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      A nomogram for predicting the risk of male breast cancer for overall survival

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          Abstract

          Background

          Male breast cancer (MBC) is a rare disease, accounting for <1% of all male carcinomas. Lack of prospective data, the current therapy for MBC is based on retrospective analysis or information that is extrapolated from studies of female patients. We constructed a nomogram model for predicting the overall survival (OS) of MBC patients and verify its feasibility using data from China.

          Methods

          Constructed a predictive model using 1224 MBC patients from the Surveillance, Epidemiology and End Results (SEER) registry between 2010 and 2015. The performance of the model was externally validated between 2002 to 2021 using 44 MBC patients from the Fujian Medical University Union Hospital. The independent prognostic factors were selected by univariate and multivariate Cox regression analyses. The nomogram was constructed to predict individual survival outcomes for MBC patients. The discriminative power, calibration, and clinical effectiveness of the nomogram were evaluated by the receiver operating characteristic (ROC) curve, and the decision curve analysis (DCA).

          Results

          A total of 1224 male breast cancer patients were in the training cohort and 44 in the validation cohort. T status ( p<0.001), age at diagnosis ( p<0.001), histologic grade ( p=0.008), M status ( p<0.001), ER status ( p=0.001), Her2 status ( p=0.019), chemotherapy ( p=0.015) were independently associated with OS. The diagnostic performance of this model was evaluated and validated using ROC curves on the training and validation datasets. In the training cohort, the nomogram-predicted AUC value was 0.786 for 3-year OS and 0.767 for 5-year OS. In the validation cohort, the nomogram-predicted AUC value was 0.893 for 3-year OS and 0.895 for 5-year OS. Decision curve analysis demonstrated that the nomogram was more benefit than the AJCC stage.

          Conclusions

          We developed a nomogram that predicts 3-year and 5-year survival in MBC patients. Validation using bootstrap sampling revealed optimal discrimination and calibration, suggesting that the nomogram may have clinical utility. The results remain reproducible in the validation cohort which included Chinese data. The model was superior to the AJCC stage system as shown in the decision curve analysis (DCA).

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          Most cited references25

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          How to build and interpret a nomogram for cancer prognosis.

          Nomograms are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models into a single numerical estimate of the probability of an event, such as death or recurrence, that is tailored to the profile of an individual patient. User-friendly graphical interfaces for generating these estimates facilitate the use of nomograms during clinical encounters to inform clinical decision making. However, the statistical underpinnings of these models require careful scrutiny, and the degree of uncertainty surrounding the point estimates requires attention. This guide provides a nonstatistical audience with a methodological approach for building, interpreting, and using nomograms to estimate cancer prognosis or other health outcomes.
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            Cancer treatment and survivorship statistics, 2022

            The number of cancer survivors continues to increase in the United States due to the growth and aging of the population as well as advances in early detection and treatment. To assist the public health community in better serving these individuals, the American Cancer Society and the National Cancer Institute collaborate triennially to estimate cancer prevalence in the United States using incidence and survival data from the Surveillance, Epidemiology, and End Results cancer registries, vital statistics from the Centers for Disease Control and Prevention's National Center for Health Statistics, and population projections from the US Census Bureau. Current treatment patterns based on information in the National Cancer Database are presented for the most prevalent cancer types by race, and cancer-related and treatment-related side-effects are also briefly described. More than 18 million Americans (8.3 million males and 9.7 million females) with a history of cancer were alive on January 1, 2022. The 3 most prevalent cancers are prostate (3,523,230), melanoma of the skin (760,640), and colon and rectum (726,450) among males and breast (4,055,770), uterine corpus (891,560), and thyroid (823,800) among females. More than one-half (53%) of survivors were diagnosed within the past 10 years, and two-thirds (67%) were aged 65 years or older. One of the largest racial disparities in treatment is for rectal cancer, for which 41% of Black patients with stage I disease receive proctectomy or proctocolectomy compared to 66% of White patients. Surgical receipt is also substantially lower among Black patients with non-small cell lung cancer, 49% for stages I-II and 16% for stage III versus 55% and 22% for White patients, respectively. These treatment disparities are exacerbated by the fact that Black patients continue to be less likely to be diagnosed with stage I disease than White patients for most cancers, with some of the largest disparities for female breast (53% vs 68%) and endometrial (59% vs 73%). Although there are a growing number of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based strategies and equitable access to available resources are needed to mitigate disparities for communities of color and optimize care for people with a history of cancer. CA Cancer J Clin. 2022;72:409-436.
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              Global patterns of breast cancer incidence and mortality: A population‐based cancer registry data analysis from 2000 to 2020

              Background Breast cancer is the most commonly diagnosed cancer and leading cause of cancer death among women worldwide but has patterns and trends which vary in different countries. This study aimed to evaluate the global patterns of breast cancer incidence and mortality and analyze its temporal trends for breast cancer prevention and control. Methods Breast cancer incidence and mortality data in 2020 were obtained from the GLOBOCAN online database. Continued data from the Cancer Incidence in Five Continents Time Trends, the International Agency for Research on cancer mortality and China National Central Cancer Registry were used to analyze the time trends from 2000 to 2015 through Joinpoint regression, and annual average percent changes of breast cancer incidence and mortality were calculated. Association between Human Development Index and breast cancer incidence and mortality were estimated by linear regression. Results There were approximately 2.3 million new breast cancer cases and 685,000 breast cancer deaths worldwide in 2020. Its incidence and mortality varied among countries, with the age‐standardized incidence ranging from the highest of 112.3 per 100,000 population in Belgium to the lowest of 35.8 per 100,000 population in Iran, and the age‐standardized mortality from the highest of 41.0 per 100,000 population in Fiji to the lowest of 6.4 per 100,000 population in South Korea. The peak age of breast cancer in some Asian and African countries were over 10 years earlier than in European or American countries. As for the trends of breast cancer, the age‐standardized incidence rates significantly increased in China and South Korea but decreased in the United States of America (USA) during 2000‐2012. Meanwhile, the age‐standardized mortality rates significantly increased in China and South Korea but decreased in the United Kingdom, the USA, and Australia during 2000 and 2015. Conclusions The global burden of breast cancer is rising fast and varies greatly among countries. The incidence and mortality rates of breast cancer increased rapidly in China and South Korea but decreased in the USA. Increased health awareness, effective prevention strategies, and improved access to medical treatment are extremely important to curb the snowballing breast cancer burden, especially in the most affected countries.
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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                04 August 2023
                2023
                : 13
                : 1068187
                Affiliations
                [1] 1 Fujian Medical University Union Hospital , Fuzhou, Fujian, China
                [2] 2 Department of Breast Surgery, Fujian Medical University Union Hospital , Fuzhou, Fujian, China
                [3] 3 Department of General Surgery, Fujian Medical University Union Hospital , Fuzhou, Fujian, China
                [4] 4 Department of Urology, Fujian Medical University Union Hospital , Fuzhou, Fujian, China
                [5] 5 Department of Breast Surgery, Women and Children’s Hospital, School of Medicine, Xiamen University , Xiamen, Fujian, China
                [6] 6 Breast Cancer Institute, Fujian Medical University , Fuzhou, Fujian, China
                Author notes

                Edited by: Anna Diana, Ospedale del Mare, Italy

                Reviewed by: Yutian Zou, Sun Yat-sen University Cancer Center (SYSUCC), China; Xuxu Gou, University of California, San Francisco, United States

                *Correspondence: Chunsen Xu, csxu@ 123456fjmu.edu.cn
                Article
                10.3389/fonc.2023.1068187
                10436472
                37601680
                2bc60bf7-0d2f-41d5-9622-9fc48a837ea3
                Copyright © 2023 Wen, Bai, Zheng, Liu, Lin, Han and Xu

                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
                : 12 October 2022
                : 20 July 2023
                Page count
                Figures: 7, Tables: 2, Equations: 0, References: 25, Pages: 10, Words: 3646
                Categories
                Oncology
                Original Research
                Custom metadata
                Breast Cancer

                Oncology & Radiotherapy
                male breast cancer,nomogram,predictive model,risk factors,seer
                Oncology & Radiotherapy
                male breast cancer, nomogram, predictive model, risk factors, seer

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