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      Survival outcomes of stage I colorectal cancer: development and validation of the ACEPLY model using two prospective cohorts

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          Abstract

          Background

          Approximately 10% of stage I colorectal cancer (CRC) patients experience unfavorable clinical outcomes after surgery. However, little is known about the subset of stage I patients who are predisposed to high risk of recurrence or death. Previous evidence was limited by small sample sizes and lack of validation.

          Methods

          We aimed to identify early indicators and develop a risk stratification model to inform prognosis of stage I patients by employing two large prospective cohorts. Prognostic factors for stage II tumors, including T stage, number of nodes examined, preoperative carcinoma embryonic antigen (CEA), lymphovascular invasion, perineural invasion (PNI), and tumor grade were investigated in the discovery cohort, and significant findings were further validated in the other cohort. We adopted disease-free survival (DFS) as the primary outcome for maximum statistical power and recurrence rate and overall survival (OS) as secondary outcomes. Hazard ratios (HRs) were estimated from Cox proportional hazard models, which were subsequently utilized to develop a multivariable model to predict DFS. Predictive performance was assessed in relation to discrimination, calibration and net benefit.

          Results

          A total of 728 and 413 patients were included for discovery and validation. Overall, 6.7% and 4.1% of the patients developed recurrences during follow-up. We identified consistent significant effects of PNI and higher preoperative CEA on inferior DFS in both the discovery (PNI: HR = 4.26, 95% CI: 1.70–10.67, p = 0.002; CEA: HR = 1.46, 95% CI: 1.13–1.87, p = 0.003) and the validation analysis (PNI: HR = 3.31, 95% CI: 1.01–10.89, p = 0.049; CEA: HR = 1.58, 95% CI: 1.10–2.28, p = 0.014). They were also significantly associated with recurrence rate. Age at diagnosis was a prominent determinant of OS. A prediction model on DFS using Age at diagnosis, CEA, PNI, and number of LYmph nodes examined (ACEPLY) showed significant discriminative performance (C-index: 0.69, 95% CI:0.60–0.77) in the external validation cohort. Decision curve analysis demonstrated added clinical benefit of applying the model for risk stratification.

          Conclusions

          PNI and preoperative CEA are useful indicators for inferior survival outcomes of stage I CRC. Identification of stage I patients at high risk of recurrence is feasible using the ACEPLY model, although the predictive performance is yet to be improved.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-022-02693-7.

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          Most cited references40

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

            Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies.A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies
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              mice: Multivariate Imputation by Chained Equations inR

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                Author and article information

                Contributors
                wuaw@sina.com
                yazhou.he@scu.edu.cn
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                4 January 2023
                4 January 2023
                2023
                : 21
                : 3
                Affiliations
                [1 ]GRID grid.412901.f, ISNI 0000 0004 1770 1022, Colorectal Cancer Center, Department of General Surgery, , West China Hospital, Sichuan University, ; Chengdu, China
                [2 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, West China School of Public Health and West China Fourth Hospital, , Sichuan University, ; Chengdu, China
                [3 ]GRID grid.412474.0, ISNI 0000 0001 0027 0586, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Unit III & Ostomy Service, Gastrointestinal Cancer Centre, , Peking University Cancer Hospital & Institute, ; Beijing, China
                [4 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, Department of Vascular Surgery, West China Hospital, West China School of Medicine, , Sichuan University, ; Chengdu, China
                [5 ]GRID grid.256112.3, ISNI 0000 0004 1797 9307, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, , Fujian Medical University, ; Fuzhou, China
                [6 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, Department of Epidemiology and Medical Statistics, West China School of Public Health and West China Fourth Hospital, , Sichuan University, ; Chengdu, China
                [7 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, , Zhejiang University School of Medicine, ; Hangzhou, China
                [8 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Centre for Global Health, , Usher Institute, The University of Edinburgh, ; Edinburgh, UK
                [9 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, Department of Oncology, West China School of Public Health and West China Fourth Hospital, , Sichuan University, ; Chengdu, China
                Article
                2693
                10.1186/s12916-022-02693-7
                9814451
                36600277
                2cc64e0f-f5f5-491a-b287-3345c2feda39
                © 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
                : 6 May 2022
                : 2 December 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82103541
                Award ID: 82103918
                Award ID: 82173156
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100017939, Sichuan Provincial Postdoctoral Science Foundation;
                Award ID: 2019HXBH041
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100012542, Sichuan Province Science and Technology Support Program;
                Award ID: 2022NSFSC1314
                Award ID: 2021YFS0025
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2023

                Medicine
                colorectal cancer,early stage,prognosis,risk factor,prediction model
                Medicine
                colorectal cancer, early stage, prognosis, risk factor, prediction model

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