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      Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram

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          Abstract

          Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis ( P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

          Abstract

          Gastric cancer can metastasise to the peritoneal cavity; predicting in which patients this will occur is important for clinical management of the disease. Here, the authors use multi-photon imaging to derive a collagen signature of the primary cancer that allows the prediction of metastasis.

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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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            A Proportional Hazards Model for the Subdistribution of a Competing Risk

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              Decision curve analysis: a novel method for evaluating prediction models.

              Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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                Author and article information

                Contributors
                gzliguoxin@163.com
                naichengang@126.com
                shuangmuzhuo@gmail.com
                yanjunfudan@163.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 January 2021
                8 January 2021
                2021
                : 12
                : 179
                Affiliations
                [1 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, , Southern Medical University, ; Guangzhou, 510515 China
                [2 ]GRID grid.411902.f, ISNI 0000 0001 0643 6866, School of Science, , Jimei University, ; Xiamen, 361021 Fujian China
                [3 ]GRID grid.413402.0, ISNI 0000 0004 6068 0570, Department of Hepatobiliary and Pancreatic Surgery, Guangdong Provincial Hospital of Traditional Chinese Medicine, , The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, ; Guangzhou, 510120 China
                [4 ]GRID grid.415110.0, ISNI 0000 0004 0605 1140, Department of Pathology, , Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, ; Fuzhou, 350014 China
                [5 ]GRID grid.415110.0, ISNI 0000 0004 0605 1140, Precision Medicine Center, , Fujian Provincial Cancer Hospital, ; Fuzhou, 350014 China
                [6 ]GRID grid.416466.7, Department of Gastroenterology, , Nanfang Hospital, Southern Medical University, ; Guangzhou, 510515 China
                [7 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, Department of Radiology, , Sun Yat-sen University Cancer Center, ; Guangzhou, 510060 China
                [8 ]GRID grid.411503.2, ISNI 0000 0000 9271 2478, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, , Fujian Normal University, ; Fuzhou, 350007 China
                [9 ]GRID grid.488542.7, ISNI 0000 0004 1758 0435, Department of Oncological Surgery, , The Second Affiliated Hospital of Fujian Medical University, ; Quanzhou, 362000 China
                Author information
                http://orcid.org/0000-0002-1865-2862
                http://orcid.org/0000-0003-4622-6868
                http://orcid.org/0000-0002-8892-6013
                http://orcid.org/0000-0003-2773-7048
                http://orcid.org/0000-0003-2540-8232
                http://orcid.org/0000-0001-5767-5197
                http://orcid.org/0000-0003-1373-4380
                Article
                20429
                10.1038/s41467-020-20429-0
                7794254
                33420057
                c48ec795-960e-4844-ab99-ab9c3f93a124
                © The Author(s) 2021

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 September 2019
                : 27 November 2020
                Funding
                Funded by: The China Postdoctoral Science Foundation (2020M682789)
                Funded by: The State’s Key Project of Research and Development Plan (2017YFC0108300 and 2017YFC0108302) and the Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer (2020B121201004)
                Funded by: The Science and Technology Program of Fujian Province (2018Y2003, 2019L3018 and 2019YZ016006)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81771881
                Award ID: 81773117
                Award Recipient :
                Funded by: The Natural Science Foundation of Fujian Province (2018J07004), the Joint Funds of Fujian Provincial Health and Education Research (2019-WJ-21) and the State’s Key Project of Research and Development Plan (2019YFE0113700)
                Funded by: the Special Fund for Guangdong Province Public Research and Capacity Building (2014B020215002), the Natural Science Foundation of Guangdong Province (2015A030308006),the Guangzhou Industry University Research Cooperative Innovation Major Project (201704020062), the Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2016PY010), the Scientific Research Foundation for High-Level Talents in Nanfang Hospital of Southern Medical University (201404280056), the Clinical Research Project of Nanfang Hospital (2018CR034), the President Funding of Nanfang Hospital (2019Z023), and the Training Program for Undergraduate Innovation and Entrepreneurship (201912121008, 202012121091 and 202012121277)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer microenvironment,gastrointestinal cancer
                Uncategorized
                cancer microenvironment, gastrointestinal cancer

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