Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
Eur Radiol. 2021 Sep;31(9):7031-7038. doi: 10.1007/s00330-021-07724-0. Epub 2021 Feb 10.
To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data.
Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W/T2W/ADC/SUV/TLG/CT) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset.
When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture).
In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research.
• Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.
通过使用预处理多参数 PET 和 MRI 数据，研究与肿瘤全局特征相比，量化局部肿瘤异质性是否可以提高预测放化疗反应的能力。
回顾性分析 61 例接受放化疗的局部晚期直肠癌患者，在基线时使用 MRI 和 FDG-PET/CT 进行分期。在 MRI 和 PET/CT 扫描上对全肿瘤体积进行分割，从这些扫描中计算出肿瘤全局特征（T2W/T2W/ADC/SUV/TLG/CT）和局部纹理特征（来自局部熵/均值/标准差图的直方图特征）。将这些特征集与临床基线参数（如年龄/性别/TN 分期）相结合，构建多变量预测模型，以预测放化疗的良好（Mandard TRG1-2）与不良（Mandard TRG3-5）反应。使用 bootstrap 进行的留一法交叉验证（LOOCV）用于估计“独立”数据集的性能。
仅使用影像学特征时，局部纹理特征的 AUC 为 0.81，而肿瘤全局特征的 AUC 为 0.74。经过内部交叉验证（LOOCV），预测良好反应的 AUC 以临床基线变量+肿瘤全局特征的组合最高（AUC=0.83），与基线+局部纹理的 AUC=0.79 相比，与所有组合（基线+全局+局部纹理）的 AUC=0.76 相比。
在基于影像学的预测模型中，与肿瘤全局特征相比，局部纹理分析具有预测反应的潜在附加价值。然而，当与临床基线参数（如 cTN 分期）相结合时，局部纹理分析的附加价值似乎有限。将基线变量与定量影像学参数相结合来预测反应的总体性能很有希望，值得进一步研究。
与肿瘤全局特征相比，在放化疗前 FDG-PET/CT 和 MRI 上对局部肿瘤纹理进行量化具有预测直肠癌放化疗反应的潜在附加价值。
然而，当与临床基线参数（如 cTN 分期）相结合时，局部纹理相对于肿瘤全局特征的附加价值是有限的。
结合临床基线变量与全局定量肿瘤特征的最佳模型的预测性能令人鼓舞（AUC 0.83），值得在更大规模上进一步研究这一方向。
