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

Table 2 Features of quantitative parameters in the three habitat subregions

Habitat subregion	K (per minute)	Ve (mL/mL)	ADC (× 10^-3mm²/second)	Proportion of volume (%)	Biological characteristics
H1 (high vascularity/high cellularity)	0.312 ± 0.089	0.487 ± 0.106	0.78 ± 0.15	28.3 ± 11.4	Highly proliferative activity and abundant blood supply
H2 (low vascularity/high cellularity)	0.089 ± 0.031	0.284 ± 0.078	0.69 ± 0.18	41.5 ± 13.7	Hypoxia and high cell density
H3 (low vascularity/low cellularity)	0.062 ± 0.024	0.198 ± 0.065	1.42 ± 0.31	30.2 ± 14.1	Necrosis, hypoxia, or low cell density
P value (H1 vs H2 vs H3)	< 0.001	< 0.001	< 0.001	0.023	-

ADC: Apparent diffusion coefficient.

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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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