Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
Eur Radiol. 2019 Mar;29(3):1625-1634. doi: 10.1007/s00330-018-5725-3. Epub 2018 Sep 25.
To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging.
A total of 194 patients with Knosp grade two and three PAs (training set: n = 97; test set: n = 97) were enrolled in this retrospective study. From CE-T1 and T2 MR images, 2553 quantitative imaging features were extracted. To select the most informative features, least absolute shrinkage and selection operator (LASSO) was performed. Subsequently, a linear support vector machine (SVM) was used to fit the predictive model. Furthermore, a nomogram was constructed by incorporating clinico-radiological risk factors and radiomics signature, and the clinical usefulness of the nomogram was validated using decision curve analysis (DCA).
Three imaging features were selected in the training set, based on which the radiomics model yielded area under the curve (AUC) values of 0.852 and 0.826 for the training and test sets. The nomogram based on the radiomics signature and the clinico-radiological risk factors yielded an AUC of 0.899 in the training set and 0.871 in the test set.
The nomogram developed in this study might aid neurosurgeons in the pre-operative prediction of CS invasion by Knosp grade two and three PAs, which might contribute to creating surgical strategies.
• Pre-operative diagnosis of CS invasion by PAs might affect creating surgical strategies • MRI might help for diagnosis of CS invasion by PAs before surgery • Radiomics might improve the CS invasion detection by MR images.
利用基于对比增强 T1（CE-T1）和 T2 加权磁共振（MR）成像的放射组学方法，术前预测垂体腺瘤（PA）的海绵窦（CS）侵袭。
本回顾性研究共纳入 194 例 Knosp 分级为 2 级和 3 级的 PA 患者（训练集：n=97；测试集：n=97）。从 CE-T1 和 T2 MR 图像中提取了 2553 个定量成像特征。为了选择最具信息量的特征，进行了最小绝对收缩和选择算子（LASSO）。随后，使用线性支持向量机（SVM）拟合预测模型。此外，通过纳入临床-放射学风险因素和放射组学特征构建了列线图，并通过决策曲线分析（DCA）验证了该列线图的临床实用性。
在训练集中，基于三个影像学特征，放射组学模型在训练集和测试集的 AUC 值分别为 0.852 和 0.826。基于放射组学特征和临床-放射学风险因素的列线图在训练集的 AUC 值为 0.899，在测试集的 AUC 值为 0.871。
本研究中开发的列线图可能有助于神经外科医生术前预测 Knosp 分级为 2 级和 3 级的 PA 对 CS 的侵袭，这可能有助于制定手术策略。
• PA 对 CS 的侵袭术前诊断可能会影响制定手术策略。
• MRI 可能有助于手术前诊断 PA 对 CS 的侵袭。
• 放射组学可能会提高 MR 图像对 CS 侵袭的检测。
