磁共振成像放射组学模型预测肝外胆管癌的病理分级和淋巴结转移。
Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma.
机构信息
Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China.
The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China.
出版信息
Cancer Lett. 2020 Feb 1;470:1-7. doi: 10.1016/j.canlet.2019.11.036. Epub 2019 Dec 3.
The aim of this study was to evaluate diagnostic performance of radiomics models of MRI in the detection of differentiation degree (DD) and lymph node metastases (LNM) of extrahepatic cholangiocarcinoma (ECC). We retrospectively enrolled 100 patients with ECC confirmed by pathology from January 2011 to December 2018. Three hundred radiomics features were extracted from each region of interest using MaZda software. Next, the radiomics model was developed by incorporating the optimal radiomics signatures and ADC values of tumors to predict DD (model A) and LNM (model B) of ECC, respectively, through the random forest algorithm. After which, the performance of the radiomics models were further evaluated. The model A showed better performance in both training and testing cohorts to discriminate high and medium-low differentiation groups of ECC, with an average AUC of 0.78 and 0.80, respectively. The model B also yielded the good average AUC of 0.80 and 0.90 to predict the LNM of ECC in training and testing cohorts. The radiomics models based on MRI performed well in predicting DD and LNM of ECC and have significant potential in clinical noninvasive diagnosis and in the prediction of ECC.
本研究旨在评估 MRI 放射组学模型在检测肝外胆管癌（ECC）分化程度（DD）和淋巴结转移（LNM）中的诊断性能。我们回顾性纳入了 2011 年 1 月至 2018 年 12 月期间经病理证实的 100 例 ECC 患者。使用 MaZda 软件从每个感兴趣区域提取 300 个放射组学特征。然后，通过随机森林算法，将肿瘤的最佳放射组学特征和 ADC 值纳入到放射组学模型中，分别预测 ECC 的 DD（模型 A）和 LNM（模型 B）。之后，进一步评估放射组学模型的性能。模型 A 在训练和测试队列中均表现出更好的性能，能够区分 ECC 的高中低分化组，平均 AUC 分别为 0.78 和 0.80。模型 B 也分别在训练和测试队列中预测 ECC 的 LNM 时，获得了较好的平均 AUC 为 0.80 和 0.90。基于 MRI 的放射组学模型在预测 ECC 的 DD 和 LNM 方面表现良好，在临床无创诊断和 ECC 预测方面具有显著的应用潜力。
Zotero 插件