Bogowicz Marta, Riesterer Oliver, Ikenberg Kristian, Stieb Sonja, Moch Holger, Studer Gabriela, Guckenberger Matthias, Tanadini-Lang Stephanie
Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Int J Radiat Oncol Biol Phys. 2017 Nov 15;99(4):921-928. doi: 10.1016/j.ijrobp.2017.06.002. Epub 2017 Jun 15.
This study aimed to predict local tumor control (LC) after radiochemotherapy of head and neck squamous cell carcinoma (HNSCC) and human papillomavirus (HPV) status using computed tomography (CT) radiomics.
HNSCC patients treated with definitive radiochemotherapy were included in the retrospective study approved by the local ethical commission (93 and 56 patients in the training and validation cohorts, respectively). Three hundred seventeen CT radiomic features, including those based on shape, intensity, texture, and wavelet transform, were calculated in the primary tumor region. Cox and logistic regression models were built to predict LC and HPV status, respectively. The best-performing features in the univariable analysis were included in the multivariable analysis after the exclusion of redundant features. The quality of the models was assessed using the concordance index (CI) for modeling of LC and receiver operating characteristics area under the curve (AUC) for HPV status prediction. The radiomics LC model was compared to a model incorporating clinical parameters (tumor stage, volume, and HPV status) and a mixed model.
A radiomic signature comprising 3 features was significantly associated with LC (CI = 0.75 and CI = 0.78), showing that tumors with a more heterogeneous CT density distribution are at risk for decreased LC. The addition of clinical parameters to the radiomics model slightly improved the model in the training cohort but not in the validation cohort. Another radiomic signature showed good performance in HPV status prediction (AUC = 0.85 and AUC = 0.78) and indicated that HPV-positive tumors have a more homogenous CT density distribution.
Heterogeneity of HNSCC tumor density, quantified by CT radiomics, is associated with LC after radiochemotherapy and HPV status.
本研究旨在利用计算机断层扫描（CT）影像组学预测头颈部鳞状细胞癌（HNSCC）放化疗后的局部肿瘤控制（LC）及人乳头瘤病毒（HPV）状态。
接受根治性放化疗的HNSCC患者纳入本回顾性研究，该研究经当地伦理委员会批准（训练队列和验证队列分别有93例和56例患者）。在原发肿瘤区域计算317个CT影像组学特征，包括基于形状、密度、纹理和小波变换的特征。分别构建Cox回归模型和逻辑回归模型预测LC和HPV状态。单变量分析中表现最佳的特征在排除冗余特征后纳入多变量分析。使用一致性指数（CI）评估LC建模的模型质量，使用曲线下受试者操作特征面积（AUC）评估HPV状态预测的模型质量。将影像组学LC模型与纳入临床参数（肿瘤分期、体积和HPV状态）的模型及混合模型进行比较。
包含3个特征的影像组学特征与LC显著相关（CI = 0.75和CI = 0.78），表明CT密度分布更不均匀的肿瘤有LC降低的风险。在影像组学模型中加入临床参数在训练队列中使模型略有改善，但在验证队列中未改善。另一个影像组学特征在HPV状态预测中表现良好（AUC = 0.85和AUC = 0.78），表明HPV阳性肿瘤的CT密度分布更均匀。
通过CT影像组学量化的HNSCC肿瘤密度异质性与放化疗后的LC及HPV状态相关。
