Liu Qilin, Liang Zhuang, Qi Xiaoshuang, Yang Shuwen, Fu Binyang, Dong Hui
Department Oral & Maxillofacial surgery, The Second Hospital of Dalian Medical University, Dalian, Medical University, Dalian City, Liaoning Province, China.
Department Oral & Maxillofacial surgery, The Second Hospital of Dalian Medical University, Dalian, Medical University, Dalian City, Liaoning Province, China.
Int Dent J. 2025 Jul 24;75(5):100914. doi: 10.1016/j.identj.2025.100914.
This study aims to explore a novel approach for predicting cervical lymph node metastasis (CLNM) and pathological subtypes in oral squamous cell carcinoma (OSCC) by comparing deep learning (DL) and habitat analysis models based on contrast-enhanced CT (CECT).
A retrospective analysis was conducted using CECT images from patients diagnosed with OSCC via paraffin pathology at the Second Affiliated Hospital of Dalian Medical University. All patients underwent primary tumor resection and cervical lymph node dissection, with a total of 132 cases included. A DL model was developed by analysing regions of interest (ROIs) in the CECT images using a convolutional neural network (CNN). For habitat analysis, the ROI images were segmented into 3 regions using K-means clustering, and features were selected through a fully connected neural network (FCNN) to build the model. A separate clinical model was constructed based on nine clinical features, including age, gender, and tumor location. Using LNM and pathological subtypes as endpoints, the predictive performance of the clinical model, DL model, habitat analysis model, and a combined clinical + habitat model was evaluated using confusion matrices and receiver operating characteristic (ROC) curves.
For LNM prediction, the combined clinical + habitat model achieved an area under the ROC curve (AUC) of 0.97. For pathological subtype prediction, the AUC was 0.96. The DL model yielded an AUC of 0.83 for LNM prediction and 0.91 for pathological subtype classification. The clinical model alone achieved an AUC of 0.94 for predicting LNM.
The integrated habitat-clinical model demonstrates improved predictive performance. Combining habitat analysis with clinical features offers a promising approach for the prediction of oral cancer.
The habitat-clinical integrated model may assist clinicians in performing accurate preoperative prognostic assessments in patients with oral cancer.
本研究旨在通过比较基于增强CT（CECT）的深度学习（DL）模型和栖息地分析模型，探索一种预测口腔鳞状细胞癌（OSCC）颈淋巴结转移（CLNM）和病理亚型的新方法。
对大连医科大学附属第二医院经石蜡病理确诊为OSCC的患者的CECT图像进行回顾性分析。所有患者均接受了原发性肿瘤切除和颈淋巴结清扫，共纳入132例病例。通过使用卷积神经网络（CNN）分析CECT图像中的感兴趣区域（ROI）来开发DL模型。对于栖息地分析，使用K均值聚类将ROI图像分割为3个区域，并通过全连接神经网络（FCNN）选择特征来构建模型。基于年龄、性别和肿瘤位置等9个临床特征构建了一个单独的临床模型。以LNM和病理亚型作为终点，使用混淆矩阵和受试者操作特征（ROC）曲线评估临床模型、DL模型、栖息地分析模型以及临床+栖息地联合模型的预测性能。
对于LNM预测，临床+栖息地联合模型的ROC曲线下面积（AUC）为0.97。对于病理亚型预测，AUC为0.96。DL模型在LNM预测中的AUC为0.83，在病理亚型分类中的AUC为0.91。单独的临床模型在预测LNM时的AUC为0.94。
综合栖息地-临床模型显示出更好的预测性能。将栖息地分析与临床特征相结合为口腔癌的预测提供了一种有前景的方法。
栖息地-临床综合模型可能有助于临床医生对口腔癌患者进行准确的术前预后评估。
