Accurate identifying head and neck squamous cell cancer (HNSCC) patients at high risk of local persistence/recurrence (P/R) is of importance for personalized patient management. Here we developed a multi-objective, multi-classifier radiomics model for early HNSCC local P/R prediction based on post-treatment PET/CT scans and clinical data.

We retrospectively identified 328 individuals (69 patients have local P/R) with HNSCC treated with definitive radiation therapy at our institution. The median follow-up from treatment completion to the first surveillance PET/CT imaging was 114 days (range: 82-159 days). Post-treatment PET/CT scans were reviewed and contoured for all patients. For each imaging modality, we extracted 257 radiomic features to build a multi-objective radiomics model with sensitivity, specificity, and feature sparsity as objectives for model training. Multiple representative classifiers were combined to construct the predictive model. The output probabilities of models built with features from various modalities were fused together to make the final prediction.

We built and evaluated three single-modality models and two multi-modality models. The combination of PET, CT, and clinical data in the multi-objective, multi-classifier radiomics model trended towards the best prediction performance, with a sensitivity of 93%, specificity of 83%, accuracy of 85%, and AUC of 0.94.

Our study demonstrates the feasibility of employing a multi-objective, multi-classifier radiomics model with PET/CT radiomic features and clinical data to predict outcomes for patients with HNSCC after radiation therapy. The proposed prediction model shows the potential to detect cancer local P/R early after radiation therapy.

准确识别局部持续/复发 (P/R) 高风险的头颈部鳞状细胞癌 (HNSCC) 患者对于个性化患者管理非常重要。在这里，我们开发了一个基于治疗后 PET/CT 扫描和临床数据的早期 HNSCC 局部 P/R 预测的多目标、多分类器放射组学模型。

我们回顾性地确定了在我们机构接受根治性放射治疗的 328 名 HNSCC 个体（69 名患者有局部 P/R）。从治疗完成到首次监测 PET/CT 成像的中位随访时间为 114 天（范围：82-159 天）。对所有患者的治疗后 PET/CT 扫描进行了回顾和轮廓分析。对于每种成像模态，我们提取了 257 个放射组学特征，构建了一个以敏感性、特异性和特征稀疏性为模型训练目标的多目标放射组学模型。将多个代表性分类器组合起来构建预测模型。将使用来自各种模式的特征构建的模型的输出概率融合在一起以进行最终预测。

我们建立并评估了三个单模态模型和两个多模态模型。多目标、多分类放射组学模型中 PET、CT 和临床数据的组合趋向于最佳预测性能，灵敏度为 93%，特异性为 83%，准确度为 85%，AUC 为 0.94。

我们的研究证明了采用具有 PE​​T/CT 影像组学特征和临床数据的多目标、多分类影像组学模型来预测 HNSCC 患者放射治疗后预后的可行性。所提出的预测模型显示了在放射治疗后早期检测癌症局部 P/R 的潜力。