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      Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy

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          Abstract

          Background

          Whether peritumoral dilation radiomics can excellently predict early recrudescence (≤2 years) in hepatocellular carcinoma (HCC) remains unclear.

          Methods

          Between March 2012 and June 2018, 323 pathologically confirmed HCC patients without macrovascular invasion, who underwent liver resection and preoperative gadoxetate disodium (Gd-EOB-DTPA) MRI, were consecutively recruited into this study. Multivariate logistic regression identified independent clinicoradiologic predictors of 2-year recrudescence. Peritumoral dilation (tumor and peritumoral zones within 1cm) radiomics extracted features from 7-sequence images for modeling and achieved average but robust predictive performance through 5-fold cross validation. Independent clinicoradiologic predictors were then incorporated with the radiomics model for constructing a comprehensive nomogram. The predictive discrimination was quantified with the area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI).

          Results

          With the median recurrence-free survival (RFS) reaching 60.43 months, 28.2% (91/323) and 16.4% (53/323) patients suffered from early and delay relapse, respectively. Microvascular invasion, tumor size >5 cm, alanine aminotransferase >50 U/L, γ-glutamyltransferase >60 U/L, prealbumin ≤250 mg/L, and peritumoral enhancement independently impaired 2-year RFS in the clinicoradiologic model with AUC of 0.694 (95% CI 0.628–0.760). Nevertheless, these indexes were paucity of robustness (P >0.05) when integrating with 38 most recurrence-related radiomics signatures for developing the comprehensive nomogram. The peritumoral dilation radiomics—the ultimate prediction model yielded satisfactory mean AUCs (training cohort: 0.939, 95% CI 0.908–0.973; validation cohort: 0.842, 95% CI 0.736–0.951) after 5-fold cross validation and fitted well with the actual relapse status in the calibration curve. Besides, our radiomics model obtained the best clinical net benefits, with significant improvements of NRI (35.9%-66.1%, P <0.001) versus five clinical algorithms: the clinicoradiologic model, the tumor-node-metastasis classification, the Barcelona Clinic Liver Cancer stage, the preoperative and postoperative risks of Early Recurrence After Surgery for Liver tumor.

          Conclusion

          Gd-EOB-DTPA MRI-based peritumoral dilation radiomics is a potential preoperative biomarker for early recurrence of HCC patients without macrovascular invasion.

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          Most cited references65

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          EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma

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            Hepatocellular carcinoma

            Hepatocellular carcinoma appears frequently in patients with cirrhosis. Surveillance by biannual ultrasound is recommended for such patients because it allows diagnosis at an early stage, when effective therapies are feasible. The best candidates for resection are patients with a solitary tumour and preserved liver function. Liver transplantation benefits patients who are not good candidates for surgical resection, and the best candidates are those within Milan criteria (solitary tumour ≤5 cm or up to three nodules ≤3 cm). Image-guided ablation is the most frequently used therapeutic strategy, but its efficacy is limited by the size of the tumour and its localisation. Chemoembolisation has survival benefit in asymptomatic patients with multifocal disease without vascular invasion or extrahepatic spread. Finally, sorafenib, lenvatinib, which is non-inferior to sorafenib, and regorafenib increase survival and are the standard treatments in advanced hepatocellular carcinoma. This Seminar summarises the scientific evidence that supports the current recommendations for clinical practice, and discusses the areas in which more research is needed.
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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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                Author and article information

                Journal
                J Hepatocell Carcinoma
                J Hepatocell Carcinoma
                jhc
                jhepc
                Journal of Hepatocellular Carcinoma
                Dove
                2253-5969
                09 June 2021
                2021
                : 8
                : 545-563
                Affiliations
                [1 ]Department of Radiology, Zhongshan Hospital, Fudan University , Shanghai, 200032, People’s Republic of China
                [2 ]Shanghai Institute of Medical Imaging , Shanghai, 200032, People’s Republic of China
                [3 ]Department of General Surgery, Zhongshan Hospital, Fudan University , Shanghai, 200032, People’s Republic of China
                [4 ]Shanghai United Imaging Intelligence Co., Ltd , Shanghai, 200232, People’s Republic of China
                [5 ]Department of Medical Imaging, Shanghai Medical College, Fudan University , Shanghai, 200032, People’s Republic of China
                Author notes
                Correspondence: Mengsu Zeng; Chun Yang Department of Radiology, Zhongshan Hospital, Fudan University , 180 Feng Lin Road, Shanghai, 200032, People’s Republic of ChinaTel +86-21-13501922963; +86-21-18702135336 Email mengsu_zeng@163.com; dryangchun@hotmail.com
                Author information
                http://orcid.org/0000-0002-0299-9407
                Article
                309570
                10.2147/JHC.S309570
                8200148
                34136422
                a96322de-8fbb-48eb-9669-3c0a7add2bcb
                © 2021 Chong et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 05 March 2021
                : 06 May 2021
                Page count
                Figures: 5, Tables: 11, References: 66, Pages: 19
                Funding
                Funded by: National Natural Science Foundation of China, open-funder-registry 10.13039/501100001809;
                Funded by: Shanghai Science and Technology;
                Funded by: Zhongshan Hospital, Fudan University;
                Funded by: Shanghai Municipal Key Clinical Specialty;
                Funded by: Clinical Research Plan of SHDC;
                This work was supported by the National Natural Science Foundation of China (grant number: 91859107); Shanghai Science and Technology Committee (grant numbers: 18DZ1930102, 19411965500); Zhongshan Hospital, Fudan University (grant numbers: 2018ZSLC22, 2020ZSLC61); Shanghai Municipal Key Clinical Specialty (grant number: W2019-018); and Clinical Research Plan of SHDC (grant number: SHDC2020CR1029B).
                Categories
                Original Research

                gadoxetate disodium,hepatocellular carcinoma,radiomics,magnetic resonance imaging,neoplasm recurrence

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