PDF(Pubmed)
Abstract：
BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI).
METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility.
RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model.
CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.
METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility.
RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model.
CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.
摘要：
背景：本研究旨在开发和验证一种基于机器学习（ML）的融合模型，以使用多参数磁共振成像（MRI）术前预测头颈部鳞状细胞癌（HNSCC）患者的Ki-67表达水平。
方法：回顾性研究了来自两个医疗中心的351例经病理证实的HNSCC患者，并将其分为训练组（n=196），内部验证(n=84)，和外部验证(n=71)队列。从T2加权图像和对比增强的T1加权图像中提取影像组学特征并进行筛选。七个ML分类器，包括k-最近邻(KNN)，支持向量机(SVM)，逻辑回归(LR)，随机森林(RF)，线性判别分析(LDA)，朴素贝叶斯(NB)，和极限梯度提升（XGBoost）进行了训练。最佳分类器用于计算放射组学（Rad）评分，并结合临床因素构建融合模型。根据校准评估性能，歧视,重新分类，和临床效用。
结果：最终选择了结合多参数MRI的13个特征。SVM分类器表现出最佳性能,在验证队列中，曲线下平均面积（AUC）最高，为0.851。融合了基于SVM的Rad评分与临床T分期和MR报告的淋巴结状态的融合模型在训练中取得了令人鼓舞的预测性能（AUC=0.916），内部验证（AUC=0.903），和外部验证(AUC=0.885)队列。此外，与临床模型相比，融合模型显示出更好的临床获益和更高的分类准确性。
结论：基于多参数MRI的基于ML的融合模型有望预测HNSCC患者的Ki-67表达水平，这可能有助于预后评估和临床决策。
方法：回顾性研究了来自两个医疗中心的351例经病理证实的HNSCC患者，并将其分为训练组（n=196），内部验证(n=84)，和外部验证(n=71)队列。从T2加权图像和对比增强的T1加权图像中提取影像组学特征并进行筛选。七个ML分类器，包括k-最近邻(KNN)，支持向量机(SVM)，逻辑回归(LR)，随机森林(RF)，线性判别分析(LDA)，朴素贝叶斯(NB)，和极限梯度提升（XGBoost）进行了训练。最佳分类器用于计算放射组学（Rad）评分，并结合临床因素构建融合模型。根据校准评估性能，歧视,重新分类，和临床效用。
结果：最终选择了结合多参数MRI的13个特征。SVM分类器表现出最佳性能,在验证队列中，曲线下平均面积（AUC）最高，为0.851。融合了基于SVM的Rad评分与临床T分期和MR报告的淋巴结状态的融合模型在训练中取得了令人鼓舞的预测性能（AUC=0.916），内部验证（AUC=0.903），和外部验证(AUC=0.885)队列。此外，与临床模型相比，融合模型显示出更好的临床获益和更高的分类准确性。
结论：基于多参数MRI的基于ML的融合模型有望预测HNSCC患者的Ki-67表达水平，这可能有助于预后评估和临床决策。
