背景:慢性硬膜下血肿(CSDH)是一种普遍的医学疾病,由于复发的风险,对术后管理提出了重大挑战。这种复发不仅会给患者带来身体上的痛苦,而且还会增加家庭和医疗保健系统的经济负担。目前,预后判断在很大程度上取决于临床医生的专业知识,揭示了临床环境中精确预测模型的缺乏。
目的:本研究旨在使用机器学习(ML)技术构建预测模型,以评估手术后CSDH复发的可能性,这给患者和医疗保健系统带来了更大的好处。
方法:收集来自133名患者的数据并将其划分为训练集(n=93)和测试集(n=40)。使用3DSlicer软件从术前颅骨计算机断层扫描中提取影像组学特征。这些特点,结合临床数据和复合临床-影像组学特征,作为模型开发的输入变量。四种不同的机器学习算法被用来建立预测模型,它们的性能通过准确性进行了严格的评估,曲线下面积(AUC),和召回指标。确定了最优模型,然后是递归特征消除,用于特征选择,导致增强的预测功效。使用来自其他医疗机构的数据集进行外部验证。
结果:经过严格的实验分析,支持向量机模型,基于临床影像组学特征,成为预测CSDH患者术后复发最有效的方法。在特征选择之后,对模型产生重大影响的关键变量作为输入集,从而增强其预测准确性。该模型表现出强大的性能,指标包括92.72%的准确率,AUC为91.34%,和93.16%的召回。外部验证进一步证实了其有效性,准确率为90.32%,AUC为91.32%,召回率88.37%,确认其临床适用性。
结论:这项研究证实了基于ML的预测模型的可行性和临床相关性,使用临床影像组学特征,对于CSDH患者术后复发的相对准确的预测。如果将该模型整合到临床实践中,这对提高临床决策过程的质量和效率具有重要意义,这可以提高诊断和治疗的准确性,减少不必要的检查和手术,减少医疗资源的浪费。
BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also add to the financial burden on the family and the health care system. Currently, prognosis determination largely depends on clinician expertise, revealing a dearth of precise prediction models in clinical settings.
OBJECTIVE: This study aims to use machine learning (ML) techniques for the construction of predictive models to assess the likelihood of CSDH recurrence after surgery, which leads to greater benefits for patients and the health care system.
METHODS: Data from 133 patients were amassed and partitioned into a training set (n=93) and a test set (n=40). Radiomics features were extracted from preoperative cranial computed tomography scans using 3D Slicer software. These features, in conjunction with clinical data and composite clinical-radiomics features, served as input variables for model development. Four distinct ML algorithms were used to build predictive models, and their performance was rigorously evaluated via accuracy, area under the curve (AUC), and recall metrics. The optimal model was identified, followed by recursive feature elimination for feature selection, leading to enhanced predictive efficacy. External validation was conducted using data sets from additional health care facilities.
RESULTS: Following rigorous experimental analysis, the support vector machine model, predicated on clinical-radiomics features, emerged as the most efficacious for predicting postoperative recurrence in patients with CSDH. Subsequent to feature selection, key variables exerting significant impact on the model were incorporated as the input set, thereby augmenting its predictive accuracy. The model demonstrated robust performance, with metrics including accuracy of 92.72%, AUC of 91.34%, and recall of 93.16%. External validation further substantiated its effectiveness, yielding an accuracy of 90.32%, AUC of 91.32%, and recall of 88.37%, affirming its clinical applicability.
CONCLUSIONS: This study substantiates the feasibility and clinical relevance of an ML-based predictive model, using clinical-radiomics features, for relatively accurate prognostication of postoperative recurrence in patients with CSDH. If the model is integrated into clinical practice, it will be of great significance in enhancing the quality and efficiency of clinical decision-making processes, which can improve the accuracy of diagnosis and treatment, reduce unnecessary tests and surgeries, and reduce the waste of medical resources.