A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

Xiaopeng Yao; Xinqiao Huang; Chunmei Yang; Anbin Hu; Guangjin Zhou; Jianbo Lei; Jian Shu

doi:10.2196/23578

A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

JMIR Med Inform. 2020 Oct 5;8(10):e23578. doi: 10.2196/23578.

Authors

Xiaopeng Yao^{1

2}, Xinqiao Huang³, Chunmei Yang³, Anbin Hu^{1

2}, Guangjin Zhou⁴, Jianbo Lei^{1

5}, Jian Shu³

Affiliations

¹ School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
² Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China.
³ Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁴ Department of Radiology, Peking University Third Hospital, Beijing, China.
⁵ Center for Medical Informatics/Institute of Medical Technology, Peking University, Beijing, China.

PMID: 33016889
PMCID: PMC7573697
DOI: 10.2196/23578

Abstract

Background: Radiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC.

Objective: The objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC.

Methods: For this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC).

Results: A radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively.

Conclusions: The MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.

Keywords: MRI; PSO-SVM algorithm; algorithm; cancer; differentiation degree; extrahepatic cholangiocarcinoma; lymph; lymph node metastases; magnetic resonance imaging; oncology; radiomics; radiomics feature.

©Xiaopeng Yao, Xinqiao Huang, Chunmei Yang, Anbin Hu, Guangjin Zhou, Jianbo Lei, Jian Shu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.10.2020.