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      Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

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          Abstract

          Background

          This retrospective study aimed to develop and validate a combined model based [ 18F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients.

          Methods

          Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [ 18F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

          Results

          Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595–0.874) and 0.750 (95% CI, 0.577–0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685–0.916) and 0.869 (95% CI, 0.715–0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794–0.963) and 0.892 (95% CI, 0.758–0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma.

          Conclusions

          The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.

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          Most cited references30

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          Computational Radiomics System to Decode the Radiographic Phenotype

          Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.
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            The International Neuroblastoma Risk Group (INRG) classification system: an INRG Task Force report.

            Because current approaches to risk classification and treatment stratification for children with neuroblastoma (NB) vary greatly throughout the world, it is difficult to directly compare risk-based clinical trials. The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. The statistical and clinical significance of 13 potential prognostic factors were analyzed in a cohort of 8,800 children diagnosed with NB between 1990 and 2002 from North America and Australia (Children's Oncology Group), Europe (International Society of Pediatric Oncology Europe Neuroblastoma Group and German Pediatric Oncology and Hematology Group), and Japan. Survival tree regression analyses using event-free survival (EFS) as the primary end point were performed to test the prognostic significance of the 13 factors. Stage, age, histologic category, grade of tumor differentiation, the status of the MYCN oncogene, chromosome 11q status, and DNA ploidy were the most highly statistically significant and clinically relevant factors. A new staging system (INRG Staging System) based on clinical criteria and tumor imaging was developed for the INRG Classification System. The optimal age cutoff was determined to be between 15 and 19 months, and 18 months was selected for the classification system. Sixteen pretreatment groups were defined on the basis of clinical criteria and statistically significantly different EFS of the cohort stratified by the INRG criteria. Patients with 5-year EFS more than 85%, more than 75% to or = 50% to < or = 75%, or less than 50% were classified as very low risk, low risk, intermediate risk, or high risk, respectively. By defining homogenous pretreatment patient cohorts, the INRG classification system will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world and the development of international collaborative studies.
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              Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma

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                Author and article information

                Contributors
                yangjigang@ccmu.edu.cn
                Journal
                BMC Med Imaging
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central (London )
                1471-2342
                28 May 2022
                28 May 2022
                2022
                : 22
                : 102
                Affiliations
                [1 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Nuclear Medicine, Beijing Friendship Hospital, , Capital Medical University, ; 95 Yong An Road, Xi Cheng District, Beijing, 100045 China
                [2 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Surgical Oncology, National Center for Children’s Health, Beijing Children’s Hospital, , Capital Medical University, ; Beijing, 100045 China
                [3 ]Sinounion Medical Technology (Beijing) Co., Ltd. Beijing, 1 Yongtaizhuang North Road, Hai Dian District, Beijing, 100192 China
                [4 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Biomedical Engineering, School of Medicine, , Tsinghua University, ; Beijing, 100084 China
                Article
                828
                10.1186/s12880-022-00828-z
                9148481
                35643445
                f90a3122-a8d8-42b9-9882-64e73e72becb
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 12 December 2021
                : 17 May 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 82102088
                Award ID: 81971642
                Funded by: Capital’s Funds for Health Improvement and Research
                Award ID: 2020-2-2025
                Funded by: National Key Research and Development Plan
                Award ID: 2020YFC0122000
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Radiology & Imaging
                neuroblastoma,[18f]fdg,pet/ct,radiomics,recurrence
                Radiology & Imaging
                neuroblastoma, [18f]fdg, pet/ct, radiomics, recurrence

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