Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models

doi:10.4329/wjr.123757

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World Journal of Radiology

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1949-8470

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Aug 28, 2026; 18(8): 123757
Published online Aug 28, 2026. doi: 10.4329/wjr.123757

Table 1 Segmentation reproducibility analysis using dice similarity coefficient

Reproducibility parameter	Minimum DSC	Maximum DSC	mean ± SD
Interobserver reproducibility	0.84	0.93	0.89 ± 0.03
Intraobserver reproducibility	0.87	0.95	0.91 ± 0.02

DSC: Dice similarity coefficient.

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Citation: Sathish S, Nigam H, Gupta R. Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models. World J Radiol 2026; 18(8): 123757
URL: https://www.wjgnet.com/1949-8470/full/v18/i8/123757.htm
DOI: https://dx.doi.org/10.4329/wjr.123757

Sathish S, Nigam H, Gupta R. Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models. World J Radiol 2026; 18(8): 123757 [PMID: 42677009 DOI: 10.4329/wjr.123757]

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