基于实验室参数的机器学习模型的开发与验证,用于胶质瘤术前预测Ki-67的表达。

IF 3.5 2区 医学 Q1 CLINICAL NEUROLOGY
Jinlan Huang, Shoupeng Ding, Lijin Lin, Guiyang Zhong, Zhou Yu, Qingwen Luo, Dongmei Chen, Yazhi Chen, Shouzhao Zheng, Shihao Zheng
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引用次数: 0

摘要

目的:神经胶质瘤是脑肿瘤中最常见的一种,病死率高。Ki-67增殖指数是细胞增殖的重要标志,已被证明可以预测肿瘤的分类和预后。本研究的目的是开发和验证一种基于机器学习(ML)和常规实验室参数的无创模型,用于术前预测胶质瘤中Ki-67的水平。方法:回顾性纳入2个医疗中心(2020年1月至2023年12月)病理确诊的胶质瘤患者506例,分为训练组(n = 352)、内部验证组(n = 88)和外部验证组(n = 66)。根据Ki-67增殖指数将患者分为低Ki-67(指数< 10%)组和高Ki-67(指数≥10%)组。术前1周内通过实验室信息系统获取实验室参数。使用极端梯度增强(XGBoost)、支持向量机(SVM)和最小绝对收缩和选择算子(LASSO)筛选与Ki-67水平相关的潜在特征。然后,对SVM、XGBoost、逻辑回归(LR)、随机森林、自适应增强(AdaBoost)、梯度增强机、中间分割、朴素贝叶斯、神经网络、袋装分类与回归树(CART)等10个ML分类器进行训练。这些模型的性能在内部和外部验证集上进行评估,使用接收者工作特征曲线下的面积(AUC)。采用校准曲线、决策曲线和临床影响曲线分析进行验证。结果:选取了15个满足XGBoost、SVM和LASSO要求的实验室参数。在所有被测试的ML模型中,LR模型具有较高的AUC、准确性、敏感性和特异性。LR模型在训练集中的auc为0.838,在内部验证集中的auc为0.800(准确度最高[0.782],灵敏度最佳[0.845]),在外部验证集中的auc为0.757。最后,将LR模型可视化为基于前6个实验室参数(年龄、阴离子间隙、载脂蛋白a -1、载脂蛋白B、钙、肌酐)的nomogram,以单独预测胶质瘤患者Ki-67增殖指数。结论:作者成功构建了基于常规实验室参数的LR模型,具有较高的敏感性和特异性,可用于术前预测胶质瘤患者Ki-67水平,为预后评估和临床决策提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a machine learning model based on laboratory parameters for preoperative prediction of Ki-67 expression in gliomas.

Objective: Glioma is the most common form of brain tumor and has high mortality. The Ki-67 proliferation index, a vital marker of cell proliferation, has been demonstrated to predict tumor classification and prognosis. The aim of this study was to develop and validate a noninvasive model based on machine learning (ML) and routine laboratory parameters to preoperatively predict the level of Ki-67 in gliomas.

Methods: A total of 506 patients with pathological confirmation of glioma from 2 medical centers (January 2020 to December 2023) were retrospectively enrolled and divided into training (n = 352), internal validation (n = 88), and external validation (n = 66) cohorts. According to the Ki-67 proliferation index, patients were classified into low Ki-67 (index < 10%) and high Ki-67 (index ≥ 10%) groups. Laboratory parameters were obtained within 1 week before surgery from the Laboratory Information System. The potential features associated with Ki-67 levels were screened using extreme gradient boosting (XGBoost), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO). Then, 10 ML classifiers, including SVM, XGBoost, logistic regression (LR), random forest, adaptive boosting (AdaBoost), gradient boosting machine, partitioning around medoids, naive Bayes, neural network, and bagged classification and regression trees (CART), were trained. The performance of these models was evaluated on internal and external validation sets using the area under the receiver operating characteristic curve (AUC). Calibration curve, decision curve, and clinical impact curve analyses were used for validation.

Results: Fifteen laboratory parameters that met the requirements of XGBoost, SVM, and LASSO were selected. Among all tested ML models, the LR model had superior performance with relatively high AUC, accuracy, sensitivity, and specificity. The LR model achieved AUCs of 0.838 in the training set, 0.800 (with the highest accuracy [0.782] and optimal sensitivity [0.845]) in the internal validation set, and 0.757 in the external validation set. Finally, the LR model was visualized as a nomogram based on the top 6 laboratory parameters (age, anion gap, apolipoprotein A-1, apolipoprotein B, calcium, creatinine) to individually predict the Ki-67 proliferation index in patients with gliomas.

Conclusions: The authors successfully constructed an LR model based on routine laboratory parameters, with relatively high sensitivity and specificity, to preoperatively predict the level of Ki-67 in patients with gliomas, which might be helpful for prognostic evaluation and clinical decision-making.

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来源期刊
Journal of neurosurgery
Journal of neurosurgery 医学-临床神经学
CiteScore
7.20
自引率
7.30%
发文量
1003
审稿时长
1 months
期刊介绍: The Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, and Neurosurgical Focus are devoted to the publication of original works relating primarily to neurosurgery, including studies in clinical neurophysiology, organic neurology, ophthalmology, radiology, pathology, and molecular biology. The Editors and Editorial Boards encourage submission of clinical and laboratory studies. Other manuscripts accepted for review include technical notes on instruments or equipment that are innovative or useful to clinicians and researchers in the field of neuroscience; papers describing unusual cases; manuscripts on historical persons or events related to neurosurgery; and in Neurosurgical Focus, occasional reviews. Letters to the Editor commenting on articles recently published in the Journal of Neurosurgery, Journal of Neurosurgery: Spine, and Journal of Neurosurgery: Pediatrics are welcome.
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