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目的:评估3D-酰胺质子转移加权成像（APTw）、表观扩散系数（ADC）直方图分析预测异柠檬酸脱氢酶（ IDH）突变型WHO 2/3级胶质瘤中α地中海贫血伴智力低下综合征X连锁（ ATRX）基因突变的价值。 方法:选择自2017年6月至2023年10月于南方医科大学珠江医院神经外科中心功能神经外科就诊并经手术病理确诊的78例 IDH突变型WHO 2/3级胶质瘤患者为研究对象，其中52例为 ATRX野生型，26例为 ATRX突变型。收集患者术前3D-APTw、ADC图像资料，经后处理后分别采用基于包含瘤周水肿的病灶勾画方式以及基于肿瘤实体的病灶勾画方式对病灶进行分割，之后分别从3D-APTw及ADC图像中提取直方图特征（10百分位数、90百分位数、最大值、平均值、中位数、最小值、偏度、峰度、熵、全距、均匀性和方差）。采用单因素分析比较 ATRX突变型组与 ATRX野生型组患者间各直方图特征的差异，采用多因素Logistic回归分析筛选 ATRX基因突变的独立预测因子并用独立预测因子构建Logistic回归预测模型。采用受试者工作特征（ROC）曲线评估各独立预测因子及Logistic回归预测模型对 ATRX基因突变的预测价值。 结果:（1）基于包含瘤周水肿的病灶勾画方式时，单因素分析示 ATRX突变型组与 ATRX野生型组患者间相对3D-APTw最小值、3D-APTw偏度、相对ADC 90百分位数、相对ADC平均值、相对ADC中位数、ADC峰度、ADC偏度、ADC均匀性及ADC熵的差异均有统计学意义（ P<0.05）。基于肿瘤实体的病灶勾画方式时，单因素分析示 ATRX突变型组与 ATRX野生型组患者间相对3D-APTw 90百分位数、3D-APTw偏度、相对ADC 90百分位数、相对ADC平均值、相对ADC中位数、ADC峰度、ADC偏度、ADC均匀性及ADC熵的差异均有统计学意义（ P<0.05）。（2）基于包含瘤周水肿的病灶勾画方式时，多因素Logistic回归分析示3D-APTw偏度及ADC峰度为 IDH突变型WHO 2/3级胶质瘤患者中 ATRX基因突变的独立预测因子（ OR=0.168，95% CI：0.034~0.800， P=0.025； OR=0.508，95% CI：0.319~0.807， P=0.004），以此2个直方图特征构建的Logistic回归预测模型为 P（Y=1|X）=1/1+e -（1.827-1.785×3D-APTw偏度-0.678×ADC峰度）。基于肿瘤实体的病灶勾画方式时，多因素Logistic回归分析示3D-APTw偏度及ADC峰度为 IDH突变型WHO 2/3级胶质瘤患者中 ATRX基因突变的独立预测因子（ OR=0.164，95% CI：0.034~0.791， P=0.024； OR=0.496，95% CI：0.312~0.788， P=0.003），以此2个直方图特征构建的Logistic回归预测模型为 P（Y=1|X）=1/1+e -(1.585-1.810×3D-APTw偏度-0.702×ADC峰度)。（3）ROC曲线分析显示：基于包括瘤周水肿的病灶勾画方式时，3D-APTw偏度、ADC峰度作为独立预测因子的曲线下面积（AUC）分别为0.725（95% CI：0.608~0.842， P=0.001）、0.794（95% CI：0.685~0.904， P<0.001）；Logistic回归预测模型的AUC为0.836（95% CI：0.729~0.942， P<0.001），当最佳阈值为0.505时，其预测 ATRX基因突变的敏感度为73.10%，特异度为90.40%。基于肿瘤实体的病灶勾画方式时，3D-APTw偏度、ADC峰度作为独立预测因子的AUC分别为0.705（95% CI：0.587~0.823， P=0.003）、0.808（95% CI：0.704~0.913， P<0.001）；Logistic回归预测模型的AUC为0.844（95% CI：0.739~0.949， P<0.001），当最佳阈值为0.399时，其预测 ATRX基因突变的敏感度为84.60%，特异度为80.80%。 结论:3D-APTw及ADC直方图分析能在一定程度上预测 IDH突变型WHO 2/3级胶质瘤中 ATRX基因是否发生突变。

Objective:To evaluate the role of histogram analysis based on amide proton transfer weighted (APTw) and apparent diffusion coefficient (ADC) imaging in predicting alpha-thalassemia/mental retardation syndrome X-linked ( ATRX) mutation in isocitrate dehydrogenase ( IDH)-mutant WHO grading 2/3 gliomas. Methods:Seventy-eight patients with IDH-mutant WHO grading 2/3 gliomas, admitted to and confirmed by surgical pathology in Department of Functional Neurosurgery, Neurosurgery Center, Zhujiang Hospital, Southern Medical University from June 2017 to October 2023, including 52 with ATRX wild and 26 with ATRX mutant-type, were selected. Preoperative 3D-APTw and ADC imaging data were collected; after post-processing, the lesions were segmented using lesion outlining method based on inclusion of peri-tumor edema and lesion outlining method based on tumor entity, respectively; after that, the histogram features (the 10 th percentile, 90 th percentile, maximum, mean, median, minimum, skewness, kurtosis, entropy, range, uniformity, and variance) were extracted from 3D-APTw and ADC imaging, respectively. Univariate Logistic regression was used to compare the differences in histogram features between patients in the ATRX mutant group and ATRX wild-type group, and multivariate Logistic regression was used to screen the independent predictors for ATRX mutation (a Logistic regression prediction model was constructed). Predictive values of independent predictors and Logistic regression prediction models in ATRX mutation were evaluated by receiver operating characteristic (ROC) curve. Results:(1) With lesion outlining method based on inclusion of peri-tumor edema, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw minimum, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity, and ADC entropy ( P<0.05). With lesion outlining method based on tumor entity, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw 90 th percentile, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity and ADC entropy ( P<0.05). (2) With lesion outlining method based on inclusion of peri-tumor edema, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were the independent predictor for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.168, 95% CI: 0.034-0.800, P=0.025; OR=0.508, 95% CI: 0.319-0.807, P=0.004). The constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.827-1.785×3D-APTw skewness-0.678×ADC kurtosis). With lesion outlining method based on tumor entity, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were independent predictors for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.164, 95% CI: 0.034-0.791, P=0.024; OR=0.496, 95% CI: 0.312-0.788, P=0.003); the constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.585-1.810×3D-APTw skewness-0.702×ADC kurtosis). (3) ROC curve analysis showed that, with lesion outlining method based on inclusion of peri-tumor edema, area under ROC curve (AUC) of 3D-APTw skewness and ADC kurtosis was 0.725 (95% CI: 0.608-0.842, P=0.001) and 0.794 (95% CI: 0.685-0.904), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.836 (95% CI: 0.729-0.942, P<0.001), and its sensitivity and specificity were 73.10% and 90.40% when the best threshold was 0.505. ROC curve showed that, with lesion outlining method based on tumor entity, AUC of 3D-APTw skewness and ADC kurtosis was 0.705 (95% CI: 0.587-0.823, P=0.003) and 0.808 (95% CI: 0.704-0.913), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.844 (95% CI: 0.739-0.949, P<0.001), and its sensitivity and specificity were 84.60% and 80.80% when the best threshold was 0.399. Conclusion:Histogram analysis based on 3D-APTw and ADC imaging can predict ATRX mutation in IDH mutant WHO grading 2/3 gliomas to a certain extent.