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Feasibility of magnetic resonance imaging-based radiomics features for preoperative prediction of extrahepatic cholangiocarcinoma stage

无线电技术 磁共振成像 旁侵犯 线性判别分析 规范化(社会学) 接收机工作特性 医学 人工智能 分割 放射科 阶段(地层学) 模式识别(心理学) 计算机科学 癌症 地质学 社会学 古生物学 内科学 人类学
作者
Xinqiao Huang,Jian Shu,Yulan Yan,Xin Chen,Chunmei Yang,Tiejun Zhou,Man Li
出处
期刊:European Journal of Cancer [Elsevier BV]
卷期号:155: 227-235 被引量:25
标识
DOI:10.1016/j.ejca.2021.06.053
摘要

The aim of this study is to develop and test radiomics models based on magnetic resonance imaging (MRI) to preoperatively and respectively predict the T stage, perineural invasion, and microvascular invasion of extrahepatic cholangiocarcinoma (eCCA) through a non-invasive approach.This research included 101 eCCA patients (29-83 years; 45 females and 56 males) between August 2011 and December 2019. Radiomics features were retrospectively extracted from T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map using MaZda software. The region of interest was manually delineated in the largest section on four MRI images as ground truth while keeping 1-2 mm margin to tumor border, respectively. Pretreatment, dimension reduction method, and classifiers were used to establish radiomics signatures for assessing three pathological characteristics of eCCA. Finally, independent training and testing datasets were used to assess radiomics signature performance based on receiver operating characteristic curve analysis, accuracy, precision, sensitivity, and specificity.This study extracted 1208 radiomics features from four MRI images of each patient. The best performing radiomics signatures for assessing the T stage, perineural invasion, and microvascular invasion were respectively produced by L1_normalization + linear discriminant analysis (LDA) + logistic regression, Box_Cox transformer + LDA + K-nearest neighbor, and L2_normalization + LDA + AdaBoost. The area under the curve values of the radiomics signatures for predicting the training and testing cohorts in each subgroup were respectively 1 and 0.962 (T stage), 1 and 1 (both perineural invasion and microvascular invasion).These proposed radiomic models based on MR images had powerful performance and high potential in predicting T stage, perineural, and microvascular invasion of eCCA.Prognostic study.
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