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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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 3 Twenty-three features included in the fusion model after LASSO selection

Feature style	Feature name	Subregion/sequence	LASSO coefficient	Predictive performance
Habitat radiomics (8)	H3_GLCM_entropy	H3 (low vascularity/low cellularity)	0.312	Ineffective
	H3_ volume proportion	H3/whole-tumor	0.287	Ineffective
	H1_Ktrans_mean value	H1 (high vascularity/high cellularity)	-0.265	Effective
	H2_ADC_skewness	H2 (low vascularity/high cellularity)	0.243	Ineffective
	H1_GLRLM_LRHGE	H1/ADC	-0.198	Effective
	H2/H1 volume ratio	H1 and H2	0.187	Ineffective
	H1_Ve_kurtosis	H1/DCE-MRI	-0.142	Effective
	H3_GLSZM_LZSAE	H3/T2WI	0.131	Ineffective
Deep learning (7)	DL_Feature_312	ResNet50/T2WI	-0.289	Effective
	DL_Feature_578	ResNet50/ADC	0.251	Ineffective
	DL_Feature_049	ResNet50/Ktrans	-0.234	Effective
	The rest 4 features	Multisequence fusion layers	0.09-0.18	-
Whole-tumor radiomics (5)	Whole-tumor_ADC_mean value	DWI/ADC	-0.198	Effective
	Whole-tumor_shape_ sphericity	T2WI	-0.156	Effective
	The rest 3 features	Multisequence	0.08-0.14	-
Clinical-dosimetrics (3)	FIGO stage	Clinical	0.321	Ineffective
	RBE-weighted total dose of ²⁵²Cf neutrons at A point	Dosimetrics	-0.254	Effective
	Maximum tumor diameter	Clinical/MRI	0.187	Ineffective

DCE: Dynamic contrast enhanced; MRI: Magnetic resonance imaging; ADC: Apparent diffusion coefficient; T2WI: T2-weighted imaging; FIGO: International Federation of Gynecology and Obstetrics; ²⁵²Cf: Californium-252; RBE: Relative biological effectiveness.

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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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