A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features

doi:10.12122/j.issn.1673-4254.2024.10.19

Abstract

Abstract:

Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features. Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone, tumor core, and whole tumor on T1, T2, and T1-weighted contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) sequences. The cases were divided into a training set (271 cases) and a test set (117 cases). Random survival forest algorithms were used to select the radiomics features associated with overall survival (OS) in the training set to construct a radiomic score (Rad-score), based on which the patients were classified into high- and low-risk groups for Kaplan-Meier survival analysis. Cox proportional hazard regression models for the 3 different tumor zones were constructed, and their performance for predicting 1- and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients. The best-performing model was used for constructing a nomogram for survival predictions. Results Five radiomics features from the tumor core, 7 from the peritumoral edema zone, and 5 from the whole tumor were selected. In both the training and test sets, the high- and low-risk groups had significantly different OS (P<0.05), and age, IDH status and Rad-score were independent factors affecting OS. The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set, 0.764 and 0.800 in the test set, and 0.938 and 0.917 in external validation, respectively. Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Key words: glioma, radiomics, magnetic resonance imaging, machine learning, prognostic analysis

Xiaoyin HUANG, Fenglian CHEN, Yu ZHANG, Shujun LIANG. A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features[J]. Journal of Southern Medical University, 2024, 44(10): 2004-2014.

Figures/Tables 14

References 41

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Item	Training dataset (n=271)	Test dataset (n=117)	χ²	P
Age (year, Mean±SD)	60.72±13.40	58.32±13.29	79.249	0.110
Gender [n (%)]			2.399	0.121
Male	158 (58.30)	78 (66.70)
Female	113 (41.70)	39 (33.30)
WHO grade [n (%)]			1.990	0.370
Ⅱ	2 (0.70)	0 (0.00)
Ⅲ	3 (1.10)	3 (2.60)
Ⅳ	266 (98.20)	114 (97.40)
IDH [n (%)]			1.462	0.227
Wide type	251 (92.60)	104 (88.90)
Mutant	20 (7.40)	13 (11.10)
Status [n (%)]			1.421	0.233
Non-censoring	159 (58.70)	61 (52.10)
Censoring	112 (41.30)	76 (47.90)
Survival time (d, Mean±SD)	461.57±414.48	558.85±463.16	329.837	0.312

Item	Training dataset (n=271)	Test dataset (n=117)	χ²	P
Age (year, Mean±SD)	60.72±13.40	58.32±13.29	79.249	0.110
Gender [n (%)]			2.399	0.121
Male	158 (58.30)	78 (66.70)
Female	113 (41.70)	39 (33.30)
WHO grade [n (%)]			1.990	0.370
Ⅱ	2 (0.70)	0 (0.00)
Ⅲ	3 (1.10)	3 (2.60)
Ⅳ	266 (98.20)	114 (97.40)
IDH [n (%)]			1.462	0.227
Wide type	251 (92.60)	104 (88.90)
Mutant	20 (7.40)	13 (11.10)
Status [n (%)]			1.421	0.233
Non-censoring	159 (58.70)	61 (52.10)
Censoring	112 (41.30)	76 (47.90)
Survival time (d, Mean±SD)	461.57±414.48	558.85±463.16	329.837	0.312

No.	Feature name	Sequence	Coefficient
1	wavelet.HHH_glszm_SizeZoneNonUniformityNormalized	T1	-2.886
2	wavelet.HHL_glcm_Imc2	T1	-1.126
3	lbp.3D.m2_firstorder_Maximum	T1	1.154
4	wavelet.HHL_gldm_DependenceNonUniformityNormalized	T2	-1.209
5	original_shape_MajorAxisLength	FLAIR	1.530

No.	Feature name	Sequence	Coefficient
1	wavelet.HHH_glszm_SizeZoneNonUniformityNormalized	T1	-2.886
2	wavelet.HHL_glcm_Imc2	T1	-1.126
3	lbp.3D.m2_firstorder_Maximum	T1	1.154
4	wavelet.HHL_gldm_DependenceNonUniformityNormalized	T2	-1.209
5	original_shape_MajorAxisLength	FLAIR	1.530

No.	Feature name	Sequence	Coefficient
1	original_shape_Maximum2DDiameterSlice	T1	0.988
2	original_shape_Flatness	T1	-0.915
3	wavelet.HHH_gldm_DependenceNonUniformityNormalized	T1CE	0.902
4	wavelet.HHL_glszm_SizeZoneNonUniformity	T1CE	0.526
5	lbp.3D.m2_firstorder_90Percentile	T1CE	0.956
6	lbp.3D.k_firstorder_Minimum	FLAIR	-3.017
7	log.sigma.4.0.mm.3D_glrlm_LongRunLowGrayLevelEmphasis	FLAIR	2.411