van Timmeren Janna E, Leijenaar Ralph T H, van Elmpt Wouter, Reymen Bart, Lambin Philippe
a Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology , Maastricht University Medical Centre (MUMC) , Maastricht , The Netherlands.
Acta Oncol. 2017 Nov;56(11):1537-1543. doi: 10.1080/0284186X.2017.1350285. Epub 2017 Aug 22.
Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment adaptation. The aims of this study were to (1) describe and investigate a methodology for feature selection of a longitudinal radiomics approach (2) investigate which time-point during treatment is potentially useful for early treatment response assessment.
For 90 NSCLC patients CBCT scans of the first two fractions of treatment (considered as 'test-retest' scans) were analyzed, as well as weekly CBCT images. One hundred and sixteen radiomic features were extracted from the GTV of all scans and subsequently absolute and relative differences were calculated between weekly CBCT images and the CBCT of the first fraction. Test-retest scans were used to determine the smallest detectable change (C = 1.96 * SD) allowing for feature selection by choosing a minimum number of patients for which a feature should change more than 'C' to be considered as relevant. Analysis of which features change at which moment during treatment was used to investigate which time-point is potentially relevant to extract longitudinal radiomics information for early treatment response assessment.
A total of six absolute delta features changed for at least ten patients at week 2 of treatment and increased to 61 at week 3, 79 at week 4 and 85 at week 5. There was 93% overlap between features selected at week 3 and the other weeks.
This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.
锥形束CT（CBCT）扫描通常每天进行，用于非小细胞肺癌（NSCLC）患者的定位验证。利用放射组学得出的定量信息可能有助于（早期）治疗调整。本研究的目的是：（1）描述并研究一种纵向放射组学方法的特征选择方法；（2）研究治疗期间的哪个时间点可能有助于早期治疗反应评估。
分析了90例NSCLC患者治疗前两部分的CBCT扫描（视为“重测”扫描）以及每周的CBCT图像。从所有扫描的大体肿瘤体积（GTV）中提取了116个放射组学特征，随后计算了每周CBCT图像与第一部分CBCT之间的绝对和相对差异。重测扫描用于确定最小可检测变化（C = 1.96 *标准差），通过选择特征变化超过“C”的患者数量最少的特征来进行特征选择，这些特征被视为相关特征。分析治疗期间哪些特征在哪个时刻发生变化，以研究哪个时间点可能与提取纵向放射组学信息用于早期治疗反应评估相关。
共有6个绝对差值特征在治疗第2周至少有10名患者发生变化，在第3周增加到61个，第4周为79个，第5周为85个。第3周选择的特征与其他周之间有93%的重叠。
本研究描述了一种纵向放射组学的特征选择方法，该方法能够选择可重复的差值放射组学特征，这些特征因其在治疗期间的变化而具有信息价值，可能用于自适应放疗的治疗决策。尽管如此，所选差值放射组学特征的预后价值应在未来的研究中进行调查。
