Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: Effects of post-reconstruction methods in a dual-center study Journal Article

   

Authors:	Arendt, C. T.; Leithner, D.; Mayerhoefer, M. E.; Gibbs, P.; Czerny, C.; Arnoldner, C.; Burck, I.; Leinung, M.; Tanyildizi, Y.; Lenga, L.; Martin, S. S.; Vogl, T. J.; Schernthaner, R. E.
Article Title:	Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: Effects of post-reconstruction methods in a dual-center study
Abstract:	Objectives: To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. Methods: One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. Results: Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72–0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82–0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79–0.92) was revealed. Conclusion: Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. Key Points: • Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation. © 2020, The Author(s).
Keywords:	retrospective studies; tomography, x-ray computed; otitis media; temporal bone; cholesteatoma
Journal Title:	European Radiology
Volume:	31
Issue:	6
ISSN:	0938-7994
Publisher:	Springer
Date Published:	2021-06-01
Start Page:	4071
End Page:	4078
Language:	English
DOI:	10.1007/s00330-020-07564-4
PUBMED:	33277670
PROVIDER:	scopus
PMCID:	PMC8128805
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097056677&doi=10.1007%2fs00330-020-07564-4&partnerID=40&md5=34988571400712760f1df708c69b1197
Notes:	Article -- Export Date: 1 June 2021 -- Source: Scopus

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