文章：

利用PET影像组学机器学习模型预测早期口腔鳞状细胞癌后续转移能力的评估

Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models

原文发布日期：5 November 2025

DOI: 10.3390/cancers17213573

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: Oral squamous cell carcinoma (OSCC) carries a risk of late metastasis not only in advanced stages but also in early stages. In this study, we built and tested radiomics-based machine learning (ML) models for predicting the risk of metastasis from early OSCC on18F-FDG positron emission tomography (PET).Methods: Patients diagnosed with T1 or T2 squamous cell carcinoma who underwent a preoperative18F-FDG PET-CT examination at a single institution between 2016 and December 2022 were included in this retrospective study. The presence or absence of late cervical lymph node metastasis was confirmed for all patients. Among the radiomics features extracted from the images, we selected those that were useful for predicting late metastasis and used them to create ML models. We then verified the prediction accuracy of the models.Results: A total of 109 subjects were included, of which 31 had late lymph node metastasis and 78 were without metastasis. The most accurate ML model created using radiomics features selected from the subject cases had an area under the curve of 0.977 and accuracy of 87.5%.Conclusions: We confirmed that ML models using radiomics features extracted from PET images can be useful for predicting late metastasis in patients with early-stage OSCC.

摘要翻译： 

背景/目的：口腔鳞状细胞癌（OSCC）不仅在晚期阶段，甚至在早期阶段也存在晚期转移的风险。本研究基于¹⁸F-FDG正电子发射断层扫描（PET）图像，构建并测试了用于预测早期OSCC转移风险的放射组学机器学习（ML）模型。 方法：本回顾性研究纳入了2016年至2022年12月期间在同一机构接受术前¹⁸F-FDG PET-CT检查、诊断为T1或T2期鳞状细胞癌的患者。所有患者均经病理证实是否存在晚期颈部淋巴结转移。我们从图像中提取放射组学特征，筛选出对预测晚期转移具有价值的特征，并以此构建ML模型，随后验证了模型的预测准确性。 结果：共纳入109例患者，其中31例发生晚期淋巴结转移，78例未发生转移。基于病例筛选的放射组学特征构建的最优ML模型曲线下面积为0.977，预测准确率达87.5%。 结论：本研究证实，利用PET图像提取的放射组学特征构建的ML模型，可有效预测早期OSCC患者的晚期转移风险。

原文链接：

Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models