Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
Eur J Radiol. 2017 Dec;97:8-15. doi: 10.1016/j.ejrad.2017.10.009. Epub 2017 Oct 9.
PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients.
348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ).
The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δ<5%. The overall influence (δ) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56).
The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future.
基于 PET 的放射组学特征在预测遗传数据方面显示出巨大的潜力。然而，各种实验参数会影响特征提取流程，因此，本研究旨在探讨实验设置如何影响放射组学特征在预测非小细胞肺癌（NSCLC）患者体细胞突变状态中的性能。
我们的分析纳入了 348 名接受过体细胞突变检测和诊断 PET 成像的 NSCLC 患者。对不同体素大小、滤波器和 bin 宽度的放射组学特征提取进行了分析。共评估了 66 个放射组学特征。使用受试者工作特征曲线下面积（AUC）评估特征预测突变状态的性能。通过最小和最大 AUC 之间的相对差异（δ）来量化实验参数对特征可预测性的影响。
绝大多数特征（n=56，85%）对 EGFR 突变状态具有显著的预测能力（AUC≥0.61）。29 个放射组学特征显著预测 EGFR 突变，并且对实验设置具有鲁棒性，δ<5%。所有特征的总体影响（δ）分别为体素大小、滤波器和 bin 宽度的 5%至 15%。对于所有特征，没有一个实验设计可以预测 KRAS+和 KRAS-（AUC≤0.56）。
29 个放射组学特征的预测能力对实验设置的选择具有鲁棒性；然而，对于所有其他特征，需要仔细选择这些设置。所研究的处理方法的综合影响可能很大，必须予以考虑。未来应研究优化设置，以最大限度地提高个体放射组学特征的预测性能。
