Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Switzerland.
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht Comprehensive Cancer Centre, Maastricht University Medical Centre, Maastricht, The Netherlands.
Radiother Oncol. 2017 Dec;125(3):385-391. doi: 10.1016/j.radonc.2017.10.023. Epub 2017 Nov 6.
This study investigated an association of post-radiochemotherapy (RCT) PET radiomics with local tumor control in head and neck squamous cell carcinoma (HNSCC) and evaluated the models against two radiomics software implementations.
649 features, available in two radiomics implementations and based on the same definitions, were extracted from HNSCC primary tumor region in 18F-FDG PET scans 3 months post definitive RCT (training cohort n = 128, validation cohort n = 50) and compared using the intraclass correlation coefficient (ICC). Local recurrence models were trained, separately for both implementations, using principal component analysis (PCA) and the least absolute shrinkage and selection operator. The reproducibility of the concordance indexes (CI) in univariable Cox regression for features preselected in PCA and the final multivariable models was investigated using respective features from the other implementation.
Only 80 PET radiomic features yielded ICC > 0.8 in the comparison between the implementations. The change of implementation caused high variability of CI in the univariable analysis. However, both final multivariable models performed equally well in the training and validation cohorts (CI > 0.7) independent of radiomics implementation.
The two post-RCT PET radiomic models, based on two different software implementations, were prognostic for local tumor control in HNSCC. However, 88% of the features was not reproducible between the implementations.
本研究旨在探讨放化疗后（RCT）PET 影像组学与头颈部鳞状细胞癌（HNSCC）局部肿瘤控制之间的相关性，并评估这些模型与两种影像组学软件实现之间的关系。
从 18F-FDG PET 扫描中提取 18F-FDG PET 扫描后 3 个月（训练队列 n=128，验证队列 n=50）根治性 RCT 后 HNSCC 原发肿瘤区域的 649 个特征，这两种影像组学实现方式基于相同的定义，采用组内相关系数（ICC）进行比较。使用主成分分析（PCA）和最小绝对收缩和选择算子（LASSO），分别为两种实现方式训练局部复发模型。使用 PCA 中预选特征和最终多变量模型的相应特征，研究了在单变量 Cox 回归中一致性指数（CI）的可重复性。
只有 80 个 PET 影像组学特征在两种实现方式的比较中 ICC>0.8。实施方式的改变导致单变量分析中 CI 的变化很大。然而，两种最终的多变量模型在训练队列和验证队列中的表现都同样良好（CI>0.7），与影像组学的实施方式无关。
基于两种不同软件实现的两种 RCT 后 PET 影像组学模型对头颈部鳞状细胞癌局部肿瘤控制具有预测价值。然而，88%的特征在两种实现方式之间不可重现。
