摘要
一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-中枢神经系统疾病和状况的新研究-脑膜肿瘤是一篇报道的主题。根据NewsRx C或受访者来自中华人民共和国广州的消息，研究称，本研究旨在建立并验证一种基于多参数MRI G(MRI)放射组学和临床特征的脊髓内神经鞘瘤鉴别模型。本研究分析了209例脊髓内肿瘤患者的术前MRI(MR)图像和临床特征。训练组(n=111)和试验组(n=48)的比例为7-3.我们的新闻记者引用了南方医科大学第三附属D医院的研究，“使用训练队列构建了列线图，并在测试队列和独立验证队列(n=50)中进行了内部和外部验证。使用受试者操作特征(ROC)的曲线(AUC)下面积，决策曲线分析(DCA)，评估模型性能。”与放射组学模型和临床模型相比，诺模图在区分脊髓神经鞘瘤和男性神经鞘瘤方面显示出更好的预测效果。在训练、测试和外部验证队列中，诺模图的AUC分别为0.994、0.962和0.949.DCAs表明，诺模图产生的净效益最好，标定曲线表明，诺模图与实际观测值吻合良好。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions-Meningeal Neoplasms is the subject of a report. Accor ding to news originating from Guangzhou, People's Republic of China, by NewsRx c orrespondents, research stated, "The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imagin g (MRI) radiomics and clinical features to distinguish intraspinal schwannomas f rom meningiomas. This research analyzed the preoperative magnetic resonance (MR) images and clinical characteristics of 209 patients with intraspinal tumors who received tumor resection at three institutions. 159 individuals from institutio ns 1 and 2 were randomly assigned into a training group (n=111) and a test group (n=48) in a 7-3 ratio." Our news journalists obtained a quote from the research from the Third Affiliate d Hospital of Southern Medical University, "A nomogram was constructed using the training cohort and was internally and externally verified in the test cohort a nd an independent validation cohort (n=50). Model performance was assessed utili zing the area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration curves. The nomogram exhibited s uperior predictive efficacy in distinguishing between spinal schwannomas and men ingiomas when compared to both the radiomics model and the clinical model. The n omogram yielded AUCs of 0.994, 0.962, and 0.949 in the training, test, and exter nal validation cohorts, respectively, indicating its exceptional differentiating ability. The DCAs demonstrated that the nomogram yielded the best net benefit. The calibration curves indicated that the nomogram got good agreement between th e predicted and the actual observation."
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