Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, Ohio.
School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma.
Int J Radiat Oncol Biol Phys. 2024 Aug 1;119(5):1569-1578. doi: 10.1016/j.ijrobp.2024.02.021. Epub 2024 Mar 10.
Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.
The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors.
The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index.
This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.
鉴于现有的下颌骨放射性骨坏死（ORN）正常组织并发症概率估计模型存在局限性，本研究旨在通过利用数据的结构特性丰富统计推断，并通过纳入下颌骨整体辐射剂量分布的无监督学习分析，为 ORN 风险评估提供一种临床可靠的模型。
该分析基于 2005 年至 2015 年期间在德克萨斯大学 MD 安德森癌症中心接受治疗的 1259 例头颈部癌症患者的回顾性数据进行。在至少 12 个月的治疗后随访期间，该队列中有 173 例（13.7%）患者发生 ORN（I 至 IV 级）。使用 K-均值聚类方法识别这些患者的下颌骨剂量-体积直方图（DVH）的（结构）聚类。使用软间隔支持向量机确定聚类边界并划分剂量-体积空间。根据发生率和其他临床危险因素计算每个剂量-体积区域发生 ORN 的风险。
K-均值聚类方法在 DVH 中识别出 6 个聚类。基于前 5 个聚类，软间隔支持向量机将剂量-体积空间划分为具有不同风险指数的不同区域。第六个聚类完全与其他聚类重叠；该聚类区域由其边界确定。对于每个区域，根据预处理牙拔除状态（ORN 的一个与剂量无关的显著非剂量相关危险因素）报告 ORN 发生率作为相应的风险指数。
本研究通过对大规模数据集进行无监督学习分析，评估头颈部癌症患者下颌骨 ORN 的风险。结果提供了一种基于整个 DVH 和预处理牙拔除状态的 ORN 风险评估工具，以及在不同风险水平下剂量优化的一系列限制。
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