Azimi Parisa, Yazdanian Taravat, Zohrevand Amirhosein, Ahmadiani Abolhassan
Neurosurgeon, Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Research Fellow at the Neurological Clinical Research Institute and Healey and AMG Center for ALS, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Int J Mol Cell Med. 2024;13(1):79-90. doi: 10.22088/IJMCM.BUMS.13.1.79.
Glioblastoma (GBM) is the most aggressive and lethal brain tumor. Artificial neural networks (ANNs) have the potential to make accurate predictions and improve decision making. The aim of this study was to create an ANN model to predict 15-month survival in GBM patients according to gene expression databases. Genomic data of GBM were downloaded from the CGGA, TCGA, MYO, and CPTAC. Logistic regression (LR) and ANN model were used. Age, gender, IDH wild-type/mutant and the 31 most important genes from our previous study, were determined as input factors for the established ANN model. 15-month survival time was used to evaluate the results. The normalized importance scores of each covariate were calculated using the selected ANN model. The area under a receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) statistic and accuracy of prediction were measured to evaluate the two models. SPSS 26 was utilized. A total of 551 patients (61% male, mean age 55.5 ± 13.3 years) patients were divided into training, testing, and validation datasets of 441, 55 and 55 patients, respectively. The main candidate genes found were: FN1, ICAM1, MYD88, IL10, and CCL2 with the ANN model; and MMP9, MYD88, and CDK4 with LR model. The AUCs were 0.71 for the LR and 0.81 for the ANN analysis. Compared to the LR model, the ANN model showed better results: Accuracy rate, 83.3 %; H-L statistic, 6.5 %; and AUC, 0.81 % of patients. The findings show that ANNs can accurately predict the 15-month survival in GBM patients and contribute to precise medical treatment.
胶质母细胞瘤（GBM）是最具侵袭性和致命性的脑肿瘤。人工神经网络（ANN）有潜力做出准确预测并改善决策。本研究的目的是根据基因表达数据库创建一个ANN模型，以预测GBM患者的15个月生存率。从CGGA、TCGA、MYO和CPTAC下载GBM的基因组数据。使用逻辑回归（LR）和ANN模型。年龄、性别、异柠檬酸脱氢酶（IDH）野生型/突变型以及我们之前研究中的31个最重要基因，被确定为所建立的ANN模型的输入因素。用15个月生存时间来评估结果。使用选定的ANN模型计算每个协变量的标准化重要性得分。测量受试者工作特征（ROC）曲线下面积（AUC）、霍斯默-莱梅肖（H-L）统计量和预测准确性，以评估这两个模型。使用SPSS 26。总共551例患者（61%为男性，平均年龄55.5±13.3岁）分别被分为441例、55例和55例患者的训练、测试和验证数据集。通过ANN模型发现的主要候选基因有：纤连蛋白1（FN1）、细胞间黏附分子1（ICAM1）、髓样分化因子88（MYD88）、白细胞介素10（IL10）和趋化因子配体2（CCL2）；通过LR模型发现的有基质金属蛋白酶9（MMP9）、MYD88和细胞周期蛋白依赖性激酶4（CDK4）。LR分析的AUC为0.71，ANN分析的AUC为0.81。与LR模型相比，ANN模型显示出更好的结果：准确率为83.3%；H-L统计量为6.5%；患者的AUC为0.81%。研究结果表明，人工神经网络可以准确预测GBM患者的15个月生存率，并有助于精准医疗。
