Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany.
Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany.
Sci Rep. 2024 Sep 4;14(1):20586. doi: 10.1038/s41598-024-71200-0.
Preoperative identification of intracranial meningiomas with aggressive behaviour may help in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic tool with potential applications in patient risk stratification. In this study, we aimed to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas. We performed a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018. Recurrence within 5 years after Simpson Grade I-III resection was considered as early. Preoperative T1 CE MRI sequences were analysed conventionally by two radiologists. Additionally a semantic feature score based on systematic analysis of morphological characteristics was developed and a radiomic analysis were performed. For the radiomic model, tumour volume was extracted manually, 791 radiomic features were extracted. Eight feature selection algorithms and eight machine learning methods were used. Models were analysed using test and training datasets. In total, 226 patients were included. There were 21% CNS WHO grade 2 tumours, no CNS WHO grade 3 tumour, and 25 (11%) tumour recurrences were detected in total. In ROC analysis the best radiomic models demonstrated superior performance for determination of CNS WHO grade (AUC 0.930) and early recurrence (AUC 0.892) in comparison to the semantic feature score (AUC 0.74 and AUC 0.65) and conventional radiological analysis (AUC 0.65 and 0.54). The combination of human classifiers, semantic score and radiomic analysis did not markedly increase the model performance. Radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.
术前识别具有侵袭性行为的颅内脑膜瘤有助于选择最佳治疗策略。放射组学作为一种强大的诊断工具正在兴起，具有潜在的患者风险分层应用。在这项研究中，我们旨在比较常规、基于语义和放射组学分析的预测价值，以确定颅内脑膜瘤的中枢神经系统（CNS）世界卫生组织（WHO）分级和早期肿瘤复发。我们对 2007 年至 2018 年间手术治疗的颅内脑膜瘤进行了单中心回顾性分析。Simpson 分级 I-III 切除后 5 年内复发被认为是早期复发。对术前 T1CE MRI 序列进行了两位放射科医生的常规分析。此外，还开发了一种基于形态特征系统分析的语义特征评分，并进行了放射组学分析。对于放射组学模型，手动提取肿瘤体积，提取了 791 个放射组学特征。使用了 8 种特征选择算法和 8 种机器学习方法。使用测试和训练数据集对模型进行了分析。共纳入 226 例患者。其中 21%为 CNS WHO 分级 2 级肿瘤，无 CNS WHO 分级 3 级肿瘤，总共有 25 例（11%）肿瘤复发。在 ROC 分析中，与语义特征评分（AUC 0.74 和 AUC 0.65）和常规放射学分析（AUC 0.65 和 0.54）相比，最佳放射组学模型在确定 CNS WHO 分级（AUC 0.930）和早期复发（AUC 0.892）方面表现出更好的性能。人类分类器、语义评分和放射组学分析的组合并没有显著提高模型性能。放射组学分析是术前识别侵袭性和非典型颅内脑膜瘤的有前途的工具，将来可能成为一种有用的工具。
