Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma

Chunmei Yang; Mengping Huang; Shupan Li; Jianqiang Chen; Yao Yang; Na Qin; Deqing Huang; Jian Shu

doi:10.1016/j.canlet.2019.11.036

Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma

Cancer Lett. 2020 Feb 1:470:1-7. doi: 10.1016/j.canlet.2019.11.036. Epub 2019 Dec 3.

Authors

Chunmei Yang¹, Mengping Huang², Shupan Li³, Jianqiang Chen⁴, Yao Yang⁵, Na Qin⁶, Deqing Huang⁷, Jian Shu⁸

Affiliations

¹ Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China. Electronic address: 2695747382@qq.com.
² Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China. Electronic address: 450415395@qq.com.
³ The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China. Electronic address: ShupanL@my.swjtu.edu.cn.
⁴ The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China. Electronic address: cjqsyghr@my.swjtu.edu.cn.
⁵ The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China. Electronic address: 2014111530@my.swjtu.edu.cn.
⁶ The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China. Electronic address: qinna@swjtu.cn.
⁷ The Institute of Systems Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China. Electronic address: elehd@home.swjtu.edu.cn.
⁸ Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China. Electronic address: shujiannc@163.com.

PMID: 31809800
DOI: 10.1016/j.canlet.2019.11.036

Abstract

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.

Keywords: Differentiation degree; Extrahepatic cholangiocarcinoma; Lymph node metastases; Magnetic resonance imaging; Radiomics.

Publication types

Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Adult
Aged
Aged, 80 and over
Algorithms
Bile Duct Neoplasms / diagnostic imaging*
Bile Duct Neoplasms / pathology
Bile Duct Neoplasms / therapy
Bile Ducts, Extrahepatic / diagnostic imaging*
Bile Ducts, Extrahepatic / pathology
Cholangiocarcinoma / diagnostic imaging*
Cholangiocarcinoma / pathology
Cholangiocarcinoma / therapy
Clinical Decision-Making
Female
Humans
Image Processing, Computer-Assisted*
Logistic Models
Lymph Nodes / pathology
Lymphatic Metastasis / diagnostic imaging*
Magnetic Resonance Imaging
Male
Middle Aged
Models, Biological
Neoplasm Grading
Patient Selection
ROC Curve
Reproducibility of Results
Retrospective Studies
Software