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Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population

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Journal of Nuclear Cardiology Aims and scope

Abstract

Objective

We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach.

Methods

713 rest 201Thallium/stress 99mTechnetium MPS studies with correlating invasive angiography with 372 revascularization events (275 PCI/97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation, stress combined supine/prone total perfusion deficit (TPD), supine rest and stress TPD, exercise ejection fraction, and end-systolic volume, along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). Tenfold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data.

Results

The sensitivity of machine learning (ML) (73.6% ± 4.3%) for prediction of revascularization was similar to one reader (73.9% ± 4.6%) and standalone measures of perfusion (75.5% ± 4.5%). The specificity of ML (74.7% ± 4.2%) was also better than both expert readers (67.2% ± 4.9% and 66.0% ± 5.0%, P < .05), but was similar to ischemic TPD (68.3% ± 4.9%, P < .05). The receiver operator characteristics areas under curve for ML (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02) but superior to reader 2 (0.72 ± 0.02, P < .01) and standalone measure of perfusion (0.77 ± 0.02, P < .01).

Conclusion

ML approach is comparable or better than experienced readers in prediction of the early revascularization after MPS, and is significantly better than standalone measures of perfusion derived from MPS.

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Acknowledgments

This research was supported in part by Grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institutes of Health (NHLBI/NIH) (PI: Piotr Slomka). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NHLBI. We would like to thank Dr Caroline Kilian and Arpine Oganyan for editing and proofreading the text.

Disclosure

Cedars-Sinai Medical Center receives royalties for the quantitative assessment of function, perfusion, and viability, a portion of which is distributed to some of the authors of this manuscript (DB, GG, PS).

Author information

Authors and Affiliations

  1. Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA

    Reza Arsanjani MD, Damini Dey PhD, Tigran Khachatryan MD, Aryeh Shalev MD, Sean W. Hayes MD, Rine Nakanishi MD, Guido Germano PhD, Daniel S. Berman MD & Piotr Slomka PhD

  2. Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA

    Mathews Fish MD

  3. David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Reza Arsanjani MD, Damini Dey PhD, Guido Germano PhD, Daniel S. Berman MD & Piotr Slomka PhD

  4. Artificial Intelligence in Medicine Program, 8700 Beverly Blvd, Ste A047N, Los Angeles, CA, 90048, USA

    Piotr Slomka PhD

Authors
  1. Reza Arsanjani MD
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  2. Damini Dey PhD
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  3. Tigran Khachatryan MD
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  4. Aryeh Shalev MD
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  5. Sean W. Hayes MD
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  6. Mathews Fish MD
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  7. Rine Nakanishi MD
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  8. Guido Germano PhD
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  9. Daniel S. Berman MD
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  10. Piotr Slomka PhD
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Corresponding author

Correspondence to Piotr Slomka PhD.

Additional information

See related editorial, doi:10.1007/s12350-014-0041-z.

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Arsanjani, R., Dey, D., Khachatryan, T. et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J. Nucl. Cardiol. 22, 877–884 (2015). https://doi.org/10.1007/s12350-014-0027-x

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  • DOI: https://doi.org/10.1007/s12350-014-0027-x

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