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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

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          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.

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          Most cited references45

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          Radiomics: Images Are More than Pictures, They Are Data

          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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            Radiomics: the bridge between medical imaging and personalized medicine

            Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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              The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM.

              The American Joint Committee on Cancer and the International Union for Cancer Control update the tumor-node-metastasis (TNM) cancer staging system periodically. The most recent revision is the 7th edition, effective for cancers diagnosed on or after January 1, 2010. This editorial summarizes the background of the current revision and outlines the major issues revised. Most notable are the marked increase in the use of international datasets for more highly evidenced-based changes in staging, and the enhanced use of nonanatomic prognostic factors in defining the stage grouping. The future of cancer staging lies in the use of enhanced registry data standards to support personalization of cancer care through cancer outcome prediction models and nomograms.
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                Author and article information

                Contributors
                Journal
                JMIR Med Inform
                JMIR Med Inform
                JMI
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                2291-9694
                October 2020
                5 October 2020
                : 8
                : 10
                : e23578
                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
                Author notes
                Corresponding Author: Jian Shu shujiannc@ 123456163.com
                Author information
                https://orcid.org/0000-0003-1586-2079
                https://orcid.org/0000-0001-8385-9045
                https://orcid.org/0000-0002-4341-7573
                https://orcid.org/0000-0001-8610-6313
                https://orcid.org/0000-0001-6309-6386
                https://orcid.org/0000-0002-1744-0235
                https://orcid.org/0000-0002-7386-7217
                Article
                v8i10e23578
                10.2196/23578
                7573697
                33016889
                99371785-9bf2-498c-8eca-4d635a3689cf
                ©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.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                : 17 August 2020
                : 6 September 2020
                : 18 September 2020
                Categories
                Original Paper
                Original Paper

                pso-svm algorithm,magnetic resonance imaging,lymph node metastases,differentiation degree,extrahepatic cholangiocarcinoma,radiomics feature,algorithm,mri,radiomics,lymph,cancer,oncology

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