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      A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma

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          Abstract

          Objective

          To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data.

          Methods

          A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman’s correlation analysis, Pearson’s correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were validated using validation cohort data. Multimodal radiomics scores, integrating CT and MRI data, were applied, together with clinical risk factors, to construct nomograms for individualized survival assessment, and calibration curves were used to evaluate model consistency. Harrell’s concordance index (C-index) values were calculated to evaluate the prediction performance of the models.

          Results

          The radiomics score established using CT and MR data was an independent predictor of prognosis (DFS and OS) in patients with HCC ( p < 0.05). Prediction models illustrated by nomograms for predicting prognosis in liver cancer were established. Integrated CT and MRI and clinical multimodal data had the best predictive performance in the training and validation cohorts for both DFS [(C-index (95% CI): 0.858 (0.811–0.905) and 0.704 (0.563–0.845), respectively)] and OS [C-index (95% CI): 0.893 (0.846–0.940) and 0.738 (0.575–0.901), respectively]. The calibration curve showed that the multimodal radiomics model provides greater clinical benefits.

          Conclusion

          Multimodal (MRI/CT) radiomics models can serve as effective visual tools for predicting prognosis in patients with liver cancer. This approach has great potential to improve treatment decisions when applied for preoperative prediction in patients with HCC.

          Related collections

          Most cited references38

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma

              • Record: found
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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.

                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                07 March 2022
                2022
                : 12
                : 745258
                Affiliations
                [1] 1 Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University , Qingdao, China
                [2] 2 Department of Radiology, The Affiliated Hospital of Qingdao University , Qingdao, China
                [3] 3 Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University , Qingdao, China
                [4] 4 GE Healthcare , Shanghai, China
                [5] 5 Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University , Qingdao, China
                [6] 6 Shandong College Collaborative Innovation Center of Digital Medicine Clinical Treatment and Nutrition Health, Qingdao University , Qingdao, China
                Author notes

                Edited by: Alessandro Vitale, University Hospital of Padua, Italy

                Reviewed by: Marco Massani, ULSS2 Marca Trevigiana, Italy; Tingfan Wu, GE Healthcare, China

                *Correspondence: Xianjun Zhou, 18661809968@ 123456163.com ; Qian Dong, 18661801885@ 123456163.com

                †These authors have contributed equally to this work

                This article was submitted to Surgical Oncology, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2022.745258
                8936674
                35321432
                f99be2c4-da3a-4f0d-8867-1192a1725f1f
                Copyright © 2022 He, Hu, Zhu, Xu, Ge, Hao, Dong, Chen, Dong and Zhou

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 July 2021
                : 04 February 2022
                Page count
                Figures: 3, Tables: 6, Equations: 0, References: 38, Pages: 13, Words: 6578
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                liver cancer,multimodal imaging,computed tomography,mri,radiomics,nomogram
                Oncology & Radiotherapy
                liver cancer, multimodal imaging, computed tomography, mri, radiomics, nomogram

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