Pfaehler Elisabeth, Schindele Andreas, Dierks Alexander, Busse Cornelius, Brumberg Joachim, Kübler Alexander C, Buck Andreas K, Linz Christian, Lapa Constantin, Brands Roman C, Kertels Olivia
Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, Germany.
Sci Rep. 2025 May 20;15(1):17475. doi: 10.1038/s41598-025-02305-3.
Oral Squamous Cell Carcinoma (OSCC) represents more than 90% of oral cancers. The usefulness of radiomic features extracted from PET images of OSCC patients to predict tumor characteristics such as primary tumor stage (T-stage), or tumor grade has not been investigated yet. In this prospective study, 112 patients with newly diagnosed, treatment-naïve OSCC were included. Tumor segmentation was performed using three strategies, the majority vote of these segmentations was used to calculate 445 radiomic features. Features instable over segmentation methods and features highly correlated with volume, SUV, and SUV were eliminated. A Random Forest classifier was trained to predict T-stage, tumor grade, lymph node involvement, and tumor recurrence. Stratified 10-fold cross-validation was performed. Evaluation metrics such as accuracy and area under the curve (AUC) were reported. SHAP dependence plots were generated to understand classifier decisions. The classifier reached a mean cross-validation AUC of 0.83 for predicting T-stage, an AUC of 0.56 for the grading of the primary tumor, a mean AUC of 0.64 for lymph node involvement, and a mean AUC of 0.63 for recurrence. In patients with newly-diagnosed OSCC, radiomics might have some potential to predict T-stage. These results need to be validated in a larger patient cohort.
口腔鳞状细胞癌（OSCC）占口腔癌的90%以上。从OSCC患者的PET图像中提取的放射组学特征用于预测肿瘤特征（如原发肿瘤分期（T分期）或肿瘤分级）的有效性尚未得到研究。在这项前瞻性研究中，纳入了112例新诊断的、未接受过治疗的OSCC患者。使用三种策略进行肿瘤分割，这些分割的多数投票结果用于计算445个放射组学特征。消除了在分割方法上不稳定的特征以及与体积、SUV和SUV高度相关的特征。训练了一个随机森林分类器来预测T分期、肿瘤分级、淋巴结受累情况和肿瘤复发情况。进行了分层10折交叉验证。报告了诸如准确率和曲线下面积（AUC）等评估指标。生成了SHAP依赖图以了解分类器的决策。该分类器预测T分期的平均交叉验证AUC为0.83，原发肿瘤分级的AUC为0.56，淋巴结受累的平均AUC为0.64，复发的平均AUC为0.63。在新诊断的OSCC患者中，放射组学可能具有预测T分期的一些潜力。这些结果需要在更大的患者队列中进行验证。
