■肛门鳞状细胞癌(SCCA)在放化疗(CRT)后可复发。治疗反应的早期预测对于个体化治疗至关重要。关于放射性生物标志物的现有数据是有限且矛盾的。我们对四项前瞻性试验进行了个体患者数据荟萃分析(IPM),研究了在2至3周的CRT扩散加权(DW)磁共振成像(MRI)是否预测SCCA治疗失败。
■来自四项试验的个体患者数据,包括基线和CRT期间的配对DW-MRI,被组合成一个数据集。使用逻辑回归评估ADC体积直方图参数与治疗失败(局部和任何失败)之间的关联。预定义的分析包括将患者分类为所描绘的肿瘤体积的平均ADC的变化高于和低于20%。
■该研究发现,在所有142名患者中,11.3%(n=16)的局部治疗失败。ADC平均变化<20%和>20%导致局部故障率为16.7%和8.0%,分别。然而,没有其他基于ADC的直方图参数与局部区域或任何治疗失败相关.
■DW-MRI标准参数,作为一种孤立的生物标志物,在此IPM中,未发现与SCCA治疗失败的几率增加相关。放射学生物标志物调查涉及多个步骤并且可能导致异构数据。在未来,在大型前瞻性试验中纳入放射学生物标志物对于减少异质性和最大化学习至关重要.
UNASSIGNED: Squamous cell carcinoma of the anus (SCCA) can recur after chemoradiotherapy (CRT). Early prediction of treatment response is crucial for individualising treatment. Existing data on radiological biomarkers is limited and contradictory. We performed an individual patient data meta-analysis (IPM) of four prospective trials investigating whether diffusion-weighted (DW) magnetic resonance imaging (MRI) in weeks two to three of CRT predicts treatment failure in SCCA.
UNASSIGNED: Individual patient data from four trials, including paired DW-MRI at baseline and during CRT, were combined into one dataset. The association between ADC volume histogram parameters and treatment failure (locoregional and any failure) was assessed using logistic regression. Pre-defined analysis included categorising patients into a change in the mean ADC of the delineated tumour volume above and below 20%.
UNASSIGNED: The study found that among all included 142 patients, 11.3 % (n = 16) had a locoregional treatment failure. An ADC mean change of <20 % and >20 % resulted in a locoregional failure rate of 16.7 % and 8.0 %, respectively. However, no other ADC-based histogram parameter was associated with locoregional or any treatment failure.
UNASSIGNED: DW-MRI standard parameters, as an isolated biomarker, were not found to be associated with increased odds of treatment failure in SCCA in this IPM. Radiological biomarker investigations involve multiple steps and can result in heterogeneous data. In future, it is crucial to include radiological biomarkers in large prospective trials to minimize heterogeneity and maximize learning.