Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian She Dong Road 1, Zhengzhou, 450052, Henan, China.
Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Eur Radiol. 2023 May;33(5):3455-3466. doi: 10.1007/s00330-023-09459-6. Epub 2023 Feb 28.
OBJECTIVES: To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. METHODS: We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. RESULTS: We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. CONCLUSIONS: Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. KEY POINTS: • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.
目的：探讨基于动态磁敏感对比灌注加权成像（DSC-PWI）的放射组学特征是否可以提高成人弥漫性胶质瘤分子亚型的预测能力，并进一步开发和验证一种结合常规和灌注 MRI 放射组学特征的多模态放射组学模型。 方法：我们分别从常规 MRI 和 DSC-PWI 的每个序列中提取了 1197 个放射组学特征。使用 Boruta 算法进行特征选择和组合，并应用三分类随机森林方法构建模型。我们还通过整合放射组学特征和临床指标构建了一个联合模型。通过验证集中的 AUC 值比较模型对鉴别分子亚型（异柠檬酸脱氢酶野生型 [IDHwt]、异柠檬酸脱氢酶突变型和 1p/19q 未缺失 [IDHmut-noncodel]、异柠檬酸脱氢酶突变型和 1p/19q 缺失 [IDHmut-codel]）的诊断性能。 结果：共纳入 272 例（训练集 n=166，验证集 n=106）Ⅱ-Ⅳ级胶质瘤患者（平均年龄 48.7 岁，范围 19-77 岁）。分子亚型的比例分别为 66.2% IDHwt、15.1% IDHmut-noncodel 和 18.8% IDHmut-codel。19 个放射组学特征（13 个来自常规 MRI，6 个来自 DSC-PWI）被选择来构建多模态放射组学模型。在验证集中，与常规放射组学模型相比，多模态放射组学模型在预测 IDHwt 和 IDHmut-codel 亚型方面表现更好，在预测 IDHmut-noncodel 亚型方面与常规放射组学模型相当。多模态放射组学模型在预测三种分子亚型方面的性能与联合模型相似。 结论：在弥漫性胶质瘤患者中，将 DSC-PWI 与常规 MRI 相结合可以提高分子亚型预测能力。 要点：• 多模态放射组学模型在预测 IDH 野生型和 IDH 突变型和 1p/19q 缺失型胶质瘤方面优于常规 MRI。• 多模态放射组学模型在预测三种分子亚型方面的性能与联合模型相当。• T1 加权钆增强和相对脑血容量图像的放射组学特征在预测分子亚型方面发挥了重要作用。
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