Department of Radiological Sciences, University of California, Los Angeles, CA, United States of America. Physics and Biology in Medicine IDP, University of California, Los Angeles, CA, United States of America.
Phys Med Biol. 2020 Aug 27;65(17):175006. doi: 10.1088/1361-6560/ab9e58.
The objective of this study was to explore radiomics features from longitudinal diffusion-weighted MRIs (DWIs) for pathologic treatment effect prediction in patients with localized soft tissue sarcoma (STS) undergoing hypofractionated preoperative radiotherapy (RT). Thirty patients with localized STS treated with preoperative hypofractionated RT were recruited to this longitudinal imaging study. DWIs were acquired at three time points using a 0.35 T MRI-guided radiotherapy system. Treatment effect score (TES) was obtained from the post-surgery pathology as a surrogate of treatment outcome. Patients were divided into two groups based on TES. Response prediction was first performed using a support vector machine (SVM) with only mean apparent diffusion coefficient (ADC) or delta ADC to serve as the benchmark. Radiomics features were then extracted from tumor ADC maps at each of the three time points. Logistic regression and SVM were constructed to predict the TES group using features selected by univariate analysis and sequential forward selection. Classification performance using SVM with features from different time points and with or without delta radiomics were evaluated. Prediction performance using only mean ADC or delta ADC was poor (area under the curve (AUC) < 0.7). For the radiomics study using features from all time points and corresponding delta radiomics, SVM significantly outperformed logistic regression (AUC of 0.91 ± 0.05 v.s. 0.85 ± 0.06). Prediction AUC values using single or multiple time points without delta radiomics were all below 0.74. Including delta radiomics of mid- or post-treatment relative to the baseline drastically boosted the prediction. In this work, an SVM model was built to predict the TES using radiomics features from longitudinal DWI. Based on this study, we found that use of mean ADC, delta ADC, or radiomics features alone was not sufficient for response prediction, and including delta radiomics features of mid- or post-treatment relative to the baseline can optimize the prediction of TES, a pathologic and clinical endpoint.
本研究的目的是探索来自纵向弥散加权磁共振成像（DWI）的放射组学特征，以预测接受局部软组织肉瘤（STS）术前分割放疗（RT）的患者的病理治疗效果。本纵向影像研究共纳入 30 名接受术前分割 RT 治疗的局部 STS 患者。在使用 0.35T MRI 引导放疗系统的三个时间点采集 DWI。从术后病理中获得治疗效果评分（TES）作为治疗结果的替代物。根据 TES 将患者分为两组。首先使用仅平均表观扩散系数（ADC）或 Delta ADC 作为基准的支持向量机（SVM）进行响应预测。然后从三个时间点的肿瘤 ADC 图中提取放射组学特征。使用单变量分析和逐步向前选择选择的特征构建逻辑回归和 SVM，以预测 TES 组。使用来自不同时间点的特征的 SVM 分类性能以及是否使用 Delta 放射组学的分类性能进行评估。仅使用平均 ADC 或 Delta ADC 的预测性能较差（曲线下面积（AUC）<0.7）。对于使用所有时间点的特征和相应的 Delta 放射组学的放射组学研究，SVM 明显优于逻辑回归（AUC 为 0.91±0.05 v.s. 0.85±0.06）。不使用 Delta 放射组学的单时间点或多时间点的预测 AUC 值均低于 0.74。使用基线治疗中或治疗后的 Delta 放射组学大大提高了预测能力。在这项工作中，构建了一个 SVM 模型，使用纵向 DWI 的放射组学特征来预测 TES。基于这项研究，我们发现仅使用平均 ADC、Delta ADC 或放射组学特征不足以进行响应预测，并且包括与基线相比治疗中或治疗后的 Delta 放射组学特征可以优化 TES 的预测，这是一个病理和临床终点。
