Habibi Mohammad Amin, Dinpazhouh Ali, Aliasgary Aliakbar, Mirjani Mohammad Sina, Mousavinasab Mehdi, Ahmadi Mohammad Reza, Minaee Poriya, Eazi SeyedMohammad, Shafizadeh Milad, Gurses Muhammet Enes, Lu Victor M, Berke Chandler N, Ivan Michael E, Komotar Ricardo J, Shah Ashish H
Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
Student Research Committee, Faculty of Medicine, Qom University of Medical Science, Qom, Iran.
Neuroradiol J. 2024 Aug 5:19714009241269526. doi: 10.1177/19714009241269526.
Glioma is one of the most common primary brain tumors. The presence of the telomerase reverse transcriptase promoter (pTERT) mutation is associated with a better prognosis. This study aims to investigate the TERT mutation in patients with glioma using machine learning (ML) algorithms on radiographic imaging.
This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The electronic databases of PubMed, Embase, Scopus, and Web of Science were searched from inception to August 1, 2023. The statistical analysis was performed using the MIDAS package of STATA v.17.
A total of 22 studies involving 5371 patients were included for data extraction, with data synthesis based on 11 reports. The analysis revealed a pooled sensitivity of 0.86 (95% CI: 0.78-0.92) and a specificity of 0.80 (95% CI 0.72-0.86). The positive and negative likelihood ratios were 4.23 (95% CI: 2.99-5.99) and 0.18 (95% CI: 0.11-0.29), respectively. The pooled diagnostic score was 3.18 (95% CI: 2.45-3.91), with a diagnostic odds ratio 24.08 (95% CI: 11.63-49.87). The Summary Receiver Operating Characteristic (SROC) curve had an area under the curve (AUC) of 0.89 (95% CI: 0.86-0.91).
The study suggests that ML can predict TERT mutation status in glioma patients. ML models showed high sensitivity (0.86) and moderate specificity (0.80), aiding disease prognosis and treatment planning. However, further development and improvement of ML models are necessary for better performance metrics and increased reliability in clinical practice.
胶质瘤是最常见的原发性脑肿瘤之一。端粒酶逆转录酶启动子（pTERT）突变的存在与较好的预后相关。本研究旨在利用影像学上的机器学习（ML）算法研究胶质瘤患者的TERT突变情况。
本研究按照系统评价和Meta分析的首选报告项目（PRISMA）指南进行。检索了PubMed、Embase、Scopus和Web of Science的电子数据库，检索时间从建库至2023年8月1日。使用STATA v.17的MIDAS软件包进行统计分析。
共纳入22项研究，涉及5371例患者进行数据提取，并基于11份报告进行数据综合。分析显示合并敏感度为0.86（95%可信区间：0.78 - 0.92），特异度为0.80（95%可信区间0.72 - 0.86）。阳性似然比和阴性似然比分别为4.23（95%可信区间：2.99 - 5.99）和0.18（95%可信区间：0.11 - 0.29）。合并诊断分数为3.18（95%可信区间：2.45 - 3.91），诊断比值比为24.08（95%可信区间：11.63 - 49.87）。总结性受试者工作特征（SROC）曲线下面积（AUC）为0.89（95%可信区间：0.86 - 0.91）。
该研究表明ML可以预测胶质瘤患者的TERT突变状态。ML模型显示出高敏感度（0.86）和中等特异度（0.80），有助于疾病预后和治疗规划。然而，为了获得更好的性能指标并提高临床实践中的可靠性，有必要进一步开发和改进ML模型。
