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      Using population-based data to evaluate the impact of adherence to endocrine therapy on survival in breast cancer through the web-application BreCanSurvPred

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          Abstract

          We show how the use and interpretation of population-based cancer survival indicators can help oncologists talk with breast cancer (BC) patients about the relationship between their prognosis and their adherence to endocrine therapy (ET). The study population comprised a population-based cohort of estrogen receptor positive BC patients ( N = 1268) diagnosed in Girona and Tarragona (Northeastern Spain) and classified according to HER2 status (+ / −), stage at diagnosis (I/II/III) and five-year cumulative adherence rate (adherent > 80%; non-adherent ≤ 80%). Cox regression analysis was performed to identify significant prognostic factors for overall survival, whereas relative survival (RS) was used to estimate the crude probability of death due to BC (P BC). Stage and adherence to ET were the significant factors for predicting all-cause mortality. Compared to stage I, risk of death increased in stage II (hazard ratio [HR] 2.24, 95% confidence interval [CI]: 1.51–3.30) and stage III (HR 5.11, 95% CI 3.46–7.51), and it decreased with adherence to ET (HR 0.57, 95% CI 0.41–0.59). P BC differences were higher in non-adherent patients compared to adherent ones and increased across stages: stage I: 6.61% (95% CI 0.05–13.20); stage II: 9.77% (95% CI 0.59–19.01), and stage III: 22.31% (95% CI 6.34–38.45). The age-adjusted survival curves derived from this modeling were implemented in the web application BreCanSurvPred ( https://pdocomputation.snpstats.net/BreCanSurvPred). Web applications like BreCanSurvPred can help oncologists discuss the consequences of non-adherence to prescribed ET with patients.

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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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            Breast Cancer Treatment

            Breast cancer will be diagnosed in 12% of women in the United States over the course of their lifetimes and more than 250 000 new cases of breast cancer were diagnosed in the United States in 2017. This review focuses on current approaches and evolving strategies for local and systemic therapy of breast cancer.
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              Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

              Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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                Author and article information

                Contributors
                r.cleries@iconcologia.net
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 May 2022
                16 May 2022
                2022
                : 12
                : 8097
                Affiliations
                [1 ]GRID grid.418284.3, ISNI 0000 0004 0427 2257, Pla Director d’Oncología, IDIBELL, ; Av. Gran Vía 199-203, 08908 Hospitalet de Llobregat, Barcelona, Spain
                [2 ]GRID grid.418284.3, ISNI 0000 0004 0427 2257, Institut d’Investigació Biomèdica de Bellvitge, , IDIBELL, Av. Gran Via de L’Hospitalet, ; 199-203 – 1a planta, 08908 Hospitalet de Llobregat, Barcelona, Spain
                [3 ]GRID grid.429182.4, Institut d’Investigació Biomèdica de Girona, IDIBGI, ; C/Dr.Castany S/N. Edifici M2. Parc Hospitalari Martí I Julià, 17190 Salt, Spain
                [4 ]GRID grid.411136.0, ISNI 0000 0004 1765 529X, Registre de Càncer de Tarragona, Servei d’Epidemiologia i Prevenció del Càncer, Hospital Universitari Sant Joan de Reus, IISPV, ; Reus, Spain
                [5 ]GRID grid.6835.8, ISNI 0000 0004 1937 028X, Department de Estadística I Investigació Operativa de La Universitat Politècnica de Catalunya. EDIFICI H, ; Diagonal 647, 08028 Barcelona, Spain
                [6 ]GRID grid.5268.9, ISNI 0000 0001 2168 1800, Grupo de Investigación en Salud Pública, , Universidad de Alicante, ; 03690 Alicante, Spain
                [7 ]GRID grid.429182.4, Registre de Cáncer de Girona – Unitat d’Epidemiologia. Pla Director d’Oncologia. Institut Català d’Oncología. Grup d’Epidemiologia Descriptiva, Genètica I Prevenció del Càncer de Girona-IDIBGI, ; 17005 Girona, Spain
                [8 ]GRID grid.5319.e, ISNI 0000 0001 2179 7512, Facultat de Medicina, , Universitat de Girona (UdG), ; Girona, Spain
                [9 ]GRID grid.512890.7, Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP), ; Madrid, Spain
                [10 ]Independent Researcher, Barcelona, Spain
                [11 ]GRID grid.5841.8, ISNI 0000 0004 1937 0247, Secció de Estadística del Departament de Genètica, , Microbiología i Estadística de La Facultat de Biologia. Universitat de Barcelona, ; 08028 Barcelona, Spain
                [12 ]GRID grid.411295.a, ISNI 0000 0001 1837 4818, Servei d’Oncología Médica, , Institut Català d’Oncología. Hospital Universitari de Girona Doctor Josep Trueta, ; 17005 Girona, Spain
                [13 ]GRID grid.5841.8, ISNI 0000 0004 1937 0247, Department de Ciències Clíniques de La Universitat de Barcelona, ; 08907 Barcelona, Spain
                Article
                12228
                10.1038/s41598-022-12228-y
                9110408
                35577853
                9f8b0032-2138-4730-b91d-89c2451775b4
                © The Author(s) 2022

                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 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/.

                History
                : 6 August 2021
                : 22 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004587, Instituto de Salud Carlos III;
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award ID: PI18/01836
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003030, Agència de Gestió d'Ajuts Universitaris i de Recerca;
                Award ID: 2017SGR00735
                Award ID: 2017SGR00735
                Award ID: 2017SGR00735
                Award ID: 2017SGR00735
                Award ID: 2017SGR00735
                Award Recipient :
                Funded by: MCIU/AEI/FEDERPGC2018-095931-B-100
                Award ID: PGC2018-095931-B-100
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cancer,breast cancer,cancer epidemiology
                Uncategorized
                cancer, breast cancer, cancer epidemiology

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