Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer
Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer
复制标题
基于放射组学的局部晚期直肠癌新辅助治疗无反应的治疗前预测。
DOI：
10.1245/s10434-019-07300-3
复制
发表时间：
2019-06-01
影响因子：
3.7
通讯作者：
Tian, Jie
中科院分区：
文献类型：
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作者：
Zhou, Xuezhi;Yi, Yongju;Tian, Jie
目的探讨局部进展期直肠癌(LARC)患者治疗前多参数磁共振成像(MRI)放射学特征是否可用于预测新辅助治疗无反应。所有患者在接受新辅助治疗前都接受了T1加权、T2加权、弥散加权和对比增强的T1加权MRI扫描。我们从每个患者的治疗前多参数磁共振图像中提取了2424个放射组学特征。采用Wilcoxon秩和检验、Spearman相关分析、最小绝对收缩和选择算子回归等方法进行特征选择，通过多因素Logistic回归分析建立基于MRI的多参数放射学模型。对所有的单通道MRI数据进行特征选择和多因素Logistic回归分析，以建立四个单通道放射学模型。结果基于16个特征的多参数、基于MRI的放射组学模型在两组人群中均有较好的预测效果(P
ObjectiveThe aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predicting non-response to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC).MethodsWe retrospectively enrolled 425 patients with LARC [allocated in a 3:1 ratio to a primary (n=318) or validation (n=107) cohort] who received neoadjuvant therapy before surgery. All patients underwent T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI scans before receiving neoadjuvant therapy. We extracted 2424 radiomic features from the pretherapeutic, multiparametric MR images of each patient. The Wilcoxon rank-sum test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression were successively performed for feature selection, whereupon a multiparametric MRI-based radiomic model was established by means of multivariate logistic regression analysis. This feature selection and multivariate logistic regression analysis was also performed on all single-modality MRI data to establish four single-modality radiomic models. The performance of the five radiomic models was evaluated by receiver operating characteristic (ROC) curve analysis in both cohorts.ResultsThe multiparametric, MRI-based radiomic model based on 16 features showed good predictive performance in both the primary (p