Yu Yang, Lu Xiaoli, Yao Yidi, Xie Yongsheng, Ren Yan, Chen Liang, Mao Ying, Yao Zhenwei, Yue Qi
Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Neurooncol Adv. 2023 Aug 8;5(1):vdad094. doi: 10.1093/noajnl/vdad094. eCollection 2023 Jan-Dec.
Germinomas are sensitive to radiation and chemotherapy, and their management distinctly differs from other kinds of pineal region tumors. The aim of this study was to construct a prediction model based on clinical features and preoperative magnetic resonance (MR) manifestations to achieve noninvasive diagnosis of germinomas in pineal region.
A total of 126 patients with pineal region tumors were enrolled, including 36 germinomas, 53 nongerminomatous germ cell tumors (NGGCTs), and 37 pineal parenchymal tumors (PPTs). They were divided into a training cohort ( = 90) and a validation cohort ( = 36). Features were extracted from clinical records and conventional MR images. Multivariate analysis was performed to screen for independent predictors to differentiate germ cell tumors (GCTs) and PPTs, germinomas, and NGGCTs, respectively. From this, a 2-step nomogram model was established, with model 1 for discriminating GCTs from PPTs and model 2 for identifying germinomas in GCTs. The model was tested in a validation cohort.
Both model 1 and model 2 yielded good predictive efficacy, with c-indexes of 0.967 and 0.896 for the diagnosis of GCT and germinoma, respectively. Calibration curve, decision curve, and clinical impact curve analysis further confirmed their predictive accuracy and clinical usefulness. The validation cohort achieved areas under the receiver operating curves of 0.885 and 0.926, respectively.
The 2-step model in this study can noninvasively differentiate GCTs from PPTs and further identify germinomas, thus holding potential to facilitate treatment decision-making for pineal region tumors.
生殖细胞瘤对放疗和化疗敏感，其治疗方法与其他类型的松果体区肿瘤明显不同。本研究的目的是构建一种基于临床特征和术前磁共振（MR）表现的预测模型，以实现松果体区生殖细胞瘤的无创诊断。
共纳入126例松果体区肿瘤患者，包括36例生殖细胞瘤、53例非生殖细胞性生殖细胞肿瘤（NGGCTs）和37例松果体实质肿瘤（PPTs）。他们被分为训练队列（n = 90）和验证队列（n = 36）。从临床记录和传统MR图像中提取特征。进行多变量分析以筛选独立预测因子，分别用于区分生殖细胞肿瘤（GCTs）和PPTs、生殖细胞瘤和NGGCTs。据此，建立了一个两步列线图模型，模型1用于区分GCTs和PPTs，模型2用于识别GCTs中的生殖细胞瘤。该模型在验证队列中进行了测试。
模型1和模型2均产生了良好的预测效果，诊断GCT和生殖细胞瘤的c指数分别为0.967和0.896。校准曲线、决策曲线和临床影响曲线分析进一步证实了它们的预测准确性和临床实用性。验证队列的受试者操作曲线下面积分别为0.885和0.926。
本研究中的两步模型可以无创地区分GCTs和PPTs，并进一步识别生殖细胞瘤，因此具有促进松果体区肿瘤治疗决策的潜力。
