Skip to main content

Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score

The International Journal of Cardiovascular Imaging volume 36, pages 2039–2050 (2020)Cite this article

Abstract

To explore the superiority of radiomics analysis in the diagnostic performance of coronary computed tomography angiography (CCTA) for identifying myocardial ischaemia and predicting major adverse cardiovascular events (MACE). A total of 105 lesions from 88 patients who underwent CCTA and invasive fractional flow reserve measurement were collected as the training set, and another 31 patients with CCTA and clinical outcome information were used as the validation set. Conventional CCTA features included the stenosis diameter, length, Agatston score and high-risk plaque characteristics. After extracting and selecting radiomics features, the robustness of the radiomics features was examined, and then conventional and radiomics models were established using logistic regressions. The area under the receiver operating characteristic (ROC) curve (AUC) and Net Reclassification Index (NRI) were analysed to compare the discrimination and classification abilities between the two models in both the training and validation sets. A total of 1409 radiomics features were extracted, and three wavelet features were finally screened out. The robustness test showed good stability for the refined radiomics features. Compared with the conventional model, the radiomics model displayed a significantly improved diagnostic performance in the training set (AUC 0.762 vs. 0.631, 95% confidence interval [CI] 0.671–0.853 vs. 0.519–0.742, P = 0.058) but a slightly improved diagnostic performance in the validation set (AUC 0.671 vs. 0.592, 95% CI 0.466–0.875 vs. 0.519–0.742, P = 0.448). The NRI of the radiomics model was increased in both the training and validation sets (NRI 0.198 and 0.238, respectively). Quantitative radiomics analysis was feasible and might help to improve the diagnostic performance of CCTA but is still controversial for predicting MACE.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

AUC:

Area under the curve

CCTA:

Coronary computed tomography angiography

FFR:

Fractional flow reserve

LASSO:

Least absolute shrinkage and selection operator

LR+:

Positive likelihood ratio

LR−:

Negative likelihood ratio

MACE:

Major adverse cardiovascular events

NPV:

Negative predictive value

NRI:

Net Reclassification Index

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

References

  1. Fihn SD, Blankenship JC, Alexander KP et al (2014) 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association task force on practice guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for cardiovascular angiography and interventions, and society of thoracic surgeons. J Am Coll Cardiol 64:1929–1949

    PubMed  Google Scholar 

  2. Authors/Task Force members, Windecker S, Kolh P et al (2014) 2014 ESC/EACTS guidelines on myocardial revascularization: the task force on myocardial revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI). Eur Heart J 35:2541–2619

    Google Scholar 

  3. Shaw LJ, Hausleiter J, Achenbach S et al (2012) Coronary computed tomographic angiography as a gatekeeper to invasive diagnostic and surgical procedures: results from the multicenter CONFIRM (Coronary CT angiography evaluation for clinical outcomes: an international multicenter) registry. J Am Coll Cardiol 60:2103–2114

    PubMed  Google Scholar 

  4. Han D, Torii S, Yahagi K et al (2018) Quantitative measurement of lipid rich plaque by coronary computed tomography angiography: a correlation of histology in sudden cardiac death. Atherosclerosis 275:426–433

    CAS  PubMed  Google Scholar 

  5. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    PubMed  PubMed Central  Google Scholar 

  6. Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269

    PubMed  PubMed Central  Google Scholar 

  7. Song J, Shi J, Dong D et al (2018) A new approach to predict progression-free survival in stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy. Clin Cancer Res 24:3583–3592

    CAS  PubMed  Google Scholar 

  8. Dong D, Tang L, Li ZY et al (2019) Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol 30:431–438

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  11. Kolossvary M, Karady J, Szilveszter B et al (2017) Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circ Cardiovasc Imaging 10:e006843

    PubMed  PubMed Central  Google Scholar 

  12. De Bruyne B, Fearon WF, Pijls NH et al (2014) Fractional flow reserve-guided PCI for stable coronary artery disease. N Engl J Med 371:1208–1217

    PubMed  Google Scholar 

  13. Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd (2018) Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review. J Thorac Imaging 33:88–96

    PubMed  Google Scholar 

  14. Levine GN, Bates ER, Blankenship JC et al (2011) 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention. a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines and the society for cardiovascular angiography and interventions. J Am Coll Cardiol 58:e44–122

    PubMed  Google Scholar 

  15. Abbara S, Blanke P, Maroules CD et al (2016) SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: a report of the society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr 10:435–449

    PubMed  Google Scholar 

  16. Leipsic J, Abbara S, Achenbach S et al (2014) SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr 8:342–358

    PubMed  Google Scholar 

  17. Maurovich-Horvat P, Ferencik M, Voros S, Merkely B, Hoffmann U (2014) Comprehensive plaque assessment by coronary CT angiography. Nat Rev Cardiol 11:390–402

    PubMed  Google Scholar 

  18. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    PubMed  PubMed Central  Google Scholar 

  19. Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481

    PubMed  PubMed Central  Google Scholar 

  20. Duprez DA, Jacobs DR Jr, Lutsey PL et al (2011) Association of small artery elasticity with incident cardiovascular disease in older adults: the multi-ethnic study of atherosclerosis. Am J Epidemiol 174:528–536

    PubMed  PubMed Central  Google Scholar 

  21. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Alba AC, Agoritsas T, Walsh M et al (2017) Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA 318:1377–1384

    PubMed  Google Scholar 

  23. Wolk MJ, Bailey SR, Doherty JU et al (2014) ACCF/AHA/ASE/ASNC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2013 multimodality appropriate use criteria for the detection and risk assessment of stable ischemic heart disease: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, American Heart Association, American Society of Echocardiography, American Society of Nuclear Cardiology, Heart Failure Society of America, Heart Rhythm Society, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, and Society of Thoracic Surgeons. J Am Coll Cardiol 63:380–406

    PubMed  Google Scholar 

  24. Meijboom WB, Van Mieghem CA, van Pelt N et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643

    PubMed  PubMed Central  Google Scholar 

  25. Shreibati JB, Baker LC, Hlatky MA (2011) Association of coronary CT angiography or stress testing with subsequent utilization and spending among medicare beneficiaries. JAMA 306:2128–2136

    CAS  PubMed  Google Scholar 

  26. Danad I, Raijmakers PG, Driessen RS et al (2017) Comparison of coronary CT angiography, SPECT, PET, and hybrid imaging for diagnosis of ischemic heart disease determined by fractional flow reserve. JAMA Cardiol 2:1100–1107

    PubMed  PubMed Central  Google Scholar 

  27. Liu X, Wang Y, Zhang H et al (2019) Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography? Eur Radiol 29:3669–3677

    PubMed  Google Scholar 

  28. Cook CM, Petraco R, Shun-Shin MJ et al (2017) Diagnostic accuracy of computed tomography-derived fractional flow reserve : a systematic review. JAMA Cardiol 2:803–810

    PubMed  Google Scholar 

  29. Seitun S, De Lorenzi C, Cademartiri F et al (2018) CT myocardial perfusion imaging: a new frontier in cardiac imaging. Biomed Res Int 2018:7295460

    PubMed  PubMed Central  Google Scholar 

  30. Zhang L, Chen B, Liu X et al (2018) Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol 11:94–101

    PubMed  Google Scholar 

  31. Wang S, Shi J, Ye Z et al (2019) Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 53:1800986

    PubMed  PubMed Central  Google Scholar 

  32. Peng H, Dong D, Fang MJ et al (2019) Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res 25:4271–4279

    CAS  PubMed  Google Scholar 

  33. Dong D, Zhang F, Zhong LZ et al (2019) Development and validation of a novel MR imaging predictor of response to induction chemotherapy in locoregionally advanced nasopharyngeal cancer: a randomized controlled trial substudy (NCT01245959). BMC Med 17:190

    PubMed  PubMed Central  Google Scholar 

  34. Cui LB, Liu L, Wang HN et al (2018) Disease definition for schizophrenia by functional connectivity using radiomics strategy. Schizophr Bull 44:1053–1059

    PubMed  PubMed Central  Google Scholar 

  35. Qi H, Sun J, Qiao H et al (2018) Carotid intraplaque hemorrhage imaging with quantitative vessel wall T1 mapping: technical development and initial experience. Radiology 287:276–284

    PubMed  Google Scholar 

  36. Chaddad A, Desrosiers C, Hassan L, Tanougast C (2017) Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci 18:52

    PubMed  PubMed Central  Google Scholar 

  37. Kolossvary M, Kellermayer M, Merkely B, Maurovich-Horvat P (2018) Cardiac computed tomography radiomics: a comprehensive review on radiomic techniques. J Thorac Imaging 33:26–34

    PubMed  Google Scholar 

  38. Kolossvary M, Park J, Bang JI et al (2019) Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging 20:1250–1258

    PubMed  PubMed Central  Google Scholar 

  39. Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Google Scholar 

  40. Oikonomou EK, Williams MC, Kotanidis CP et al (2019) A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J 40:3529–3543

    PubMed  PubMed Central  Google Scholar 

  41. Ko BS, Wong DT, Cameron JD et al (2015) The ASLA Score: A CT Angiographic Index to predict functionally significant coronary stenoses in lesions with intermediate severity-diagnostic accuracy. Radiology 276:91–101

    PubMed  Google Scholar 

  42. Diaz-Zamudio M, Dey D, Schuhbaeck A et al (2015) Automated quantitative plaque burden from coronary CT angiography noninvasively predicts hemodynamic significance by using fractional flow reserve in intermediate coronary lesions. Radiology 276:408–415

    PubMed  PubMed Central  Google Scholar 

  43. Rossi A, Papadopoulou SL, Pugliese F et al (2014) Quantitative computed tomographic coronary angiography: does it predict functionally significant coronary stenoses? Circ Cardiovasc Imaging 7:43–51

    PubMed  Google Scholar 

  44. Gaur S, Ovrehus KA, Dey D et al (2016) Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J 37:1220–1227

    PubMed  PubMed Central  Google Scholar 

  45. Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206

    CAS  PubMed  Google Scholar 

  46. Mahmood U, Apte AP, Deasy JO, Schmidtlein CR, Shukla-Dave A (2017) Investigating the robustness neighborhood gray tone difference matrix and gray level co-occurrence matrix radiomic features on clinical Computed tomography systems using anthropomorphic phantoms: evidence from a Multivendor Study. J Comput Assist Tomogr 41:995–1001

    PubMed  PubMed Central  Google Scholar 

  47. Kolossvary M, Szilveszter B, Karady J, Drobni ZD, Merkely B, Maurovich-Horvat P (2018) Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques. J Cardiovasc Comput Tomogr. https://doi.org/10.1016/j.jcct.2018.11.004

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (91939303, 81820108019, 81971776, 81771924, 91959130, 81930053, 81227901), the National Key R&D Program of China (2017YFC1308700, 2017YFA0205200, 2017YFC1309100, 2017YFA0700401), the Beijing Natural Science Foundation (L182061), Translational Medicine Project of Chinese PLA General Hospital (2017TM-003), and the Youth Innovation Promotion Association CAS (2017175).

Author information

Author notes

  1. Wenchao Hu and Xiangjun Wu have contributed equally to this work.

Affiliations

  1. Second Medical Center & National Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China

    Wenchao Hu, Long-Biao Cui, Min Jiang, Jibin Zhang, Yabin Wang, Xinjiang Wang, Lei Gao & Feng Cao

  2. Medical School of Chinese PLA & Department of Cardiology, Chinese PLA General Hospital, Beijing, China

    Wenchao Hu, Min Jiang & Jibin Zhang

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Xiangjun Wu & Di Dong

  4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xiangjun Wu, Di Dong & Jie Tian

  5. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China

    Jie Tian

  6. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, China

    Jie Tian

  7. Department of Clinical Psychology, School of Medical Psychology, PLA Air Force Medical University, Xi’an, China

    Long-Biao Cui

  8. Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China

    Yabin Wang

Authors
  1. Wenchao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangjun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Long-Biao Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jibin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yabin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xinjiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jie Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Feng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jie Tian or Feng Cao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1410 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hu, W., Wu, X., Dong, D. et al. Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score. Int J Cardiovasc Imaging 36, 2039–2050 (2020). https://doi.org/10.1007/s10554-020-01896-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-020-01896-4

Keywords

  • Coronary artery disease
  • CT angiography
  • Radiomics
  • Myocardial ischaemia