Nomograms for predicting progression-free survival and overall survival after surgery and concurrent chemoradiotherapy for glioblastoma: a retrospective cohort study.
Nomograms for predicting progression-free survival and overall survival after surgery and concurrent chemoradiotherapy for glioblastoma: a retrospective cohort study.
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DOI：
10.21037/atm-21-673
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发表时间：
2021-04
影响因子：
--
通讯作者：
Wei Q
中科院分区：
文献类型：
--
作者：
Zheng L;Zhou ZR;Shi M;Chen H;Yu QQ;Yang Y;Liu L;Zhang L;Guo Y;Zhou X;Li C;Wei Q
胶质母细胞瘤（GBM）是成人最常见的恶性脑肿瘤。GBM患者预后差。即使采用积极的标准治疗，中位总生存期也仅为14.6个月。因此，确定复发和寻找影响GBM预后的因素至关重要。本研究的目的是筛选与接受手术和同步放化疗的GBM患者的无进展生存期（PFS）和总生存期（OS）相关的变量，并基于术前影像学参数和临床实践中容易获得的临床病理变量提出个体风险预测的诺模图。我们回顾性分析了2015年1月1日至2018年6月1日在浙江大学医学院附属第二医院接受手术和同步放化疗的114例连续GBM患者。24个术前磁共振成像（MRI）参数从图像采集和通信系统（PACS）中手动提取。从电子病历系统（EMRS）中提取临床病理因素。最小绝对收缩和选择算子（LASSO）回归和考克斯回归分别用于特征选择和模型预测。模型采用列线图表示，根据评分确定复发和生存的风险。使用C统计量、校准图和Kaplan-Meier曲线检验列线图预测PFS和OS的性能。结果显示，性别、Karnofsky评分（KPS）、O 6-甲基葡糖胺-DNA甲基转移酶（MGMT）蛋白表达、替莫唑胺（TMZ）辅助化疗周期数和MRI特征可有效预测PFS;性别、KPS、手术程度、TMZ周期数和MRI特征可有效预测OS（C统计量：PFS为0.81，OS为0.79）。在PFS列线图中，评分大于122的患者被认为具有高复发风险。在OS的诺模图中，临界评分为115和145，然后将患者分为低、中、高风险。总之，我们的诺模图可以有效地预测GBM患者的复发风险和生存率，从而可以很好地指导临床实践。
Glioblastoma (GBM) is the most common malignant brain tumor in adults. The prognosis of GBM patients is poor. Even with active standard treatment, the median overall survival is only 14.6 months. It is therefore critical to ascertain recurrence and search for factors that influence the prognosis of GBM. This study aimed to screen the variables related to the progression-free survival (PFS) and overall survival (OS) of GBM patients undergoing surgery and concurrent chemoradiotherapy, as well as propose a nomogram for individual risk prediction based on preoperative imaging parameters and clinicopathological variables readily available in clinical practice. We retrospectively analyzed 114 consecutive patients with GBM who underwent surgery and concurrent chemoradiotherapy at the Second Affiliated Hospital, Zhejiang University School of Medicine from January 1st, 2015, to June 1st, 2018. Twenty-four preoperative magnetic resonance imaging (MRI) parameters were extracted manually from the Picture Archiving and Communication System (PACS). Clinicopathological factors were extracted from the electronic medical record system (EMRS). Least absolute shrinkage and selection operator (LASSO) regression and Cox regression were used for feature selection and model prediction, respectively. The models were presented using nomograms, which were applied to identify the risk of recurrence and survival according to the score. The performance of the nomograms to predict PFS and OS was tested with C-statistics, calibration plots, and Kaplan-Meier curves. The results revealed that sex, Karnofsky performance score (KPS), O6-methylglucamine-DNA methyltransferase (MGMT) protein expression, number of adjuvant chemotherapy cycles with temozolomide (TMZ), and the MRI signature effectively predicted PFS; and sex, KPS, extent of surgery, number of TMZ cycles, and MRI signature effectively predicted OS. The nomogram revealed good discriminative ability (C-statistics: 0.81 for PFS and 0.79 for OS). In the nomogram of PFS, patients with a score greater than 122 were considered to have a high risk of recurrence. In the nomogram of OS, the cutoff score were 115 and 145, and then patients were classified as low, medium, and high risk. In conclusion, our nomograms can effectively predict the risk of recurrence and survival of GBM patients and thus can be a good guide for clinical practice.