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

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.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

“…With the development of precision medicine and artificial intelligence, radiomics has appeared as a new medical field that is able to capture and quantify lesion heterogeneity, especially for lesions in oncology patients, by extracting and analyzing the quantitative texture features (39). Importantly, we have made great efforts and obtained some amazing results about radiomics analysis in recent years (40)(41)(42). In this study, we first extracted 396 radiomics features from each ALN in the CECT images, and used all of the radiomics features to develop the radiomics model for discriminating ALN status in patients with breast cancer based on the SVM algorithm.…”

Applications of artificial intelligence in pancreatic and biliary diseases

“…Besides CT radiomic, Yao et al . studied patients with extrahepatic cholangiocarcinoma by extracting radiomic features from images obtained with precontrast T1‐weighted imaging, T2‐weighted imaging, and diffusion‐weighted imaging, and a radiomic model could predict lymph node metastasis of extrahepatic cholangiocarcinoma with an AUC of 0.89 44 . Those studies suggest that analysis of CT/MRI radiomic features may assist in predicting lymph node metastasis in biliary cancers.…”

A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers

“…The literature search and study selection was shown in Figure 1. The included studies were published between 2018 and 2021 (four contract-CT based on radiomics studies (18,28,29) and three MRI based on radiomics studies (19,(30)(31)(32) were included in the meta-analysis). A total of 977 BTCs patients were included.…”