Jin Shucheng, Xu Qin, Sun Chen, Zhang Yuan, Wang Yangyang, Wang Xi, Guan Xiudong, Li Deling, Li Yiming, Zhang Chuanbao, Jia Wang
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Beijing Neurosurgical Institute, Beijing, 100070, China.
J Imaging Inform Med. 2025 Apr 14. doi: 10.1007/s10278-025-01400-1.
Compared to non-functional pituitary neuroendocrine tumors (NF-PitNETs), posterior pituitary tumors (PPTs) require more intraoperative protection of the pituitary stalk and hypothalamus, and their perioperative management is more complex than NF-PitNETs. However, they are difficult to be distinguished via magnetic resonance images (MRI) before operation. Based on clinical features and radiological signature extracted from MRI, this study aims to establish a model for distinguishing NF-PitNETs and PPTs. Preoperative MRI of 110 patients with NF-PitNETs and 55 patients with PPTs were retrospectively obtained. Patients were randomly assigned to the training (n = 110) and validation (n = 55) cohorts in a 2:1 ratio. The lest absolute shrinkage and selection operator (LASSO) algorithm was applied to develop a radiomic signature. Afterwards, an individualized predictive model (nomogram) incorporating radiomic signatures and predictive clinical features was developed. The nomogram's performance was evaluated by calibration and decision curve analyses. Five features derived from contrast-enhanced images were selected using the LASSO algorithm. Based on the mentioned methods, the calculation formula of radiomic score was obtained. The constructed nomogram incorporating radiomic signature and predictive clinical features showed a good calibration and outperformed the clinical features for predicting NF-PitNETs and PPTs (area under the curve [AUC]: 0.937 vs. 0.595 in training cohort [p < 0.001]; 0.907 vs. 0.782 in validation cohort [p = 0.03]). The decision curve shows that the individualized predictive model adds more benefit than clinical feature when the threshold probability ranges from 10 to 100%. Individualized predictive model provides a novel noninvasive imaging biomarker and could be conveniently used to distinguish NF-PitNETs and PPTs, which provides a significant reference for preoperative preparation and intraoperative decision-making.
与无功能垂体神经内分泌肿瘤(NF-PitNETs)相比,垂体后叶肿瘤(PPTs)在术中需要更多地保护垂体柄和下丘脑,并且其围手术期管理比NF-PitNETs更复杂。然而,术前通过磁共振成像(MRI)很难将它们区分开来。基于从MRI中提取的临床特征和影像学特征,本研究旨在建立一个区分NF-PitNETs和PPTs的模型。回顾性收集了110例NF-PitNETs患者和55例PPTs患者的术前MRI资料。患者以2:1的比例随机分配到训练队列(n = 110)和验证队列(n = 55)。应用最小绝对收缩和选择算子(LASSO)算法来开发一个影像组学特征。之后,开发了一个结合影像组学特征和预测性临床特征的个体化预测模型(列线图)。通过校准和决策曲线分析来评估列线图的性能。使用LASSO算法从增强图像中选择了五个特征。基于上述方法,获得了影像组学评分的计算公式。构建的结合影像组学特征和预测性临床特征的列线图显示出良好的校准,并且在预测NF-PitNETs和PPTs方面优于临床特征(训练队列中的曲线下面积[AUC]:0.937对0.595 [p < 0.001];验证队列中的0.907对0.782 [p = 0.03])。决策曲线表明,当阈值概率在10%至100%范围内时,个体化预测模型比临床特征增加了更多益处。个体化预测模型提供了一种新的非侵入性影像学生物标志物,可方便地用于区分NF-PitNETs和PPTs,这为术前准备和术中决策提供了重要参考。