Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Br J Radiol. 2021 Jun 1;94(1122):20201212. doi: 10.1259/bjr.20201212. Epub 2021 Apr 21.
To establish and substantiate MRI-based radiomic models to predict the treatment response of metastatic cervical lymph node to radiochemotherapy in patients with nasopharyngeal carcinoma (NPC).
A total of 145 consecutive patients with NPC were enrolled including 102 in primary cohort and 43 in validation cohort. Metastatic lymph nodes were diagnosed according to radiologic criteria and treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors. A total of 2704 radiomic features were extracted from contrast-enhanced weighted imaging (CE- WI) and weighted imaging (WI) for each patient, and were selected to construct radiomic signatures for CE-WI, WI, and combined CE-WI and WI, respectively. The area under curve (AUC) of receiver operating characteristic, sensitivity, specificity, and accuracy were used to estimate the performance of these radiomic models in predicting treatment response of metastatic lymph node.
No significant difference of AUC was found among radiomic signatures of CE-WI, WI, and combined CE-WI and WI in the primary and validation cohorts (all > 0.05). For combined CE-WI and WI data set, 12 features were selected to develop the radiomic signature. The AUC, sensitivity, specificity, and accuracy were 0.927 (0.878-0.975), 0.911 (0.804-0.970), 0.826 (0.686-0.922), and 0.872 (0.792-0.930) in primary cohort, and were 0.772 (0.624-0.920), 0.792 (0.578-0.929), 0.790 (0.544-0.939), and 0.791 (0.640-0.900) in validation cohort.
MRI-based radiomic models were developed to predict the treatment response of metastatic cervical lymph nodes to radiochemotherapy in patients with NPC, which might facilitate individualized therapy for metastatic lymph nodes before treatment.
Predicting the response in patients with NPC before treatment may allow more individualizing therapeutic strategy and avoid unnecessary side-effects and costs. Radiomic features extracted from metastatic cervical lymph nodes showed promising application for predicting the treatment response in NPC.
建立并验证基于 MRI 的放射组学模型，以预测鼻咽癌（NPC）患者转移性颈部淋巴结对放化疗的治疗反应。
共纳入 145 例连续 NPC 患者，其中 102 例来自原队列，43 例来自验证队列。根据影像学标准诊断转移性淋巴结，根据实体瘤反应评价标准（RECIST）评价治疗反应。对每位患者的对比增强加权成像（CE-WI）和弥散加权成像（WI）图像共提取 2704 个放射组学特征，并分别构建 CE-WI、WI 和 CE-WI 联合 WI 的放射组学特征。受试者工作特征曲线下面积（AUC）、敏感性、特异性和准确性用于评估这些放射组学模型预测转移性淋巴结治疗反应的性能。
在原队列和验证队列中，CE-WI、WI 和 CE-WI 联合 WI 的放射组学特征的 AUC 无显著差异（均>0.05）。对于 CE-WI 和 WI 联合数据集，选择 12 个特征来建立放射组学特征。在原队列中，AUC、敏感性、特异性和准确性分别为 0.927（0.878-0.975）、0.911（0.804-0.970）、0.826（0.686-0.922）和 0.872（0.792-0.930），在验证队列中，AUC、敏感性、特异性和准确性分别为 0.772（0.624-0.920）、0.792（0.578-0.929）、0.790（0.544-0.939）和 0.791（0.640-0.900）。
建立了基于 MRI 的放射组学模型，以预测 NPC 患者转移性颈部淋巴结对放化疗的治疗反应，这可能有助于在治疗前为转移性淋巴结制定个体化治疗策略。
在治疗前预测 NPC 患者的反应可能允许更个体化的治疗策略，并避免不必要的副作用和成本。从转移性颈部淋巴结提取的放射组学特征在预测 NPC 患者的治疗反应方面显示出了有前景的应用。
