Simpson Garrett, Spieler Benjamin, Dogan Nesrin, Portelance Lorraine, Mellon Eric A, Kwon Deukwoo, Ford John C, Yang Fei
Department of Radiation Oncology, University of Miami, Miami, FL, 33136, USA.
Med Phys. 2020 Aug;47(8):3682-3690. doi: 10.1002/mp.14200. Epub 2020 May 16.
The aim of this study was to evaluate the potential and feasibility of radiomic features extracted from low field strength (0.35 T) magnetic resonance images (MRIs) in predicting treatment response for patients with pancreatic cancer undergoing stereotactic body radiotherapy (SBRT).
Twenty patients with unresected, non-metastatic pancreatic ductal adenocarcinoma (PDAC) were enrolled, all of whom received neoadjuvant chemotherapy followed by five-fraction MR-guided SBRT with a radiation dose range of 33-50 Gy. For each patient, five daily setup scans were acquired from a hybrid 0.35 T MRI/radiotherapy unit. Tumor heterogeneity quantified with radiomic features extracted from the gross tumor volume (GTV) was averaged over the course of treatment. Random forest (RF) and adaptive least absolute shrinkage and selection operator (LASSO) classification models were constructed to identify radiomics features predictive of treatment response. Predictive capability of the top-performing features was then evaluated using the receiver operating characteristic area under curve (AUC) obtained using leave-one-out cross-validation.
Half of the 20 patients showed response to treatment, defined by tumor regression on histopathology or tumor response on follow-up dynamic contrast-enhanced computed tomography (CT). The most predictive features selected by the RF method were GLCM energy and GLSZM gray-level variance. The RF-based model achieved an AUC = 0.81 with a 95% confidence interval of [0.594 to 1] The LASSO algorithm selected GLCM energy as the only predictive feature, achieving an AUC = 0.81 with 95% confidence interval of [0.596 to 1].
The findings of this study suggest that radiomic features extracted during MR-guided SBRT may contain predictive information about response of PDAC patients to treatment. Using the images acquired during treatment of PDAC patients supports continued expansion of radiomic analysis based on low field strength MR images and may hold the potential for providing timely indications of response to treatment.
本研究旨在评估从低场强（0.35 T）磁共振成像（MRI）中提取的影像组学特征在预测接受立体定向体部放疗（SBRT）的胰腺癌患者治疗反应方面的潜力和可行性。
招募了20例未切除、非转移性胰腺导管腺癌（PDAC）患者，所有患者均接受新辅助化疗，随后进行五分割的MR引导下SBRT，放射剂量范围为33 - 50 Gy。对于每位患者，从0.35 T MRI/放疗混合单元获取五次每日定位扫描。从大体肿瘤体积（GTV）中提取的影像组学特征量化的肿瘤异质性在治疗过程中进行平均。构建随机森林（RF）和自适应最小绝对收缩和选择算子（LASSO）分类模型，以识别预测治疗反应的影像组学特征。然后使用留一法交叉验证获得的曲线下面积（AUC）评估表现最佳特征的预测能力。
20例患者中有一半显示出治疗反应，定义为组织病理学上的肿瘤退缩或随访动态对比增强计算机断层扫描（CT）上的肿瘤反应。RF方法选择的最具预测性的特征是灰度共生矩阵（GLCM）能量和灰度游程长度矩阵（GLSZM）灰度方差。基于RF的模型AUC = 0.81，95%置信区间为[0.594至1]。LASSO算法选择GLCM能量作为唯一的预测特征，AUC = 0.81，95%置信区间为[0.596至1]。
本研究结果表明，在MR引导下的SBRT过程中提取的影像组学特征可能包含关于PDAC患者治疗反应的预测信息。利用PDAC患者治疗期间获取的图像支持基于低场强MR图像的影像组学分析的持续扩展，并可能具有提供治疗反应及时指示的潜力。
