; Thuren, T.; MacFadyen, J.G.; Chang, W.H.; Ballantyne, C.; Fonseca, F.; Nicolau, J.; Koenig, W.; Anker, S.D. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 2017, 377, 1119–1131.
  5. Lee, R.; Margaritis, M.; M Channon, K.; Antoniades, C. Evaluating oxidative stress in human cardiovascular disease: Methodological aspects and considerations. Curr. Med. Chem. 2012, 19, 2504–2520.
  6. Camici, P.G.; Rimoldi, O.E.; Gaemperli, O.; Libby, P. Non-invasive anatomic and functional imaging of vascular inflammation and unstable plaque. Eur. Heart J. 2012, 33, 1309–1317.
  7. Moss, A.J.; Williams, M.C.; Newby, D.E.; Nicol, E.D. The updated NICE guidelines: Cardiac CT as the first-line test for coronary artery disease. Curr. Cardiovasc. Imaging Rep. 2017, 10, 15.
  8. Goff, D.C.; Lloyd-Jones, D.M.; Bennett, G.; Coady, S.; D’agostino, R.B.; Gibbons, R.; Greenland, P.; Lackland, D.T.; Levy, D.; O’donnell, C.J. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 2014, 63, 2935–2959.
  9. Members, T.F.; Montalescot, G.; Sechtem, U.; Achenbach, S.; Andreotti, F.; Arden, C.; Budaj, A.; Bugiardini, R.; Crea, F.; Cuisset, T. 2013 ESC guidelines on the management of stable coronary artery disease: The Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur. Heart J. 2013, 34, 2949–3003.
  10. Maurovich-Horvat, P.; Ferencik, M.; Voros, S.; Merkely, B.; Hoffmann, U. Comprehensive plaque assessment by coronary CT angiography. Nat. Rev. Cardiol. 2014, 11, 390–402.
  11. De Mauro, A.; Greco, M.; Grimaldi, M. A formal definition of Big Data based on its essential features. Libr. Rev. 2016, 65, 122–135.
  12. Russell, S.; Norvig, P. Artificial Intelligence: A Modern Approach; Prentice Hall Press: Hoboken, NJ, USA, 2009.
  13. Lin, A.; Kolossváry, M.; Išgum, I.; Maurovich-Horvat, P.; Slomka, P.J.; Dey, D. Artificial intelligence: Improving the efficiency of cardiovascular imaging. Exp. Rev. Med. Dev. 2020, 17, 565–577.
  14. Lin, A.; Kolossváry, M.; Motwani, M.; Išgum, I.; Maurovich-Horvat, P.; Slomka, P.J.; Dey, D. Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease. Radiol. Cardiothorac. Imaging 2021, 3, e200512.
  15. Oikonomou, E.K.; Marwan, M.; Desai, M.Y.; Mancio, J.; Alashi, A.; Centeno, E.H.; Thomas, S.; Herdman, L.; Kotanidis, C.P.; Thomas, K.E. 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 2018, 392, 929–939.
  16. Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260.
  17. Deo, R.C. Machine learning in medicine. Circulation 2015, 132, 1920–1930.
  18. Dey, D.; Slomka, P.J.; Leeson, P.; Comaniciu, D.; Shrestha, S.; Sengupta, P.P.; Marwick, T.H. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J. Am. Coll. Cardiol. 2019, 73, 1317–1335.
  19. Knuuti, J.; Wijns, W.; Saraste, A.; Capodanno, D.; Barbato, E.; Funck-Brentano, C.; Prescott, E.; Storey, R.F.; Deaton, C.; Cuisset, T.; et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur. Heart J. 2020, 41, 407–477.
  20. Fihn, S.D.; Gardin, J.M.; Abrams, J.; Berra, K.; Blankenship, J.C.; Dallas, A.P.; Douglas, P.S.; Foody, J.M.; Gerber, T.C.; Hinderliter, A.L. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: A report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J. Am. Coll. Cardiol. 2012, 60, e44–e164.
  21. Yang, L.; Zhou, T.; Zhang, R.; Xu, L.; Peng, Z.; Ding, J.; Wang, S.; Li, M.; Sun, G. Meta-analysis: Diagnostic accuracy of coronary CT angiography with prospective ECG gating based on step-and-shoot, Flash and volume modes for detection of coronary artery disease. Eur. Radiol. 2014, 24, 2345–2352.
  22. Marwick, T.H.; Cho, I.; ó Hartaigh, B.; Min, J.K. Finding the gatekeeper to the cardiac catheterization laboratory: Coronary CT angiography or stress testing? J. Am. Coll. Cardiol. 2015, 65, 2747–2756.
  23. Arbab-Zadeh, A.; Miller, J.M.; Rochitte, C.E.; Dewey, M.; Niinuma, H.; Gottlieb, I.; Paul, N.; Clouse, M.E.; Shapiro, E.P.; Hoe, J. Diagnostic accuracy of computed tomography coronary angiography according to pre-test probability of coronary artery disease and severity of coronary arterial calcification: The CORE-64 (Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomography Angiography) international multicenter study. J. Am. Coll. Cardiol. 2012, 59, 379–387.
  24. Maroules, C.D.; Hamilton-Craig, C.; Branch, K.; Lee, J.; Cury, R.C.; Maurovich-Horvat, P.; Rubinshtein, R.; Thomas, D.; Williams, M.; Guo, Y. Coronary artery disease reporting and data system (CAD-RADSTM): Inter-observer agreement for assessment categories and modifiers. J. Cardiovas. Comput. Tomogr. 2018, 12, 125–130.
  25. Nakanishi, R.; Motoyama, S.; Leipsic, J.; Budoff, M.J. How accurate is atherosclerosis imaging by coronary computed tomography angiography? J. Cardiovas. Comput. Tomogr. 2019, 13, 254–260.
  26. Obaid, D.R.; Calvert, P.A.; Gopalan, D.; Parker, R.A.; Hoole, S.P.; West, N.E.; Goddard, M.; Rudd, J.H.; Bennett, M.R. Atherosclerotic Plaque Composition and Classification Identified by Coronary Computed Tomography: Assessment of Computed Tomography–Generated Plaque Maps Compared With Virtual Histology Intravascular Ultrasound and Histology. Circ. Cardiovasc. Imaging 2013, 6, 655–664.
  27. Kolossváry, M.; Kellermayer, M.; Merkely, B.; Maurovich-Horvat, P. Cardiac computed tomography radiomics. J. Thorac. Imaging 2018, 33, 26–34.
  28. O’Connor, J.P.; Rose, C.J.; Waterton, J.C.; Carano, R.A.; Parker, G.J.; Jackson, A. Imaging intratumor heterogeneity: Role in therapy response, resistance, and clinical outcome. Clin. Cancer Res. 2015, 21, 249–257.
  29. Yu, J.; Shi, Z.; Lian, Y.; Li, Z.; Liu, T.; Gao, Y.; Wang, Y.; Chen, L.; Mao, Y. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur. Radiol. 2017, 27, 3509–3522.
  30. Bickelhaupt, S.; Paech, D.; Kickingereder, P.; Steudle, F.; Lederer, W.; Daniel, H.; Götz, M.; Gählert, N.; Tichy, D.; Wiesenfarth, M. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J. Magn. Reson. Imaging 2017, 46, 604–616.
  31. Zhang, X.; Xu, X.; Tian, Q.; Li, B.; Wu, Y.; Yang, Z.; Liang, Z.; Liu, Y.; Cui, G.; Lu, H. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J. Magn. Reson. Imaging 2017, 46, 1281–1288.
  32. Coroller, T.P.; Agrawal, V.; Huynh, E.; Narayan, V.; Lee, S.W.; Mak, R.H.; Aerts, H.J. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J. Thorac. Oncol. 2017, 12, 467–476.
  33. Kickingereder, P.; Götz, M.; Muschelli, J.; Wick, A.; Neuberger, U.; Shinohara, R.T.; Sill, M.; Nowosielski, M.; Schlemmer, H.-P.; Radbruch, A. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin. Cancer Res. 2016, 22, 5765–5771.
  34. Li, H.; Zhu, Y.; Burnside, E.S.; Drukker, K.; Hoadley, K.A.; Fan, C.; Conzen, S.D.; Whitman, G.J.; Sutton, E.J.; Net, J.M. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016, 281, 382–391.
  35. Huang, Y.; Liu, Z.; He, L.; Chen, X.; Pan, D.; Ma, Z.; Liang, C.; Tian, J.; Liang, C. Radiomics signature: A potential biomarker for the prediction of disease-free survival in early-stage (I or II) Non—Small cell lung cancer. Radiology 2016, 281, 947–957.
  36. Prasanna, P.; Patel, J.; Partovi, S.; Madabhushi, A.; Tiwari, P. Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur. Radiol. 2017, 27, 4188–4197.
  37. Kolossváry, M.; Karády, J.; Szilveszter, B.; Kitslaar, P.; Hoffmann, U.; Merkely, B.; Maurovich-Horvat, P. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circ. Cardiovasc. Imaging 2017, 10, e006843.
  38. Oikonomou, E.K.; Williams, M.C.; Kotanidis, C.P.; Desai, M.Y.; Marwan, M.; Antonopoulos, A.S.; Thomas, K.E.; Thomas, S.; Akoumianakis, I.; Fan, L.M. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 2019, 40, 3529–3543.
  39. Lin, A.; Kolossváry, M.; Yuvaraj, J.; Cadet, S.; McElhinney, P.A.; Jiang, C.; Nerlekar, N.; Nicholls, S.J.; Slomka, P.J.; Maurovich-Horvat, P. Myocardial infarction associates with a distinct pericoronary adipose tissue radiomic phenotype: A prospective case-control study. JACC Cardiovasc. Imaging 2020, 13, 2371–2383.
  40. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention- MICCAI 2015, Munich, Germany, 5–9 October; pp. 234–241.
  41. Zwanenburg, A.; Leger, S.; Vallières, M.; Löck, S. Image biomarker standardisation initiative-feature definitions. arXiv 2016, arXiv:1612.07003.
  42. Ergen, B.; Baykara, M. Texture based feature extraction methods for content based medical image retrieval systems. Bio-Med. Mater. Eng. 2014, 24, 3055–3062.
  43. Haralick, R.M.; Shanmugam, K.; Dinstein, I.H. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 6, 610–621.
  44. Galloway, M.M. Texture analysis using gray level run lengths. Comput. Graph. Image Process. 1975, 4, 172–179.
  45. Guo, X.; Liu, X.; Wang, H.; Liang, Z.; Wu, W.; He, Q.; Li, K.; Wang, W. Enhanced CT images by the wavelet transform improving diagnostic accuracy of chest nodules. J. Digit. Imaging 2011, 24, 44–49.
  46. Guo, Q.; Wu, W.; Massart, D.; Boucon, C.; De Jong, S. Feature selection in principal component analysis of analytical data. Chemom. Intell. Lab. Syst. 2002, 61, 123–132.
  47. Rizzo, S.; Botta, F.; Raimondi, S.; Origgi, D.; Fanciullo, C.; Morganti, A.G.; Bellomi, M. Radiomics: The facts and the challenges of image analysis. Eur. Radiol. Exp. 2018, 2, 1–8.
  48. Fuster, V.; Moreno, P.R.; Fayad, Z.A.; Corti, R.; Badimon, J.J. Atherothrombosis and high-risk plaque: Part I: Evolving concepts. J. Am. Coll. Cardiol. 2005, 46, 937–954.
  49. Koskinas, K.C.; Ughi, G.J.; Windecker, S.; Tearney, G.J.; Räber, L. Intracoronary imaging of coronary atherosclerosis: Validation for diagnosis, prognosis and treatment. Eur. Heart J. 2016, 37, 524–535.
  50. Tearney, G.J.; Regar, E.; Akasaka, T.; Adriaenssens, T.; Barlis, P.; Bezerra, H.G.; Bouma, B.; Bruining, N.; Cho, J.-M.; Chowdhary, S. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: A report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation. J. Am. Coll. Cardiol. 2012, 59, 1058–1072.
  51. Lee, J.M.; Bang, J.-I.; Koo, B.-K.; Hwang, D.; Park, J.; Zhang, J.; Yaliang, T.; Suh, M.; Paeng, J.C.; Shiono, Y. Clinical relevance of 18F-sodium fluoride positron-emission tomography in noninvasive identification of high-risk plaque in patients with coronary artery disease. Circ. Cardiovasc. Imaging 2017, 10, e006704.
  52. Grootaert, M.O.; Moulis, M.; Roth, L.; Martinet, W.; Vindis, C.; Bennett, M.R.; De Meyer, G.R. Vascular smooth muscle cell death, autophagy and senescence in atherosclerosis. Cardiovasc. Res. 2018, 114, 622–634.
  53. Kolossváry, M.; Karády, J.; Kikuchi, Y.; Ivanov, A.; Schlett, C.L.; Lu, M.T.; Foldyna, B.; Merkely, B.; Aerts, H.J.; Hoffmann, U. Radiomics versus visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: An ex vivo study. Radiology 2019, 293, 89–96.
  54. Joshi, N.V.; Vesey, A.T.; Williams, M.C.; Shah, A.S.; Calvert, P.A.; Craighead, F.H.; Yeoh, S.E.; Wallace, W.; Salter, D.; Fletcher, A.M. 18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: A prospective clinical trial. Lancet 2014, 383, 705–713.
  55. Aikawa, E.; Nahrendorf, M.; Figueiredo, J.-L.; Swirski, F.K.; Shtatland, T.; Kohler, R.H.; Jaffer, F.A.; Aikawa, M.; Weissleder, R. Osteogenesis Associates With Inflammation in Early-Stage Atherosclerosis Evaluated by Molecular Imaging In Vivo. Circulation 2007, 116, 2841–2850.
  56. Antonopoulos, A.S.; Sanna, F.; Sabharwal, N.; Thomas, S.; Oikonomou, E.K.; Herdman, L.; Margaritis, M.; Shirodaria, C.; Kampoli, A.-M.; Akoumianakis, I. Detecting human coronary inflammation by imaging perivascular fat. Sci. Transl. Med. 2017, 9, eaal2658.
  57. Kolossvary, M.; Park, J.; Bang, J.I.; Zhang, J.; Lee, J.M.; Paeng, J.C.; Merkely, B.; Narula, J.; Kubo, T.; Akasaka, T.; et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. Eur. Heart J. Cardiovasc. Imaging 2019, 20, 1250–1258.
  58. Bäck, M.; Yurdagul, A.; Tabas, I.; Öörni, K.; Kovanen, P.T. Inflammation and its resolution in atherosclerosis: Mediators and therapeutic opportunities. Nat. Rev. Cardiol. 2019, 16, 389–406.
  59. Hsue, P.Y.; Waters, D.D. HIV infection and coronary heart disease: Mechanisms and management. Nat. Rev. Cardiol. 2019, 16, 745–759.
  60. Kim, S.T.; Park, T. Acute and chronic effects of cocaine on cardiovascular health. Int. J. Mol. Sci. 2019, 20, 584.
  61. Kolossváry, M.; Gerstenblith, G.; Bluemke, D.A.; Fishman, E.K.; Mandler, R.N.; Kickler, T.S.; Chen, S.; Bhatia, S.; Lai, S.; Lai, H. Contribution of Risk Factors to the Development of Coronary Atherosclerosis as Confirmed via Coronary CT Angiography: A Longitudinal Radiomics-based Study. Radiology 2021, 299, 203179.
  62. Lin, A.; Dey, D.; Wong, D.T.; Nerlekar, N. Perivascular adipose tissue and coronary atherosclerosis: From biology to imaging phenotyping. Curr. Atheroscler. Rep. 2019, 21, 1–12.
  63. Ding, X.; Terzopoulos, D.; Diaz-Zamudio, M.; Berman, D.S.; Slomka, P.J.; Dey, D. Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT. Med. Phys. 2015, 42, 5015–5026.
  64. Mihl, C.; Loeffen, D.; Versteylen, M.O.; Takx, R.A.; Nelemans, P.J.; Nijssen, E.C.; Vega-Higuera, F.; Wildberger, J.E.; Das, M. Automated quantification of epicardial adipose tissue (EAT) in coronary CT angiography; comparison with manual assessment and correlation with coronary artery disease. J. Cardiovas. Comput. Tomogr. 2014, 8, 215–221.
  65. Baba, S.; Jacene, H.A.; Engles, J.M.; Honda, H.; Wahl, R.L. CT Hounsfield units of brown adipose tissue increase with activation: Preclinical and clinical studies. J. Nucl. Med. 2010, 51, 246–250.
  66. Goeller, M.; Tamarappoo, B.K.; Kwan, A.C.; Cadet, S.; Commandeur, F.; Razipour, A.; Slomka, P.J.; Gransar, H.; Chen, X.; Otaki, Y. Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography. Eur. Heart J. Cardiovasc. Imaging 2019, 20, 636–643.
  67. Lin, A.; Nerlekar, N.; Yuvaraj, J.; Fernandes, K.; Jiang, C.; Nicholls, S.J.; Dey, D.; Wong, D.T. Pericoronary adipose tissue computed tomography attenuation distinguishes different stages of coronary artery disease: A cross-sectional study. Eur. Heart J. Cardiovasc. Imaging 2021, 22, 298–306.
  68. The, S. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): An open-label, parallel-group, multicentre trial. Lancet 2015, 385, 2383–2391.
  69. Van Rosendael, A.R.; Maliakal, G.; Kolli, K.K.; Beecy, A.; Al’Aref, S.J.; Dwivedi, A.; Singh, G.; Panday, M.; Kumar, A.; Ma, X. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J. Cardiovas. Comput. Tomogr. 2018, 12, 204–209.
  70. Cortes-Rodicio, J.; Sanchez-Merino, G.; Garcia-Fidalgo, M.; Tobalina-Larrea, I. Identification of low variability textural features for heterogeneity quantification of 18F-FDG PET/CT imaging. Rev. Esp. Med. Nucl. Imagen Mol. Engl. Ed. 2016, 35, 379–384.
  71. Hu, P.; Wang, J.; Zhong, H.; Zhou, Z.; Shen, L.; Hu, W.; Zhang, Z. Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget 2016, 7, 71440.
  72. Shiri, I.; Rahmim, A.; Ghaffarian, P.; Geramifar, P.; Abdollahi, H.; Bitarafan-Rajabi, A. The impact of image reconstruction settings on 18F-FDG PET radiomic features: Multi-scanner phantom and patient studies. Eur. Radiol. 2017, 27, 4498–4509.
  73. Altazi, B.A.; Zhang, G.G.; Fernandez, D.C.; Montejo, M.E.; Hunt, D.; Werner, J.; Biagioli, M.C.; Moros, E.G. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms. J. Appl. Clin. Med. Phys. 2017, 18, 32–48.
  74. Mackin, D.; Fave, X.; Zhang, L.; Fried, D.; Yang, J.; Taylor, B.; Rodriguez-Rivera, E.; Dodge, C.; Jones, A.K.; Court, L. Measuring CT scanner variability of radiomics features. Investig. Radiol. 2015, 50, 757.
  75. Lambin, P.; Leijenaar, R.T.; Deist, T.M.; Peerlings, J.; De Jong, E.E.; Van Timmeren, J.; Sanduleanu, S.; Larue, R.T.; Even, A.J.; Jochems, A. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749–762.
  76. Collins, G.S.; Reitsma, J.B.; Altman, D.G.; Moons, K.G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement. Circulation 2015, 131, 211–219.
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