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

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.

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.

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.

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.