Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting 252Cf neutron therapy response in cervical cancer

doi:10.4329/wjr.123597

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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. Sep 28, 2026; 18(9): 123597
Published online Sep 28, 2026. doi: 10.4329/wjr.123597

Table 4 Comparison of predictive model performance in the validation set

Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	F1 score
Model-C (clinical features)	0.712 (0.548-0.876)	0.700	0.652	0.786	0.731
Model-R (whole-tumor radiomics)	0.783 (0.634-0.932)	0.767	0.739	0.810	0.772
Model-RD (radiomics + deep learning)	0.835 (0.701-0.969)	0.833	0.826	0.845	0.823
Model-Fusion (this study)	0.892 (0.821-0.963)	0.867	0.870	0.857	0.863

AUC: Area under curve.

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Citation: Wang SP, Chen ZG, Shan XJ, Jin M, Zhao W, Tong X, Meng X. Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting ²⁵²Cf neutron therapy response in cervical cancer. World J Radiol 2026; 18(9): 123597
URL: https://www.wjgnet.com/1949-8470/full/v18/i9/123597.htm
DOI: https://dx.doi.org/10.4329/wjr.123597

Wang SP, Chen ZG, Shan XJ, Jin M, Zhao W, Tong X, Meng X. Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting ²⁵²Cf neutron therapy response in cervical cancer. World J Radiol 2026; 18(9): 123597 [DOI: 10.4329/wjr.123597]

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