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Abstract：
BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors.
METHODS: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.
RESULTS: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model\'s performance, increasing the validation cohort AUC to 0.87.
CONCLUSIONS: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.
CONCLUSIONS: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.
METHODS: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.
RESULTS: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model\'s performance, increasing the validation cohort AUC to 0.87.
CONCLUSIONS: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.
CONCLUSIONS: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.
摘要：
背景：口腔鳞状细胞癌（OSCC）在其早期和晚期阶段提出了重大的诊断挑战。本研究旨在利用OSCC患者术前MRI及生化指标预测肿瘤分期。
方法：本研究涉及来自两个医疗中心的198名患者。对对比增强的T1加权（ceT1W）和T2加权（T2W）MRI进行了详细分析，将这些与生化指标相结合，进行综合评价。最初，选择42项临床生化指标进行考虑。通过单变量分析和多变量分析，仅保留那些p值小于0.05的指标用于模型开发.要提取成像特征，机器学习算法与视觉转换(ViT)技术结合使用。将这些特征与生化指标整合以进行预测建模。使用接收器工作特性（ROC）曲线评估模型的性能。
结果：经过严格的生化指标筛选，为模型选择了四个关键标志物：胆固醇，甘油三酯，极低密度脂蛋白胆固醇和氯化物.模型,使用影像组学和深度学习开发，用于从ceT1W和T2W图像中提取特征，当单独使用这些成像方式时,在验证队列中显示出较低的曲线下面积(AUC)为0.85。然而,整合这些生化指标提高了模型的性能，将验证队列AUC增加到0.87。
结论：在这项研究中，多模态融合后，模型的性能显著提高，优于单模态方法。
结论：这种整合的影像组学，ViT型号,和脂质代谢物分析，提出了一种有前途的非侵入性技术来预测OSCC的分期。
方法：本研究涉及来自两个医疗中心的198名患者。对对比增强的T1加权（ceT1W）和T2加权（T2W）MRI进行了详细分析，将这些与生化指标相结合，进行综合评价。最初，选择42项临床生化指标进行考虑。通过单变量分析和多变量分析，仅保留那些p值小于0.05的指标用于模型开发.要提取成像特征，机器学习算法与视觉转换(ViT)技术结合使用。将这些特征与生化指标整合以进行预测建模。使用接收器工作特性（ROC）曲线评估模型的性能。
结果：经过严格的生化指标筛选，为模型选择了四个关键标志物：胆固醇，甘油三酯，极低密度脂蛋白胆固醇和氯化物.模型,使用影像组学和深度学习开发，用于从ceT1W和T2W图像中提取特征，当单独使用这些成像方式时,在验证队列中显示出较低的曲线下面积(AUC)为0.85。然而,整合这些生化指标提高了模型的性能，将验证队列AUC增加到0.87。
结论：在这项研究中，多模态融合后，模型的性能显著提高，优于单模态方法。
结论：这种整合的影像组学，ViT型号,和脂质代谢物分析，提出了一种有前途的非侵入性技术来预测OSCC的分期。
