{"title":"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。","authors":"Jinlan Huang, Shoupeng Ding, Lijin Lin, Guiyang Zhong, Zhou Yu, Qingwen Luo, Dongmei Chen, Yazhi Chen, Shouzhao Zheng, Shihao Zheng","doi":"10.3171/2024.11.JNS241673","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":16505,"journal":{"name":"Journal of neurosurgery","volume":" ","pages":"1-13"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a machine learning model based on laboratory parameters for preoperative prediction of Ki-67 expression in gliomas.\",\"authors\":\"Jinlan Huang, Shoupeng Ding, Lijin Lin, Guiyang Zhong, Zhou Yu, Qingwen Luo, Dongmei Chen, Yazhi Chen, Shouzhao Zheng, Shihao Zheng\",\"doi\":\"10.3171/2024.11.JNS241673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":16505,\"journal\":{\"name\":\"Journal of neurosurgery\",\"volume\":\" \",\"pages\":\"1-13\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3171/2024.11.JNS241673\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3171/2024.11.JNS241673","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
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.
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.