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"Automated 2-dimensional measurement of vestibular schwannoma: validity and accuracy of an artificial intelligence algorithm"
Olaf M. Neve, Stephan R. Romeijn, Yunjie Chen, Larissa Nagtegaal, Willem Grootjans, Jeroen C. Jansen, Marius Staring, Berit M. Verbist and Erik F. Hensen
Abstract
Objective. Validation of automated 2-dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI).
Study Design.Retrospective validation study using 2 data sets containing MRIs of vestibular schwannoma patients.
Setting. University Hospital in The Netherlands.
Methods.Two data sets were used, 1 containing 1 scan per patient (n = 134) and the other containing at least 3 consecutive MRIs of 51 patients, all with contrast-enhanced T1 or high-resolution T2 sequences. 2D measurements of the maximal extrameatal diameters in the axial plane were automatically derived from a 3D-convolutional neural network compared to manual measurements by 2 human observers. Intra- and interobserver variabilities were calculated using the intraclass correlation coefficient (ICC), agreement on tumor progression using Cohen's kappa.
Results. The human intra- and interobserver variability showed a high correlation (ICC: 0.98-0.99) and limits of agreement of 1.7 to 2.1 mm. Comparing the automated to human measurements resulted in ICC of 0.98 (95% confidence interval [CI]: 0.974; 0.987) and 0.97 (95% CI: 0.968; 0.984), with limits of agreement of 2.2 and 2.1 mm for diameters parallel and perpendicular to the posterior side of the temporal bone, respectively. There was satisfactory agreement on tumor progression between automated measurements and human observers (Cohen's κ = 0.77), better than the agreement between the human observers (Cohen's κ = 0.74).
Conclusion. Automated 2D diameter measurements and growth detection of vestibular schwannomas are at least as accurate as human 2D measurements. In clinical practice, measurements of the maximal extrameatal tumor (2D) diameters of vestibular schwannomas provide important complementary information to total tumor volume (3D) measurements. Combining both in an automated measurement algorithm facilitates clinical adoption.
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Copyright © 2023 by the authors.
Published version © 2023 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Neve:2023, |
| author |
= {Neve, Olaf M. and Romeijn, Stephan R. and Chen, Yunjie and Nagtegaal, Larissa and Grootjans, Willem and Jansen, Jeroen C. and Staring, Marius and Verbist, Berit M. and Hensen, Erik F.}, |
| title |
= {Automated 2-dimensional measurement of vestibular schwannoma: validity and accuracy of an artificial intelligence algorithm}, |
| journal |
= {Otolaryngology - Head and Neck Surgery}, |
| volume |
= {169}, |
| number |
= {6}, |
| pages |
= {1582 - 1589}, |
| month |
= {December}, |
| year |
= {2023}, |
| } |
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| last modified: 22-11-2023 |
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