Background: We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. Method: We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. Results: A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. Conclusions and Relevance: We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.
背景：我们近期开发了一种基于基因表达的HOT评分，用于识别头颈部鳞状细胞癌（HNSCC）的热/冷表型，该表型与免疫治疗反应相关。本研究旨在探讨基于计算机断层扫描（CT）的影像组学特征能否有效区分HNSCC的热表型与冷表型。方法：研究纳入癌症基因组图谱（TCGA）数据库的113例患者及皮提耶-萨尔佩特里尔医院集团（GHPS）的20例HNSCC患者，所有患者均具备治疗前CT影像数据。采用HOT评分计算所有患者的热/冷表型。使用IBEX软件（版本4.11.9，访问日期2020年3月30日）从两组数据的肿瘤勾画区域提取影像组学特征，并通过计算组内相关系数（ICC）筛选稳定性特征。在TCGA数据集训练并测试机器学习分类模型，并在GHPS队列中采用受试者工作特征曲线下面积（AUC）进行验证。结果：共筛选出144个ICC>0.9的影像组学特征。包含这些特征的XGBoost模型在GHPS验证数据集中表现出最佳预测性能（AUC=0.86）。结论与意义：本研究构建的影像组学模型能有效捕捉HNSCC的整体热/冷表型。这种无创检测方法有助于识别可能从免疫治疗中获益的HNSCC患者。
肿瘤(癌症)患者之家