Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Radiother Oncol. 2019 May;134:119-126. doi: 10.1016/j.radonc.2019.01.022. Epub 2019 Feb 7.
The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. The ability to predict disease recurrence following CRT has the potential to inform personalized radiotherapy approaches currently being explored in novel clinical trials.
An IRB waiver was obtained for retrospective analysis of standard care MRIs from ASCC patients presenting between 2010 and 2014. Whole tumor 3D volume-of-interest (VOI) was outlined on T2-weighted (T2w) and diffusion weighted imaging (DWI) of the pre- and post-treatment scans. Independent imaging features most predictive of disease recurrence were added to the baseline clinico-pathological model and the predictive value of respective extended models was calculated using net reclassification improvement (NRI) algorithm. Cross-validation analysis was carried out to determine percentage error reduction with inclusion of imaging features to the baseline model for both endpoints.
Forty patients who underwent 1.5 T pelvic MRI at baseline and following completion of CRT were included. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p = 0 < 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model.
MRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.
本研究旨在评估标准护理磁共振成像(MRI)中分析图像异质性在预测肛门鳞癌(ASCC)患者放化疗(CRT)结局中的作用。预测 CRT 后疾病复发的能力有可能为目前正在新临床试验中探索的个性化放疗方法提供信息。
本研究对 2010 年至 2014 年间就诊的 ASCC 患者的标准护理 MRI 进行回顾性分析,该研究获得了 IRB 豁免。在治疗前后的 T2 加权(T2w)和弥散加权成像(DWI)上对整个肿瘤 3D 体积感兴趣区(VOI)进行勾画。将最能预测疾病复发的独立影像学特征添加到基线临床病理模型中,并使用净重新分类改善(NRI)算法计算各个扩展模型的预测价值。通过交叉验证分析,确定在基线模型中加入影像学特征对两个终点的百分比误差减少率。
本研究共纳入了 40 名在基线和 CRT 完成后接受 1.5T 盆腔 MRI 检查的患者。基线 MRI 异质性的两个特征(基线 T2w 能量和 DWI 变异系数)的组合对疾病复发最具预测性,结果具有显著的 NRI(p<0.001)。在交叉验证分析中,将影像学变量添加到基线模型中,主要终点的百分比误差减少了 34.8%,次要终点的百分比误差减少了 18.1%,证实了这一结果。
MRI 异质性分析除了临床分期外,还提供了预测肛门 SCC CRT 结局的补充信息,值得在更大的数据集进行验证。