PDF(Pubmed)
Abstract：
UNASSIGNED: Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer.
UNASSIGNED: The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach.
UNASSIGNED: Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors.
UNASSIGNED: The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.
UNASSIGNED: The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach.
UNASSIGNED: Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors.
UNASSIGNED: The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.
摘要：
■隐匿性淋巴结转移（OLNM）是早期舌癌（cT1-2N0M0）的重要预后因素，也是治疗决策的决定因素。因此,OLNM的准确预测可以显着影响舌癌患者的临床治疗和预后。这项研究的目的是开发和验证基于多组学的模型来预测早期舌癌患者的OLNM。
■对125例诊断为早期舌癌（cT1-2N0M0）的患者行一期手术治疗和选择性颈清扫术的资料进行回顾性分析。总共100名患者被随机分配到训练集，25名患者被随机分配到测试集。收集这些患者的术前对比增强计算机断层扫描（CT）和临床资料。从原发肿瘤中提取影像组学特征作为CT图像上的感兴趣区域(ROI),和相关性分析和最小绝对收缩和选择算子(LASSO)方法用于识别最相关的特征。构建了支持向量机（SVM）分类器，并与其他机器学习算法进行了比较。用同样的方法,我们建立了一个临床模型,选择肿瘤周围和肿瘤内图像作为深度学习模型的输入.堆叠集成技术用于组合多个模型。对集成模型的预测性能进行了准确性评估，灵敏度,特异性，和受试者工作特征曲线下面积（AUC-ROC），并与专家评估进行比较。使用分层五折交叉验证方法进行内部验证。
■在125名患者中，41例(32.8%)术后病理检查显示OLNM。与单个模型相比，集成模型实现了更高的预测性能，准确率为84%，100%的灵敏度，特异性为76.5%，AUC-ROC为0.949（95%CI[0.870-1.000]）。此外,综合模式的表现超过年轻医生，与经验丰富的医生的评价相当。
■基于多组学的模型可以准确预测早期舌癌患者的OLNM，并且可以作为有价值的决策工具，以确定适当的治疗方法，并避免没有OLNM的患者进行不必要的颈部手术。
■对125例诊断为早期舌癌（cT1-2N0M0）的患者行一期手术治疗和选择性颈清扫术的资料进行回顾性分析。总共100名患者被随机分配到训练集，25名患者被随机分配到测试集。收集这些患者的术前对比增强计算机断层扫描（CT）和临床资料。从原发肿瘤中提取影像组学特征作为CT图像上的感兴趣区域(ROI),和相关性分析和最小绝对收缩和选择算子(LASSO)方法用于识别最相关的特征。构建了支持向量机（SVM）分类器，并与其他机器学习算法进行了比较。用同样的方法,我们建立了一个临床模型,选择肿瘤周围和肿瘤内图像作为深度学习模型的输入.堆叠集成技术用于组合多个模型。对集成模型的预测性能进行了准确性评估，灵敏度,特异性，和受试者工作特征曲线下面积（AUC-ROC），并与专家评估进行比较。使用分层五折交叉验证方法进行内部验证。
■在125名患者中，41例(32.8%)术后病理检查显示OLNM。与单个模型相比，集成模型实现了更高的预测性能，准确率为84%，100%的灵敏度，特异性为76.5%，AUC-ROC为0.949（95%CI[0.870-1.000]）。此外,综合模式的表现超过年轻医生，与经验丰富的医生的评价相当。
■基于多组学的模型可以准确预测早期舌癌患者的OLNM，并且可以作为有价值的决策工具，以确定适当的治疗方法，并避免没有OLNM的患者进行不必要的颈部手术。
