The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on ¹⁸F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4–13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7–12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

口咽鳞状细胞癌（OPSCC）的发病率迅速增加。在当前的临床试验中，经常使用疾病阶段和吸烟史来选择患者进行去强化治疗，但是这些功能缺乏足够的准确性来预测疾病的复发。我们的目的是开发影像学签名以评估OPSCC的早期反应并预测结局。方法：我们回顾性分析了162例同时放化疗的OPSCC患​​者，将其分为具有相似临床特征的单独训练和验证队列。鲁棒一致聚类方法被用于在空间上划分原发肿瘤和相关淋巴结到基于子区域（即，生境）¹⁸F-FDG PET和CT对比成像。我们提出了定量图像特征，以表征基线和中期治疗之间的生境和瘤周围/淋巴结组织的时间体积变化。评估了这些特征的可重复性。我们通过拟合L1正规化的Cox回归模型，开发了一种影像学特征来预测无进展生存期（PFS）。结果：我们确定了3个表型上不同的肿瘤内生境：代谢活跃和异质，增强和异质，代谢失活和同质。最终的Cox模型包含4种基于栖息地进化的特征。在这两个队列中，该影像学特征均明显优于传统影像学指标，包括用于预测PFS的治疗中期代谢肿瘤体积，C指数分别为0.72对0.67（训练）和0.66对0.56（验证）。影像学特征将患者分为高危组和低危组，其2年PFS率为59.1％对89.4％（危险比，4.4； 95％置信区间，1.4-13.4 [培训]）和61.4％对87.8％ （危险比4.6； 95％置信区间1.7-12.1 [验证]）。结论：拟议的影像学特征可以更准确地预测疾病的进展，并且，如果经过前瞻性验证，可以改善OPSCC患​​者对风险适应性治疗的选择。