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      Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram

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          Abstract

          Background

          Predicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation.

          Methods

          Total 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training ( n = 129) and validation( n = 55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration.

          Results

          Among the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index = 0.736 (95%CI:0.726–0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (C-index = 0.792(95%CI:0.727–0.857) and validation (C-index = 0.755(95%CI:0.651–0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P < 0.0001).

          Conclusions

          This study successfully built a radiomics nomogram that integrated clinicopathological and radiomics features, which can be potentially used to predict ER after curative ablation for HCC patients.

          Electronic supplementary material

          The online version of this article (10.1186/s40644-019-0207-7) contains supplementary material, which is available to authorized users.

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          Most cited references29

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          Applied Logistic Regression

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            Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

            To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
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              Machine Learning methods for Quantitative Radiomic Biomarkers

              Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
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                Author and article information

                Contributors
                18612778605@163.com
                +86-10-6313 8625 , cjr.wzhch@vip.163.com
                gudongsheng2016@ia.ac.cn
                +86-10-62527995 , tian@ieee.org
                fan-0817@163.com
                weijingwei2014@ia.ac.cn
                13466503280@163.com
                xh13206045@gmail.com
                di.dong@ia.ac.cn
                supermars15@163.com
                sunyu0624@yeah.net
                bjjjzhx@163.com
                jiliangfeng@hotmail.com
                Journal
                Cancer Imaging
                Cancer Imaging
                Cancer Imaging
                BioMed Central (London )
                1740-5025
                1470-7330
                26 April 2019
                26 April 2019
                2019
                : 19
                : 21
                Affiliations
                [1 ]ISNI 0000 0004 0369 153X, GRID grid.24696.3f, Department of Radiology, , Beijing Friendship Hospital, Capital Medical University, ; No.95, Yong An Road, Xicheng District, Beijing, 100050 China
                [2 ]GRID grid.414379.c, Center of Interventional Oncology and Liver Diseases, , Beijing Youan Hospital, Capital Medical University, ; Beijing, 100069 China
                [3 ]ISNI 0000000119573309, GRID grid.9227.e, Key Laboratory of Molecular Imaging, Institute of Automation, , Chinese Academy of Sciences, ; No.95 Zhongguancun East Road, Haidian District, Beijing, 100190 China
                [4 ]GRID grid.414379.c, Center of Clinical Pathology, , Beijing Youan Hospital, Capital Medical University, ; Beijing, 100069 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, ; Beijing, 100191 China
                [6 ]ISNI 0000 0001 0707 115X, GRID grid.440736.2, Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, , Xidian University, ; Xi’an, Shanxi 710126 China
                [7 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                Article
                207
                10.1186/s40644-019-0207-7
                6485136
                31027510
                5590279c-19de-48f1-9437-536c36f3488b
                © The Author(s). 2019

                Open AccessThis 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
                : 8 January 2019
                : 2 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100010270, Capital Health Research and Development of Special;
                Award ID: 2018-2-2182
                Funded by: the Beijing Municipal Science & Technology Commission
                Award ID: Z181100001718070
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81371546
                Award ID: 61527807
                Award ID: 81527805
                Award ID: 81771924
                Funded by: Beijing Training Project For The Leading Talents in S & T
                Award ID: Z141107001514002
                Funded by: Health Industry Special Scientific Research Project
                Award ID: 201402019
                Funded by: Beijing Municipal Administration of Hospitals’ Mission Plan
                Award ID: SML20150101
                Funded by: Beijing Scholar 2015
                Award ID: 160
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                hepatocellular carcinoma,radiomics,recurrence,forecasting,ablation techniques

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