Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm
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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.
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.
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.
Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours.
• 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.
KeywordsMultidetector computed tomography Radiography dual-energy scanned projection Computer-assisted diagnosis Artificial intelligence Head and neck neoplasms
Negative predictive value
Positive predictive value
Region of interest
Virtual monochromatic image
The authors state that this work has not received any funding.
Compliance with ethical standards:
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.
Ethics approval was obtained from the institutional review board of the Jewish General Hospital (CIUSSS West-Central Montreal).
Written informed consent was waived by the institutional review board.
• performed at one institution.
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