Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Oral Oncol. 2019 Aug;95:178-186. doi: 10.1016/j.oraloncology.2019.06.020. Epub 2019 Jun 26.
The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients.
The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS.
Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated):0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model.
For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.
本研究旨在探讨定量 CT 图像生物标志物（IBMs）是否可以改善仅包含经典预后因素的局部控制（LC）、区域控制（RC）、无远处转移生存（DMFS）和无病生存（DFS）预测模型，用于头颈部癌症（HNC）患者。
该队列纳入了分别在训练和验证分析中的 240 例和 204 例 HNC 患者。前瞻性地对临床变量进行评分，并从计划 CT 图像中提取原发肿瘤和淋巴结的 IBM。根据临床特征、IBM 以及两者的结合，基于多变量 Cox 比例风险分析，创建用于 LC、RC、DMFS 和 DFS 的临床、IBM 和联合模型。
多变量分析中确定的临床变量包括肿瘤部位、世界卫生组织（WHO）表现评分、肿瘤分期和年龄。描述肿瘤体积和不规则形状的边界框体积、表示放射学异质性的 IBM 相关系数以及显示淋巴结之间距离的 LN 长轴长度等被纳入 IBM 模型。IBM LC、RC、DMFS 和 DFS 模型的性能（验证中的 C 指数：0.62、0.80、0.68 和 0.65）与临床模型相当（0.62、0.76、0.70 和 0.66）。包括临床特征和 IBM 的联合 DFS 模型（0.70）的性能明显优于临床模型。在验证队列中，与临床模型相比，用联合模型分层的患者在 LC、RC 和 DFS 方面的风险组之间的差异更大。对于 DMFS，差异与临床模型相似。
对于预测 HNC 治疗结果，IBM 的表现与临床变量一样好或略好。
