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Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm

  • Head and Neck
  • Published:
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ABSTRACT

Objective

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm.

Methods

Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set.

Results

Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis.

Conclusions

Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours.

Key Points

• We present and validate a paradigm for texture analysis of DECT scans.

• Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis.

• DECT texture analysis has high accura\cy for diagnosis of benign parotid tumours.

• DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

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Abbreviations

DECT:

Dual-energy CT

NPV :

Negative predictive value

PPV :

Positive predictive value

ROI :

Region of interest

VMI :

Virtual monochromatic image

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Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Jewish General Hospital, Room C-212.1, 3755 Cote Ste-Catherine Road, Montreal, Quebec, H3T 1E2, Canada

    Eiman Al Ajmi, Maryam Bayat & Reza Forghani

  2. Department of Diagnostic Radiology, McGill University, Montreal, Quebec, Canada

    Eiman Al Ajmi, Behzad Forghani, Caroline Reinhold & Reza Forghani

  3. Department of Radiology, Royal Victoria Hospital, McGill University Health Centre, 1001 Decarie Blvd, Montreal, Quebec, H4A 3J1, Canada

    Caroline Reinhold & Reza Forghani

  4. Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada

    Reza Forghani

  5. Department of Otolaryngology-Head and Neck Surgery, Jewish General Hospital, Montreal, Quebec, Canada

    Reza Forghani

Authors
  1. Eiman Al Ajmi
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  2. Behzad Forghani
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  3. Caroline Reinhold
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  4. Maryam Bayat
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  5. Reza Forghani
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Corresponding author

Correspondence to Reza Forghani.

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Guarantor

The scientific guarantor of this publication is R. Forghani.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

R.F. has acted as a consultant for GE Healthcare (DECT/GSI Neuro 510K image review/study for FDA clearance).

R.F. has been featured as a speaker at lunch-and-learn sessions, Applications of Dual-Energy CT in Neuroradiology and Head and Neck Imaging, sponsored by GE Healthcare at the 27th and 28th Annual Meetings of the Eastern Neuroradiological Society (2015, 2016; no personal financial compensation or travel support).

R.F. is a shareholder and previously acted as a consultant for Real Time Medical Inc. (teleradiology company), but the activities of this company have no relation to the topic of this study.

Statistics and biometry

B.F. has significant statistical and informatics expertise and performed the mathematical and statistical analyses.

Ethical approval

Ethics approval was obtained from the institutional review board of the Jewish General Hospital (CIUSSS West-Central Montreal).

Informed consent

Written informed consent was waived by the institutional review board.

Methodology:

• retrospective

• experimental

• performed at one institution.

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Al Ajmi, E., Forghani, B., Reinhold, C. et al. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol 28, 2604–2611 (2018). https://doi.org/10.1007/s00330-017-5214-0

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  • DOI: https://doi.org/10.1007/s00330-017-5214-0

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