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 1 Comparison of baseline characteristics of patients in the training set and the validation set

Characteristic	Training set (n = 70)	Validation set (n = 30)	Statistic	P value
Age (years)	52.3 ± 9.7	51.8 ± 10.2	t = 0.245	0.807
FIGO stage (2018)
IB stage	12 (17.1)	5 (16.7)
IIA-IIB stage	28 (40.0)	13 (43.3)	χ² = 0.243	0.886
IIIA-IIIB stage	21 (30.0)	9 (30.0)
IVA stage	9 (12.9)	3 (10.0)
Pathological types
Squamous cell carcinoma	61 (87.1)	26 (86.7)	χ² = 0.003	0.954
Adenocarcinoma	9 (12.9)	4 (13.3)
Maximum tumor diameter (cm)	4.8 ± 1.4	4.6 ± 1.3	t = 0.688	0.493
SCC-Ag (ng/mL; median)	6.2 (2.1-18.4)	5.9 (1.8-20.1)	Z = 0.312	0.755
RBE-weighted total dose of ²⁵²Cf neutrons at A point (Gy)	34.2 ± 3.6	33.8 ± 3.9	t = 0.514	0.608
Overall dose of brachytherapy (Gy)	47.8 ± 1.9	48.1 ± 2.0	t = 0.727	0.469
Response to the treatment (efficacy rate)	56 (80.0)	23 (76.7)	χ² = 0.181	0.671
Complete response	32 (45.7)	14 (46.7)
Partial response	24 (34.3)	9 (30.0)
Stable disease	10 (14.3)	5 (16.7)
Progressive disease	4 (5.7)	2 (6.7)

Measurement data are expressed as the mean ± SD. Enumeration data are expressed as n (%). FIGO: International Federation of Gynecology and Obstetrics; RBE: Relative biological effectiveness; ²⁵²Cf: Californium-252; SCC-Ag: Squamous-cell carcinoma antigen.

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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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