Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, the Netherlands.
Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center (AUMC), Amsterdam, the Netherlands.
Eur J Radiol. 2021 Jun;139:109701. doi: 10.1016/j.ejrad.2021.109701. Epub 2021 Apr 8.
New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors.
177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup.
A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction.
Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS.
需要新的标志物来预测口咽鳞状细胞癌（OPSCC）患者的放化疗反应。本研究评估了磁共振（MR）放射组学预测放化疗后局部区域控制（LRC）和总生存（OS）的能力，并旨在确定这是否对传统临床结果预测因素有附加价值。
本研究共纳入 177 例 OPSCC 患者。在放化疗前采集的 T1 加权对比增强 MRI 上从原发肿瘤区域提取放射组学特征。使用临床变量（临床模型）、放射组学特征（放射组学模型）或临床和放射组学特征组合（联合模型）创建逻辑回归模型，以预测治疗后 2 年的 LRC 和 OS。使用曲线下面积（AUC）评估模型性能，使用 500 次 bootstrap 迭代计算 95%置信区间。所有分析均在总人群和 HPV 阴性肿瘤亚组中进行。
与临床模型相比（LRC：0.607 [0.594-0.620]，OS：0.708 [0.697-0.719]），联合模型预测治疗结果的 AUC 更高（LRC：0.745 [0.734-0.757]，OS：0.744 [0.735-0.753]）。放射组学模型对 LRC 的预测性能与联合模型相当（AUC：0.740 [0.729-0.750]），但对 OS 预测的性能则不然（AUC：0.654 [0.646-0.662]）。在 HPV 阴性患者中，所有模型的性能均不足，LRC 的 AUC 范围为 0.587 至 0.660，OS 预测的 AUC 范围为 0.559 至 0.600。
与仅基于临床变量的模型相比，包含 OPSCC 磁共振图像衍生的临床变量和肿瘤放射组学特征的预测模型能更好地预测放化疗后的 LRC。对于 OS 的预测，包含临床变量的预测模型比仅基于放射组学特征的模型表现更好。
