基于 CT 的头颈部鳞状细胞癌（癌旁）组织影像组学特征预测同期放化疗后局部区域复发和远处转移。

Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy.

机构信息

The D-lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.

出版信息

PLoS One. 2020 May 22;15(5):e0232639. doi: 10.1371/journal.pone.0232639. eCollection 2020.

DOI:10.1371/journal.pone.0232639

PMID:32442178

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7244120/

Abstract

INTRODUCTION

In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis.

METHODS

Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and "Big Data To Decide" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF.

RESULTS

A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods.

CONCLUSION

Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.

摘要

简介

本研究旨在探讨放射组学在预测接受放化疗的 III 期和 IV 期头颈部鳞癌（HNSCC）患者总生存期（OS）、局部区域复发（LRR）和远处转移（DM）中的作用。我们假设（肿瘤）周围组织的放射组学分析可检测到周围组织侵犯，从而提示更高的局部区域复发和远处转移的可能性。

方法

本研究使用了两个综合数据源：荷兰癌症协会数据库（Alp 7072，DESIGN）和“大数据决策”（BD2Decide）。在对比增强 CT 上勾画大体肿瘤体积（GTV）。使用 RadiomiX Discovery 工具箱（比利时列日 OncoRadiomics）提取放射组学特征。收集临床患者特征，如年龄、性别、表现状态等。选择 Cox 比例风险回归和随机生存森林（RSF）两种机器学习方法，因为它们能够处理删失数据。基于在训练数据集中的外部验证数据的单变量 Cox 回归/RSF 分析中的显著性，生成多变量临床和放射组学 Cox/RSF 模型。根据 Cox 的危险比降低和 RSF 的相对重要性选择特征。

结果

本研究共纳入 444 例接受放疗计划 CT 扫描的患者：301 例头颈部鳞状细胞癌（HNSCC）患者为训练队列（DESIGN），143 例为验证队列（BD2DECIDE）。我们发现，表现最佳的模型是一个临床模型，它能够预测口咽癌患者的远处转移，外部验证的 C 指数分别为 0.74 和 0.65，分别采用 RSF 和 Cox 模型。基于肿瘤周围的放射组学预测模型在外部验证中的表现不佳，使用两种特征选择和模型生成方法的 C 指数值范围为 0.32 至 0.61。