Purpose

Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with l-[methyl-11C] methionine ([¹¹C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system.

Methods

Consecutive 178 preoperative [¹¹C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [¹¹C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment.

Results

The combined [¹¹C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [¹¹C]MET PET/CT radiomics score, patients’ age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726–0.998) in the training dataset and 0.866 (95% CI, 0.777–0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541–1.000) in the training dataset and 0.766 (95% CI, 0.612–0.921) in the validation dataset.

Conclusion

[¹¹C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.

中文翻译：

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用于术前预测弥漫性神经胶质瘤 IDH 突变状态和 WHO 分级的临床放射组学分析的开发和验证：使用两台 PET 扫描仪进行的连续 L-[甲基-11C] 蛋氨酸队列研究

目的

异柠檬酸脱氢酶 (IDH) 基因型的确定对于弥漫性胶质瘤的诊断和预后分层至关重要。我们试图建立和验证放射组学模型和临床特征，并结合列线图，用于术前预测IDH突变状态和弥漫性胶质瘤的 WHO 分级< a i=5>l-[甲基-11C]蛋氨酸 ([¹¹C]MET) PET/CT 成像根据2016年WHO中枢神经系统肿瘤分类。

方法

回顾性研究术前连续[¹¹C]MET PET/CT图像进行放射组学分析。来自 PET 扫描仪 1 的 106 名患者用作训练数据集，来自 PET 扫描仪 2 的 72 名患者用作验证数据集。 [¹¹C]分别提取MET PET和综合CT影像组学特征；分别基于PET特征、CT特征和PET/CT组合特征建立了三个独立的预测模型。采用 SelectKBest 方法、Spearman 相关分析、最小绝对收缩和选择算子 (LASSO) 回归以及机器学习算法进行特征选择和模型构建。筛选出满意的预测模型后，将关键临床特征纳入列线图的建立。

结果

由 4 个 PET 特征和 8 个综合 CT 特征组成的组合 [¹¹C]MET PET/CT 放射组学模型显着与 IDH 基因型相关（p < 0.0001 用于训练和验证数据集）。基于[¹¹C]MET PET/CT放射组学评分、患者年龄和二分肿瘤位置状态的列线图显示出令人满意的辨别能力，并且AUC训练数据集中的值为 0.880（95% CI，0.726–0.998），验证数据集中的值为 0.866（95% CI，0.777–0.956）。在IDH分层WHO分级预测中，最终的放射组学模型由四个PET特征和两个CT特征组成，具有合理且稳定的WHO II级和III级差异疗效IDH-野生型患者中 IV 级患者的患者，训练数据集中的 AUC 为 0.820（95% CI，0.541–1.000），训练数据集中的 AUC 为 0.766（验证数据集中的 95% CI，0.612–0.921）。

结论

[¹¹C]MET PET 放射组学特征可能有益于非侵入性IDH 基因型预测和综合 CT 放射组学特征可以提高疗效。放射组学和临床特征的结合可以为临床应用建立令人满意的列线图。这项基于两台 PET 扫描仪的连续队列的非侵入性预测研究可以提供观察未经治疗的弥漫性胶质瘤中基于放射组学的研究的差异疗效和稳定性的视角。