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      Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children

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          Abstract

          Background

          To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children.

          Methods

          This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis.

          Results

          Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912–1) was better than the senior radiologist’s clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677–0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889–1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings.

          Conclusions

          A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.

          Electronic supplementary material

          The online version of this article (10.1186/s12880-019-0355-z) contains supplementary material, which is available to authorized users.

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          Most cited references16

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          The WHO 2014 Global tuberculosis report—further to go

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            Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

            Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence (AI) methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered computed tomography (CT) image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n=353) and verified them in an independent validation cohort (n=352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter). We found a radiomic signature related to radiographic heterogeneity that successfuilly discriminated between EGFR+ and EGFR- cases (AUC=0.69). Combining this signature with a clinical model of EGFR status (AUC=0.70) significantly improved prediction accuracy (AUC=0.75). The highest performing signature was capable of distinguishing between EGFR+ and KRAS+ tumors (AUC=0.80) and, when combined with a clinical model (AUC=0.81), substantially improved its performance (AUC=0.86). A KRAS+/KRAS- radiomic signature also showed significant albeit lower performance (AUC=0.63) and did not improve accuracy of a clinical predictor of KRAS status. Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied non-invasively, repeatedly and at low cost.
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              Clinical Case Definitions for Classification of Intrathoracic Tuberculosis in Children: An Update

              Consensus case definitions for childhood tuberculosis have been proposed by an international expert panel, aiming to standardize the reporting of cases in research focusing on the diagnosis of intrathoracic tuberculosis in children. These definitions are intended for tuberculosis diagnostic evaluation studies of symptomatic children with clinical suspicion of intrathoracic tuberculosis, and were not intended to predefine inclusion criteria into such studies. Feedback from researchers suggested that further clarification was required and that these case definitions could be further improved. Particular concerns were the perceived complexity and overlap of some case definitions, as well as the potential exclusion of children with acute onset of symptoms or less severe disease. The updated case definitions proposed here incorporate a number of key changes that aim to reduce complexity and improve research performance, while maintaining the original focus on symptomatic children suspected of having intrathoracic tuberculosis. The changes proposed should enhance harmonized classification for intrathoracic tuberculosis disease in children across studies, resulting in greater comparability and the much-needed ability to pool study results.
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                Author and article information

                Contributors
                shellbb@163.com
                18640371047@163.com
                mahe@bmie.neu.edu.cn
                hanff@bmie.neu.edu.cn
                289349979@qq.com
                zhaoshunying2001@163.com
                lzmpang@163.com
                454087465@qq.com
                jie.tian@ia.ac.cn
                di.dong@ia.ac.cn
                86-010-59617038 , ppengyun@hotmail.com
                Journal
                BMC Med Imaging
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central (London )
                1471-2342
                8 August 2019
                8 August 2019
                2019
                : 19
                : 63
                Affiliations
                [1 ]ISNI 0000 0004 0369 153X, GRID grid.24696.3f, Department of Radiology, , Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, ; No.56 Nanlishi Road, Beijing, 100045 China
                [2 ]ISNI 0000 0004 0368 6968, GRID grid.412252.2, Sino-Dutch Biomedical and Information Engineering School, , Northeastern University, ; No. 3-11 Wenhua Road, Shenyang, China
                [3 ]ISNI 0000 0004 0644 477X, GRID grid.429126.a, CAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, , Institute of Automation, Chinese Academy of Sciences, ; No.95 Zhongguancun East Road, Beijing, 100190 China
                [4 ]ISNI 0000 0004 0369 153X, GRID grid.24696.3f, Department of Respiratory Medicine, Beijing Children’s Hospital, National Center for Children’s Health, , Capital Medical University, ; No.56 Nanlishi Road, Beijing, 100045 China
                [5 ]ISNI 0000 0000 9999 1211, GRID grid.64939.31, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, , Beihang University, ; No. 37 Xueyuan Road, Beijing, 100191 China
                [6 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, University of Chinese Academy of Sciences, ; No.19 Yuquan Road, Beijing, China
                Author information
                http://orcid.org/0000-0001-8213-9716
                Article
                355
                10.1186/s12880-019-0355-z
                6688341
                31395012
                01ba3b83-d83a-4b4f-99f3-3758773c9ebf
                © The Author(s). 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 11 March 2019
                : 24 June 2019
                Funding
                Funded by: Open Foundation of The State Key Laboratory for Management and Control of Complex Systems
                Award ID: 20170110
                Award Recipient :
                Funded by: the National Key R&D Program of China
                Award ID: 2017YFC1308700, 2017YFC1309100
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81771924, 81501616
                Award ID: 81227901, 81671851, 81527805
                Award Recipient :
                Funded by: Beijing Natural Science Foundation
                Award ID: L182061
                Award Recipient :
                Funded by: Key Laboratory of Particle Astrophysics, Institute of High Energy Physics (CN)
                Award ID: 173211KYSB20160053
                Award Recipient :
                Funded by: The Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority
                Award ID: XTCX201814
                Award Recipient :
                Funded by: Instrument Developing Project of the Chinese Academy of Sciences
                Award ID: YZ201502
                Award Recipient :
                Funded by: Youth Innovation Promotion Association CAS
                Award ID: 2017175
                Award Recipient :
                Funded by: National Key R&D Program of China
                Award ID: 2017YFA0205200
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Radiology & Imaging
                child,tuberculosis,pulmonary,pneumonia,radiomics,nomogram
                Radiology & Imaging
                child, tuberculosis, pulmonary, pneumonia, radiomics, nomogram

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