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首页> 外文期刊>JMIR Medical Informatics >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
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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

机译：评估脱胸腺胆管癌分化程度和淋巴结转移的新方法：使用基于辐射基的粒子群优化和支持向量机模型预测

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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.
机译：背景辐射瘤可以提高传统图像诊断的准确性，以评估嗜肝胆管癌（ECC）;然而，这受放射科学家，主观评估和限制数据的变化有限。基于辐射的粒子群优化和支持向量机（PSO-SVM）模型可以提供更准确的辅助诊断，用于评估ECC的分化度（DD）和淋巴结转移（LNM）。目的，我们研究的目的是开发一种用于预测ECC的DD和LNM的PSO-SVM射频模型。该回顾性研究的方法，从2011年1月至2019年1月诊断为110例ECC患者的磁共振成像（MRI）数据用于构建戒烟预测模型。使用Mazda软件从T1-Precontrast加权成像（T1WI），T2加权成像（T2WI），T2加权成像（T2WI）和扩散加权成像（DWI）中提取了辐射瘤特征（第4.6版; Lodz技术大学电子学院）。我们执行了尺寸减少以分别获得每个序列的30个最佳特征。开发了一种PSO-SVM辐射瘤模型，通过掺入辐射族特征和表观扩散系数（ADC）值来预测ECC的DD和LNM。我们随机将110例划分为培训组（88/110,80％）和测试组（22/110,20％）。通过分析接收器操作特征曲线（AUC）下的区域来评估模型的性能。结果采用110例ECC患者开发了基于PSO-SVM的基于PSO-SVM的放射体模型。该模型分别产生0.8905和0.8461的平均AUC，用于ECC患者的培训和测试组中的DD。 ECC患者培训和测试组中LNM的平均AUC分别为0.9036和0.8889。对于110名患者，该模型具有很高的预测性能。培训组和ECC DD测试组的平均精度值分别为82.6％和80.9％; ECC培训组和测试组的平均精度值分别为83.6％和81.2％。结论基于MRI的PSO-SVM射频模型可能对辅助临床诊断和决策有用，这对于ECC的DD和LNM具有良好的临床应用潜力。

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