Huang Bowen, Chen Tengyun, Zhang Yuekang, Mao Qing, Ju Yan, Liu Yanhui, Wang Xiang, Li Qiang, Lei Yinjie, Ren Yanming
Department of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, China.
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Brain Sci. 2023 Oct 19;13(10):1483. doi: 10.3390/brainsci13101483.
The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis.
Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models.
We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919-1), 0.950 (0.877-1), 0.939 (0.845-1), and 0.875 (0.690-1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model.
The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.
弥漫性中线胶质瘤（DMG）伴H3K27M（H3K27M-DMG）改变的患者预后较差；然而，一个能够鼓励对这类病变进行个体预后准确预测的模型仍然难以捉摸。我们旨在构建基于DeepSurv的H3K27M-DMG生存模型以预测患者预后。
从单一中心招募的患者用于模型训练，从另一个中心招募的患者用于外部验证。采用单因素和多因素Cox回归分析来选择特征。构建了四个机器学习模型，并计算一致性指数（C指数）和综合Brier评分（IBS）。我们使用受试者工作特征曲线（ROC）和受试者工作特征曲线下面积（AUC）来评估预测6个月、12个月、18个月和24个月生存率的准确性。使用特征重要性热图来解释这四个模型的结果。
我们在训练集中招募了113名患者，在测试集中招募了23名患者。我们将肿瘤大小、肿瘤位置、卡氏功能状态评分（KPS）、强化情况、放疗和化疗纳入模型训练。在四个模型中，DeepSurv预测的准确性最高，训练集和外部测试集的C指数分别为0.862和0.811。DeepSurv模型在6个月、12个月、18个月和24个月时的AUC值最高，分别为0.970（0.919-1）、0.950（0.877-1）、0.939（0.845-1）和0.875（0.690-1）。我们设计了一个交互式界面，以更直观地展示DeepSurv模型提供的生存概率预测结果。
DeepSurv模型在预测准确性和稳健性方面优于传统机器学习模型，并且还可以为患者提供个性化的治疗建议。DeepSurv模型可能在未来为患者制定治疗计划时提供决策帮助。
