局部晚期宫颈癌（LACC）和肛门和咽喉鳞状细胞癌（ASCC和OPSCC）大多由致癌的人乳头瘤病毒（HPV）引起。本论文基于从术前[18F] - 荧光脱氧葡萄糖正电子发射断层扫描（[18F]-FDG PET）图像中提取的临床、生物和放射组学特征，开发了机器学习（ML）模型，用于预测HPV引发的癌症患者的生存情况。为此，使用了来自五个机构的队列：包括Gustave Roussy Campus Cancer（Center 1）的104名LACC患者队列和Leeds Teaching Hospitals NHS Trust（Center 2）的90名患者队列，包括Institut du Cancer de Montpellier（Center 3）的66名ASCC患者和Oslo University Hospital（Center 4）的67名患者，以及University Hospital of Zurich（Center 5）的45名OPSCC患者数据集。从基线[18F]-FDG PET图像中提取放射组学特征。使用ComBat技术减轻扫描仪内差异。采用修正的一致性嵌套交叉验证方法进行特征选择和超参数调整，以使用协调的成像特征和/或临床和生物变量作为输入来预测无进展生存（PFS）和总生存（OS）。每个模型在Center 1和Center 3队列上进行训练和优化，并在Center 2、Center 4和Center 5队列上进行测试。基于放射组学特征的CoxNet模型在测试集上的PFS和OS的C-index值分别为0.75和0.78，0.76、0.74和0.75。较生物临床模型，基于放射组学特征的模型表现出更好的性能，且将放射组学和生物临床变量组合在一起并未改进性能。在大多数测试配置中，基于代谢肿瘤体积（MTV）的模型获得较低的C-index值，但在时间依赖的AUC（td-AUC）方面的性能相当。结果证明了在多中心多构造数据上验证HPV引发病变患者PET图像特征的预测响应的可能性。© 2023. 作者（们）独家授权Springer-Verlag GmbH Germany，Springer Nature的一部分。

Locally advanced cervical cancer (LACC) and anal and oropharyngeal squamous cell carcinoma (ASCC and OPSCC) are mostly caused by oncogenic human papillomaviruses (HPV). In this paper, we developed machine learning (ML) models based on clinical, biological, and radiomic features extracted from pre-treatment fluorine-18-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) images to predict the survival of patients with HPV-induced cancers. For this purpose, cohorts from five institutions were used: two cohorts of patients treated for LACC including 104 patients from Gustave Roussy Campus Cancer (Center 1) and 90 patients from Leeds Teaching Hospitals NHS Trust (Center 2), two datasets of patients treated for ASCC composed of 66 patients from Institut du Cancer de Montpellier (Center 3) and 67 patients from Oslo University Hospital (Center 4), and one dataset of 45 OPSCC patients from the University Hospital of Zurich (Center 5). Radiomic features were extracted from baseline [18F]-FDG PET images. The ComBat technique was applied to mitigate intra-scanner variability. A modified consensus nested cross-validation for feature selection and hyperparameter tuning was applied on four ML models to predict progression-free survival (PFS) and overall survival (OS) using harmonized imaging features and/or clinical and biological variables as inputs. Each model was trained and optimized on Center 1 and Center 3 cohorts and tested on Center 2, Center 4, and Center 5 cohorts. The radiomic-based CoxNet model achieved C-index values of 0.75 and 0.78 for PFS and 0.76, 0.74, and 0.75 for OS on the test sets. Radiomic feature-based models had superior performance compared to the bioclinical ones, and combining radiomic and bioclinical variables did not improve the performances. Metabolic tumor volume (MTV)-based models obtained lower C-index values for a majority of the tested configurations but quite equivalent performance in terms of time-dependent AUCs (td-AUC). The results demonstrate the possibility of identifying common PET-based image signatures for predicting the response of patients with induced HPV pathology, validated on multi-center multiconstructor data.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.