Development and validation of a machine learning-based prognostic model for H3K27 mmutant diffuse midline glioma

doi:10.3969/j.issn.1004-583X.2025.11.004

Abstract

Abstract:

Objective To identify prognostic factors for H3K27 mmutant diffuse midline glioma (DMG) and to develop and validate a nomogram for predicting poor prognosis. Methods Patients with histologically confirmed H3K27Mmutant DMG recorded in the Surveillance Epidemiology and End Results database from 2000 to 2019 were retrospectively included. Cases were randomly split into a training set (n=97) and a validation set (n=41) at a 7:3 ratio. Candidate variables were screened by four machinelearning approaches-extreme gradient boosting random forest, least absolute shrinkage and selection operator regression, and decision tree. Multivariate Cox regression models were then used to develop predictive models. Risk factors identified by multiple methods were further tested by multivariate Cox regression to confirm independent prognostic value. A nomogram to predict 6, 12, and 18month overall survival was constructed from the independently significant predictors. Model discrimination, calibration, and clinical utility were evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Survival differences were assessed by Kaplan-Meier analysis. Results The four machinelearning methods produced overlapping but not identical sets of candidate predictors; five variables overlapped across methods: Age, tumor size, WHO grade, laterality, and radiation. Multivariate Cox regression identified four independent adverse prognostic factors: Age >60 years (HR=3.018; 95%CI: 1.15-7.92; P=0.025), larger tumor size (per unit increase) (HR=1.039; 95%CI: 1.01-1.06; P=0.004), higher WHO grade (HR=2.057; 95%CI: 1.21-3.49; P=0.008), and midline location (HR=2.101; 95%CI: 1.32-3.34; P=0.002). Radiotherapy was independently associated with improved survival (protective effect: HR=0.410; 95%CI: 0.23-0.75; P=0.004). The nomogram incorporating these factors demonstrated AUCs for 6, 12, and 18 month OS of 0.647, 0.746, and 0.625 in the training set and 0.632, 0.725, and 0.725 in the validation set, respectively, indicating acceptable discrimination. Calibration plots showed good agreement between predicted and observed survival probabilities, and DCA indicated favorable clinical utility. Conclusion A nomogram developed from machine learning-selected predictors reliably estimates shortterm OS in patients with H3K27 mmutant DMG. This model may help clinicians identify highrisk patients and tailor individualized treatment strategies.

Key words: H3K27M, machine learning, diffuse midline glioma (DMG), nomogram, prognostic factors

CLC Number:

R739.4

Wang Zhuangzhuang, Yang Qingjun, Ren Huan, Liu Yanting, Tian Chunlei. Development and validation of a machine learning-based prognostic model for H3K27 mmutant diffuse midline glioma[J]. Clinical Focus, 2025, 40(11): 988-998.

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URL: https://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2025.11.004

https://www.lchc.cn/EN/Y2025/V40/I11/988

Figures/Tables 10

Tab.1 Comparison of baseline characteristics between the two groups

项目	训练集( $n$ =97)	验证集( $n$ =41)	χ²/ $Z$ 值	$P$ 值
年龄(岁)
<30	58(59.8)	24(58.5)
30~60	33(34.0)	12(29.3)	0.980	0.613
>60	6(6.2)	5(12.2)
性别[例(%)]
女性男性	52(53.6) 45(46.4)	19(46.3) 22(53.7)	0.620	0.431
种族[例(%)]
黑种人	7(7.2)	3(7.3)
白种人	83(85.6)	33(80.5)	0.720	0.698
黄种人	7(7.2)	5(12.2)
肿瘤侧别[例(%)]
左侧	8(8.2)	5(12.2)
右侧	12(12.4)	8(19.5)	1.850	0.396
中线结构	77(79.4)	28(68.3)
肿瘤分期[例(%)]
局部浸润	74(76.30)	28(68.30)
临近侵袭	12(12.40)	5(12.20)	1.930	0.381
远处转移	11(11.30)	8(19.50)
放疗[例(%)]
否是	16(16.5) 81(83.5)	8(19.5) 33(80.5)	0.180	0.674
化疗[例(%)]
否是	43(44.3) 54(55.7)	16(39.0) 25(61.0)	0.350	0.554
WHO分级[例(%)]
低(Ⅰ-Ⅱ) 高(Ⅲ-Ⅳ)	73(75.3) 24(24.7)	28(68.3) 13(31.7)	0.750	0.387
肿瘤数量[例(%)]
单发多发	1(1.0) 96(99.0)	0 41(100.0)	-	1.000
肿瘤体积(cm³)	41(33, 48)	42(30, 50)	-0.120	0.903

Tab.1 Comparison of baseline characteristics between the two groups

项目	训练集( $n$ =97)	验证集( $n$ =41)	χ²/ $Z$ 值	$P$ 值
年龄(岁)
<30	58(59.8)	24(58.5)
30~60	33(34.0)	12(29.3)	0.980	0.613
>60	6(6.2)	5(12.2)
性别[例(%)]
女性男性	52(53.6) 45(46.4)	19(46.3) 22(53.7)	0.620	0.431
种族[例(%)]
黑种人	7(7.2)	3(7.3)
白种人	83(85.6)	33(80.5)	0.720	0.698
黄种人	7(7.2)	5(12.2)
肿瘤侧别[例(%)]
左侧	8(8.2)	5(12.2)
右侧	12(12.4)	8(19.5)	1.850	0.396
中线结构	77(79.4)	28(68.3)
肿瘤分期[例(%)]
局部浸润	74(76.30)	28(68.30)
临近侵袭	12(12.40)	5(12.20)	1.930	0.381
远处转移	11(11.30)	8(19.50)
放疗[例(%)]
否是	16(16.5) 81(83.5)	8(19.5) 33(80.5)	0.180	0.674
化疗[例(%)]
否是	43(44.3) 54(55.7)	16(39.0) 25(61.0)	0.350	0.554
WHO分级[例(%)]
低(Ⅰ-Ⅱ) 高(Ⅲ-Ⅳ)	73(75.3) 24(24.7)	28(68.3) 13(31.7)	0.750	0.387
肿瘤数量[例(%)]
单发多发	1(1.0) 96(99.0)	0 41(100.0)	-	1.000
肿瘤体积(cm³)	41(33, 48)	42(30, 50)	-0.120	0.903

Fig. 1 Variable correlation heat map

Fig.2 Prognostic variables for H3K27M mutant DMG patients screened using four machine learning algorithms a. The importance score of prognostic variables identified by XGBoost model; b. The importance score of the prognostic variables identified by the RF model; c. The importance score of the prognostic impact variables identified by the Lasso regression model; d. The importance score of prognostic variables identified by the DT model

Tab.2 Accuracy evaluation of different algorithm models in training set and validation set

模型	分类	AUC(95% $C I$ )	Brier值	灵敏度	特异度	阳性预测值	阴性预测值	平衡准确性
训练集	Lasso	0.82(0.76~0.87)	0.150	0.780	0.840	0.810	0.820	0.810
	XGBoost	0.91(0.87~0.94)	0.110	0.870	0.820	0.840	0.860	0.850
	RF	0.95(0.92~0.97)	0.080	0.910	0.880	0.890	0.900	0.900
	DT	0.88(0.83~0.92)	0.130	0.830	0.800	0.820	0.810	0.820
验证集	Lasso	0.76(0.68~0.83)	0.180	0.720	0.790	0.750	0.760	0.760
	XGBoost	0.80(0.73~0.86)	0.160	0.760	0.810	0.780	0.790	0.790
	RF	0.78(0.70~0.85)	0.170	0.740	0.770	0.760	0.750	0.760
	DT	0.72(0.64~0.79)	0.190	0.680	0.740	0.710	0.710	0.710

Tab.2 Accuracy evaluation of different algorithm models in training set and validation set

模型	分类	AUC(95% $C I$ )	Brier值	灵敏度	特异度	阳性预测值	阴性预测值	平衡准确性
训练集	Lasso	0.82(0.76~0.87)	0.150	0.780	0.840	0.810	0.820	0.810
	XGBoost	0.91(0.87~0.94)	0.110	0.870	0.820	0.840	0.860	0.850
	RF	0.95(0.92~0.97)	0.080	0.910	0.880	0.890	0.900	0.900
	DT	0.88(0.83~0.92)	0.130	0.830	0.800	0.820	0.810	0.820
验证集	Lasso	0.76(0.68~0.83)	0.180	0.720	0.790	0.750	0.760	0.760
	XGBoost	0.80(0.73~0.86)	0.160	0.760	0.810	0.780	0.790	0.790
	RF	0.78(0.70~0.85)	0.170	0.740	0.770	0.760	0.750	0.760
	DT	0.72(0.64~0.79)	0.190	0.680	0.740	0.710	0.710	0.710

Tab.3 Multivariate Cox regression analysis of prognosis in patients with H3K27M mutant DMG

项目	$H R$	95% $C I$	$P$ 值
年龄
30~60 vs <30	0.630	0.65~2.28	0.382
>60 vs <30	3.018	1.15~7.91	0.025
肿瘤体积	1.039	1.01~1.06	0.004
WHO分级	2.057	1.21~3.49	0.008
肿瘤侧别
右侧vs左侧	0.739	0.61~2.42	0.739
中线vs左侧	2.101	1.32~3.34	0.002
放疗	0.410	0.23~0.75	0.004

Tab.3 Multivariate Cox regression analysis of prognosis in patients with H3K27M mutant DMG

项目	$H R$	95% $C I$	$P$ 值
年龄
30~60 vs <30	0.630	0.65~2.28	0.382
>60 vs <30	3.018	1.15~7.91	0.025
肿瘤体积	1.039	1.01~1.06	0.004
WHO分级	2.057	1.21~3.49	0.008
肿瘤侧别
右侧vs左侧	0.739	0.61~2.42	0.739
中线vs左侧	2.101	1.32~3.34	0.002
放疗	0.410	0.23~0.75	0.004

Fig.3 Prognostic nomogram prediction model of H3K27M mutant DMG patients

Fig.4 ROC curve of 6-, 12-, and 18-month OS in training set and validation set a. Training set; b. Validation set

Fig.5 Calibration calibration curve of 6-, 12-, and 18-month OS in training set and validation set a. Calibration calibration curve of 6-month OS in training set; b. Calibration calibration curve of 12-month OS in training set; c. Calibration calibration curve of the 18-month OS of the training set; d. Calibration calibration curve of 6-month OS in validation set; e. Calibration calibration curve of 12-month OS in validation set; f. Calibration calibration curve for 18-month OS of the validation set

Fig.6 DCA calibration curve of 6-, 12-and 18-month OS of training set and validation set a. Training set; b. Validation set

Fig. 7 Kaplan-Meier survival analysis curves of prognostic factors in H3K27M mutant DMG a. Age; b. Tumor laterality; c. Radiotherapy; d. WHO grade; e. Tumor size

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