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Nik Ab Kadir MN, Mohd Hairon S, Ab Hadi IS, Yusof SN, Muhamat SM, Yaacob NM. A Comparison between the Online Prognostic Tool PREDICT and myBeST for Women with Breast Cancer in Malaysia. Cancers (Basel) 2023; 15:cancers15072064. [PMID: 37046725 PMCID: PMC10093426 DOI: 10.3390/cancers15072064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/18/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
The PREDICT breast cancer is a well-known online calculator to estimate survival probability. We developed a new prognostic model, myBeST, due to the PREDICT tool’s limitations when applied to our patients. This study aims to compare the performance of the two models for women with breast cancer in Malaysia. A total of 532 stage I to III patient records who underwent surgical treatment were analysed. They were diagnosed between 2012 and 2016 in seven centres. We obtained baseline predictors and survival outcomes by reviewing patients’ medical records. We compare PREDICT and myBeST tools’ discriminant performance using receiver-operating characteristic (ROC) analysis. The five-year observed survival was 80.3% (95% CI: 77.0, 83.7). For this cohort, the median five-year survival probabilities estimated by PREDICT and myBeST were 85.8% and 82.6%, respectively. The area under the ROC curve for five-year survival by myBeST was 0.78 (95% CI: 0.73, 0.82) and for PREDICT was 0.75 (95% CI: 0.70, 0.80). Both tools show good performance, with myBeST marginally outperforms PREDICT discriminant performance. Thus, the new prognostic model is perhaps more suitable for women with breast cancer in Malaysia.
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Affiliation(s)
- Mohd Nasrullah Nik Ab Kadir
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Suhaily Mohd Hairon
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Imi Sairi Ab Hadi
- Breast and Endocrine Surgery Unit, Department of Surgery, Hospital Raja Perempuan Zainab II, Ministry of Health Malaysia, Kota Bharu 15586, Kelantan, Malaysia
| | - Siti Norbayah Yusof
- Malaysian National Cancer Registry Department, National Cancer Institute, Ministry of Health Malaysia, Putrajaya 62250, Federal Territory of Putrajaya, Malaysia
| | - Siti Maryam Muhamat
- Malaysian National Cancer Registry Department, National Cancer Institute, Ministry of Health Malaysia, Putrajaya 62250, Federal Territory of Putrajaya, Malaysia
| | - Najib Majdi Yaacob
- Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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Parikh PM, Bhattacharyya GS, Biswas G, Krishnamurty A, Doval D, Heroor A, Sharma S, Deshpande R, Chaturvedi H, Somashekhar SP, Babu G, Reddy GK, Sarkar D, Desai C, Malhotra H, Rohagi N, Bapna A, Alurkar SS, Krishna P, Deo SV, Shrivastava A, Chitalkar P, Majumdar SK, Vijay D, Thoke A, Udupa KS, Bajpai J, Rath GK, Dattatreya PS, Bondarde S, Patil S. Practical Consensus Recommendations for Optimizing Risk versus Benefit of Chemotherapy in Patients with HR Positive Her2 Negative Early Breast Cancer in India. South Asian J Cancer 2021; 10:213-219. [PMID: 34984198 PMCID: PMC8719963 DOI: 10.1055/s-0041-1742080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Breast cancer is a public health challenge globally as well as in India. Improving outcome and cure requires appropriate biomarker testing to assign risk and plan treatment. Because it is documented that significant ethnic and geographical variations in biological and genetic features exist worldwide, such biomarkers need to be validated and approved by authorities in the region where these are intended to be used. The use of western guidelines, appropriate for the Caucasian population, can lead to inappropriate overtreatment or undertreatment in Asia and India. A virtual meeting of domain experts discussed the published literature, real-world practical experience, and results of opinion poll involving 185 oncologists treating breast cancer across 58 cities of India. They arrived at a practical consensus recommendation statement to guide community oncologists in the management of hormone positive (HR-positive) Her2-negative early breast cancer (EBC). India has a majority (about 50%) of breast cancer patients who are diagnosed in the premenopausal stage (less than 50 years of age). The only currently available predictive test for HR-positive Her2-negative EBC that has been validated in Indian patients is CanAssist Breast. If this test gives a score indicative of low risk (< 15.5), adjuvant chemotherapy will not increase the chance of metastasis-free survival and should not be given. This is applicable even during the ongoing COVID-19 pandemic.
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Affiliation(s)
| | | | - Ghanshyam Biswas
- Medical Oncology, Sparsh Hospital & Critical Care, Bhubaneswar, India
| | | | - Dinesh Doval
- Medical Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India
| | - Anil Heroor
- Surgical Oncology, Fortis Hospital, Mumbai, India
| | - Sanjay Sharma
- Surgical Oncology, Asian Cancer Institute, Mumbai, India
| | | | | | - S. P. Somashekhar
- Surgical Oncology, Manipal Comprehensive Cancer Center, Manipal Hospital, Bangalore, India
| | - Govind Babu
- Medical Oncology, HCG Cancer Hospital, Bengaluru, India
| | | | - Diptendra Sarkar
- Surgical Oncology, Institute of Post-Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial Hospital, Kolkata, India
| | - Chirag Desai
- Medical Oncology, Vedanta Institute of Medical Sciences, Ahmedabad, India
| | | | - Nitesh Rohagi
- Medical Oncology, Max Institute of Cancer Care, Delhi, India
| | - Ajay Bapna
- Medical Oncology, Bhagwan Mahaveer Cancer Hospital and Research Centre, Jaipur, India
| | | | - Prasad Krishna
- Medical Oncology, Mangalore Institute of Oncology, Mangalore, India
| | - S. V.S. Deo
- Surgical Oncology, All India Institute of Medical Sciences, Delhi, India
| | | | - Prakash Chitalkar
- Medical Oncology, Sri Aurobindo Medical College and Postgraduate Institute, Indore, India
| | | | | | - Aniket Thoke
- Radiation Oncology, Sanjeevani CBCC USA Cancer Hospital, Raipur, India
| | - K. S. Udupa
- Medical Oncology, Kasturba Medical College, Manipal, India
| | - Jyoti Bajpai
- Medical Oncology, Tata Memorial Hospital, Mumbai, India
| | - G. K. Rath
- Radiation Oncology, DR. B.R.A. Institute Rotary Cancer Hospital, Delhi, India
| | | | | | - Shekhar Patil
- Medical Oncology, HCG Cancer Hospital, Bengaluru, India
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Min N, Wei Y, Zheng Y, Li X. Advancement of prognostic models in breast cancer: a narrative review. Gland Surg 2021; 10:2815-2831. [PMID: 34733730 DOI: 10.21037/gs-21-441] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/13/2021] [Indexed: 11/06/2022]
Abstract
Objective To provide a reference for clinical work and guide the decision-making of healthcare providers and end-users, we systematically reviewed the development, validation and classification of classical prognostic models for breast cancer. Background Patients suffering from breast cancer have different prognosis for its high heterogeneity. Accurate prognosis prediction and risk stratification for breast cancer are crucial for individualized treatment. There is a lack of systematic summary of breast cancer prognostic models. Methods We conducted a PubMed search with keywords "breast neoplasm", "prognostic model", "recurrence" and "metastasis", and screened the retrieved publications at three levels: title, abstract and full text. We identified the articles presented the development and/or validation of models based on clinicopathological factors, genomics, and machine learning (ML) methods to predict survival and/or benefits of adjuvant therapy in female breast cancer patients. Conclusions Combining prognostic-related variables with long-term clinical outcomes, researchers have developed a series of prognostic models based on clinicopathological parameters, genomic assays, and medical figures. The discrimination, calibration, overall performance, and clinical usefulness were validated by internal and/or external verifications. Clinicopathological models integrated the clinical parameters, including tumor size, histological grade, lymph node status, hormone receptor status to provide prognostic information for patients and doctors. Gene-expression assays deeply revealed the molecular heterogeneity of breast cancer, some of which have been cited by AJCC and National Comprehensive Cancer Network (NCCN) guidelines. In addition, the models based on the ML methods provided more detailed information for prognosis prediction by increasing the data dimension. Combined models incorporating clinical variables and genomics information are still required to be developed as the focus of further researches.
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Affiliation(s)
- Ningning Min
- School of Medicine, Nankai University, Tianjin, China.,Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yufan Wei
- School of Medicine, Nankai University, Tianjin, China.,Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yiqiong Zheng
- Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- Department of General Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
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Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry. Cancers (Basel) 2021; 13:cancers13071567. [PMID: 33805407 PMCID: PMC8037061 DOI: 10.3390/cancers13071567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary Prognostication of breast cancer patients is essential for risk communication and clinical decision-making. Many clinical tools for the survival prediction of breast cancer patients have been developed over the years. However, most of them were developed from Western countries. Studies have shown that these tools did not perform well in other ethnicities, such as Asian populations, including Thai. This study developed a new prediction model for survival predictions using modern statistical methods that allow a more accurate estimation of the baseline survival. The model was entitled the Individualized Prediction of Breast cancer Survival or the IPBS model. It contains twelve routinely available predictors that oncologists usually evaluate in daily practice. The survival information provided by the model was proven to be acceptably accurate and might be useful for physicians and patients, especially in Thailand or other Asian countries, to arrive at the most appropriate management plan. Abstract Prognostic models for breast cancer developed from Western countries performed less accurately in the Asian population. We aimed to develop a survival prediction model for overall survival (OS) and disease-free survival (DFS) for Thai patients with breast cancer. We conducted a prognostic model research using a multicenter hospital-based cancer clinical registry from the Network of National Cancer Institutes of Thailand. All women diagnosed with breast cancer who underwent surgery between 1 January 2010 and 31 December 2011 were included in the analysis. A flexible parametric survival model was used for developing the prognostic model for OS and DFS prediction. During the study period, 2021 patients were included. Of these, 1386 patients with 590 events were available for a complete-case analysis. The newly derived individualized prediction of breast cancer survival or the IPBS model consists of twelve routinely available predictors. The C-statistics from the OS and the DFS model were 0.72 and 0.70, respectively. The model showed good calibration for the prediction of five-year OS and DFS. The IPBS model provides good performance for the prediction of OS and PFS for breast cancer patients. A further external validation study is required before clinical implementation.
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Bhattacharyya GS, Doval DC, Desai CJ, Chaturvedi H, Sharma S, Somashekhar S. Overview of Breast Cancer and Implications of Overtreatment of Early-Stage Breast Cancer: An Indian Perspective. JCO Glob Oncol 2020; 6:789-798. [PMID: 32511068 PMCID: PMC7328098 DOI: 10.1200/go.20.00033] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2020] [Indexed: 12/15/2022] Open
Abstract
The prevalence and mortality of breast cancer is increasing in Asian countries, including India. With advances in medical technology leading to better detection and characterization of the disease, it has been possible to classify breast cancer into various subtypes using markers, which helps predict the risk of distant recurrence, response to therapy, and prognosis using a combination of molecular and clinical parameters. Breast cancer and its therapy, mainly surgery, systemic therapy (anticancer chemotherapy, hormonal therapy, targeted therapy, and immunotherapy), and radiation therapy, are associated with significant adverse influences on physical and mental health, quality of life, and the economic status of the patient and her family. The fear of recurrence and its devastating effects often leads to overtreatment, with a toxic cost to the patient financially and physically in cases in which this is not required. This article discusses some aspects of a breast cancer diagnosis and its impact on the various facets of the life of the patient and her family. It further elucidates the role of prognostic factors, the currently available biomarkers and prognostic signatures, and the importance of ethnically validating biomarkers and prognostic signatures.
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Affiliation(s)
| | - Dinesh C. Doval
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Chirag J. Desai
- Vedanta Institute of Medical Sciences, Ahmedabad, Gujarat, India
| | | | - Sanjay Sharma
- Asian Cancer Institute, Somaiya Ayurvihar, Mumbai, Maharashtra, India
| | - S.P. Somashekhar
- Department of Surgical Oncology, Manipal Comprehensive Cancer Center, Manipal Hospital, Bengaluru, India
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Youn HJ, Han W. A Review of the Epidemiology of Breast Cancer in Asia: Focus on Risk Factors. Asian Pac J Cancer Prev 2020; 21:867-880. [PMID: 32334446 PMCID: PMC7445974 DOI: 10.31557/apjcp.2020.21.4.867] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Indexed: 01/11/2023] Open
Abstract
Background and Aim: Breast cancer is the most prevalent cancer in women. To date, regional differences in breast cancer risk factors have not been identified. The aim of our review was to gain a better understanding of the role of risk factors in women with breast cancer in Asia. Methods: We conducted a PubMed search on 15 March 2016, for journal articles published in English between 2011 and 2016, which reported data for human subjects in Asia with a diagnosis of breast cancer. Search terms included breast neoplasm, epidemiology, Asia, prevalence, incidence, risk and cost of illness. Studies of any design were included, except for review articles and meta-analyses, which were excluded to avoid duplication of data. No exclusions were made based on breast cancer treatment. We reported the results using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Results: A total of 776 abstracts were retrieved. After screening against the eligibility criteria, 562 abstracts were excluded. The remaining 214 abstracts, which were published between 2013 and 2015, were included in this review. Results were summarized and reported under three categories: incidence, prevalence or outcomes for breast cancer in Asia; modifiable risk factors; and non-modifiable risk factors. We found that the increased risk of breast cancer among participants from Asia was associated with older age, family history of breast cancer, early menarche, late menopause, high body mass index, being obese or overweight, exposure to tobacco smoke, and high dietary intake of fats or fatty foods. In contrast, intake of dietary fruits, vegetables, and plant- and soy-based products was associated with a decreased breast cancer risk. While based on limited data, when compared to women from the United States, women from Asia had a decreased risk of breast cancer. Conclusions: This review of 214 abstracts of studies in Asia, published between 2013 and 2015, confirmed the relevance of known non-modifiable and modifiable risk factors for women with breast cancer.
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Affiliation(s)
- Hyun Jo Youn
- Department of Surgery, Research Institute of Clinical Medicine, Chonbuk National University and Biomedical Research Institute, Chonbuk National University Hospital, Republic of Korea
| | - Wonshik Han
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul National University Cancer Hospital, Republic of Korea
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Avazpour N, Hajjari M, Kazemi Nezhad SR, Tahmasebi Birgani M. SNHG1 Long Noncoding RNA is Potentially Up-Regulated in Colorectal Adenocarcinoma. Asian Pac J Cancer Prev 2020; 21:897-901. [PMID: 32334448 DOI: 10.31557/apjcp.2020.21.4.897] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Indexed: 01/13/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common types of cancer worldwide. However, the molecular mechanisms involved in CRC initiation and progression is remained to be unknown. It seems that lncRNAs, as the main and lengthy functional transcripts of the genome, have important roles in different cancers such as CRC. CRC-related lncRNAs are reported to be involved in diverse molecular processes such as metastasis, invasion, cell proliferation, and apoptosis. This study was aimed to analyse the expression level of lncRNA SNHG1 in colorectal adenocarcinoma and normal tissues. We performed an in silico analysis on a large cohort and confirmed the results by experimental analysis of clinical samples through real-time PCR. Our findings demonstrated that that SNHG1 is potentially overexpressed in tumor tissues compared with adjacent normal tissues. The expression level of SNHG1 was shown to be potentially associated with clinicopathological features of tumors. The current study suggests the potential role of SNHG1 in colon cancer progression.
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Affiliation(s)
- Niloofar Avazpour
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Mohamadreza Hajjari
- Department of Genetics, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | | | - Maryam Tahmasebi Birgani
- Department of Medical Genetics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Mühlbauer V, Berger-Höger B, Albrecht M, Mühlhauser I, Steckelberg A. Communicating prognosis to women with early breast cancer - overview of prediction tools and the development and pilot testing of a decision aid. BMC Health Serv Res 2019; 19:171. [PMID: 30876414 PMCID: PMC6420759 DOI: 10.1186/s12913-019-3988-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 03/06/2019] [Indexed: 01/10/2023] Open
Abstract
Background Shared decision-making in oncology requires information on individual prognosis. This comprises cancer prognosis as well as competing risks of dying due to age and comorbidities. Decision aids usually do not provide such information on competing risks. We conducted an overview on clinical prediction tools for early breast cancer and developed and pilot-tested a decision aid (DA) addressing individual prognosis using additional chemotherapy in early, hormone receptor-positive breast cancer as an example. Methods Systematic literature search on clinical prediction tools for the effects of drug treatment on survival of breast cancer. The DA was developed following criteria for evidence-based patient information and International Patient Decision Aids Standards. We included data on the influence of age and comorbidities on overall prognosis. The DA was pilot-tested in focus groups. Comprehension was additionally evaluated through an online survey with women in breast cancer self-help groups. Results We identified three prediction tools: Adjuvant!Online, PREDICT and CancerMath. All tools consider age and tumor characteristics. Adjuvant!Online considers comorbidities, CancerMath displays age-dependent non-cancer mortality. Harm due to therapy is not reported. Twenty women participated in focus groups piloting the DA until data saturation was achieved. A total of 102 women consented to participate in the online survey, of which 86 completed the survey. The rate of correct responses was 90.5% and ranged between 84 and 95% for individual questions. Conclusions None of the clinical prediction tools fulfilled the requirements to provide women with all the necessary information for informed decision-making. Information on individual prognosis was well understood and can be included in patient decision aids. Electronic supplementary material The online version of this article (10.1186/s12913-019-3988-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Viktoria Mühlbauer
- MIN Faculty, Health Sciences and Education, University of Hamburg, Martin-Luther-King Platz 6, D-20146, Hamburg, Germany.
| | - Birte Berger-Höger
- MIN Faculty, Health Sciences and Education, University of Hamburg, Martin-Luther-King Platz 6, D-20146, Hamburg, Germany
| | - Martina Albrecht
- MIN Faculty, Health Sciences and Education, University of Hamburg, Martin-Luther-King Platz 6, D-20146, Hamburg, Germany
| | - Ingrid Mühlhauser
- MIN Faculty, Health Sciences and Education, University of Hamburg, Martin-Luther-King Platz 6, D-20146, Hamburg, Germany
| | - Anke Steckelberg
- MIN Faculty, Health Sciences and Education, University of Hamburg, Martin-Luther-King Platz 6, D-20146, Hamburg, Germany.,Institute for Health and Nursing Science, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, D-06112, Halle, Germany
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Phung MT, Tin Tin S, Elwood JM. Prognostic models for breast cancer: a systematic review. BMC Cancer 2019; 19:230. [PMID: 30871490 PMCID: PMC6419427 DOI: 10.1186/s12885-019-5442-6] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 03/06/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer. METHODS We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients. RESULTS From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations. CONCLUSIONS Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.
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Affiliation(s)
- Minh Tung Phung
- Epidemiology and Biostatistics, School of Population Health, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
| | - Sandar Tin Tin
- Epidemiology and Biostatistics, School of Population Health, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
| | - J Mark Elwood
- Epidemiology and Biostatistics, School of Population Health, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
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Bhoo-Pathy NT, Inaida S, Tanaka S, Taib NA, Yip CH, Saad M, Kawakami K, Bhoo-Pathy N. Impact of adjuvant chemotherapy on survival of women with T1N0M0, hormone receptor negative breast cancer. Cancer Epidemiol 2017; 48:56-61. [DOI: 10.1016/j.canep.2017.03.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 03/02/2017] [Accepted: 03/19/2017] [Indexed: 10/19/2022]
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Prognostic contribution of mammographic breast density and HER2 overexpression to the Nottingham Prognostic Index in patients with invasive breast cancer. BMC Cancer 2016; 16:833. [PMID: 27806715 PMCID: PMC5094093 DOI: 10.1186/s12885-016-2892-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 10/25/2016] [Indexed: 01/19/2023] Open
Abstract
Background To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance. Methods This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of <10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI. Results Both mammographic breast density (VLD) (p < 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and high risk groups of the original NPI classification. The incorporation of both factors (VLD and HER2 status) into the NPI provided improved patient outcome stratification by decreasing the percentage of patients in the intermediate prognostic groups, moving a substantial percentage towards the low and high risk prognostic groups. Conclusions Very low density (VLD) and HER2 positivity were prognostically significant factors independent of the NPI. Furthermore, the incorporation of VLD and HER2 to the NPI served to enhance its accuracy, thus offering a readily available and more accurate method for the evaluation of patient prognosis.
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Miao H, Hartman M, Verkooijen HM, Taib NA, Wong HS, Subramaniam S, Yip CH, Tan EY, Chan P, Lee SC, Bhoo-Pathy N. Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia. BMC Cancer 2016; 16:820. [PMID: 27769212 PMCID: PMC5073834 DOI: 10.1186/s12885-016-2841-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 10/05/2016] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND CancerMath is a set of web-based prognostic tools which predict nodal status and survival up to 15 years after diagnosis of breast cancer. This study validated its performance in a Southeast Asian setting. METHODS Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI). RESULTS The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76). CONCLUSIONS The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.
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Affiliation(s)
- Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore.
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore.,Department of Surgery, National University Hospital, 1E Kent Ridge Road, Singapore, 119228, Singapore.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, SE-171 77, Stockholm, Sweden
| | - Helena M Verkooijen
- Imaging Division, University Medical Center Utrecht, PO Box 85500, 3508, GA, Utrecht, The Netherlands
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Hoong-Seam Wong
- Clinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia
| | - Shridevi Subramaniam
- Clinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia
| | - Cheng-Har Yip
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Ern-Yu Tan
- Department of Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore
| | - Patrick Chan
- Department of Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore
| | - Soo-Chin Lee
- Department of Hematology Oncology, National University Cancer Institute, National University Health System, 1E Kent Ridge Road, Singapore, 119228, Singapore
| | - Nirmala Bhoo-Pathy
- Clinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia.,Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Julius Center for Health Sciences and Primary Care, University Medical Center, PO Box 85500, 3508, AB, Utrecht, The Netherlands
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13
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Rejali M, Tazhibi M, Mokarian F, Gharanjik N, Mokarian R. The Performance of the Nottingham Prognosis Index and the Adjuvant Online Decision Making Tool for Prognosis in Early-stage Breast Cancer Patients. Int J Prev Med 2015; 6:93. [PMID: 26605014 PMCID: PMC4629295 DOI: 10.4103/2008-7802.166503] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2015] [Accepted: 06/02/2015] [Indexed: 11/04/2022] Open
Abstract
Background: Prognostic tools are widely used in the practice of Oncology and have been developed to help stratify patients into specific risk-related grouping. We sought to apply of two such tools used for patients with early-stage breast cancer and to correlate them with actual outcomes. Methods: A retrospective study was designed to include early-stage breast cancer cases seen from 1994 to 2014 at the Seyedoshohada Hospital in Isfahan, Iran. Information was derived from the patients’ records, and indices were derived from prognostic tools. Information was analyzed using descriptive statistics and one sample t-test. Results: In 233 patients, the difference between the predicted overall survival (OS) by the Adjuvant Online (AO) prognosis tools (69.28) and the observed OS (71.2) was not statistically significant (P = 0.52), and the AO prognosis tools had predicted the patients’ OS correctly. In the Nottingham prognosis index (NPI), this difference in all groups except the very poor prognosis group was not statistically significant. Conclusions: Adjuvant Online prognosis tools were capable of predicting the 10-year OS rate although not in all of the subgroups. The NPI was capable of distinguishing good, moderate, and poor survival rates, but this ability was not visible in more specific groups with moderate and poor prognosis.
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Affiliation(s)
- Mehri Rejali
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehdi Tazhibi
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fariborz Mokarian
- Department of Internal Medicine, Isfahan University of Medical Sciences, Breast Cancer Research Group, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Nazjamal Gharanjik
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Reyhane Mokarian
- Department of Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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14
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Eissa S, Azzazy HME, Matboli M, Shawky SM, Said H, Anous FA. The prognostic value of histidine-rich glycoprotein RNA in breast tissue using unmodified gold nanoparticles assay. Appl Biochem Biotechnol 2015; 174:751-61. [PMID: 25091325 DOI: 10.1007/s12010-014-1085-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The aim of is this study is to explore the role of tissue histidine-rich glycoprotein (HRG) RNA as a promising clinically useful biomarker for breast cancer patients prognosis using nanogold assay. Expression of the HRG RNA was assessed by gold nanoparticles and conventional RT-PCR after purification by magnetic nanoparticles in breast tissue samples. The study included 120 patients, 60 of which were histologically proven breast carcinoma cases, 30 had benign breast lesions and 30 were healthy individuals who had undergone reductive plastic surgery. ER, PR and HER2 status were also investigated. The prognostic significance of tissue HRG RNA expression in breast cancer was explored. The magnetic nanoparticles coated with specific thiol modified oligonucleotide probe were used successfully in purification of HRG RNA from breast tissue total RNAs with satisfactory yield. The developed HRG AuNPs assay had a sensitivity and a specificity of 90 %, and a detection limit of 1.5 nmol/l. The concordance rate between the HRG AuNPs assay with RT-PCR after RNA purification using magnetic nanoparticles was 93.3 %. The median follow-up period was 60 months. Among traditional prognostic biomarkers, HRG was a significant independent prognostic marker in relapse-free survival (RFS). HRG RNA is an independent prognostic marker for breast cancer and can be detected using gold NPs assay, which is rapid, sensitive, specific, inexpensive to extend the value for breast cancer prognosis.
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Affiliation(s)
- Sanaa Eissa
- Oncology Diagnostic Unit, Medical Biochemistry and Molecular biology Department, Faculty of Medicine, Ain Shams University, P.O. Box 11381, Abbassia, Cairo, Egypt,
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15
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Wong HS, Subramaniam S, Alias Z, Taib NA, Ho GF, Ng CH, Yip CH, Verkooijen HM, Hartman M, Bhoo-Pathy N. The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer. Medicine (Baltimore) 2015; 94:e593. [PMID: 25715267 PMCID: PMC4554151 DOI: 10.1097/md.0000000000000593] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.
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Affiliation(s)
- Hoong-Seam Wong
- From the National Clinical Research Centre (HSW, SS), Level 3, Dermatology Block, Kuala Lumpur Hospital, Jalan Pahang; Department of Surgery (ZA, NAT, CHN, CHY); Department of Oncology (GFH), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Imaging Division (HMV), University Medical Center Utrecht, Utrecht, The Netherlands; Saw Swee Hock School of Public Health (HMV, MH), National University of Singapore; Department of Surgery (MH), Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; Julius Centre University of Malaya (NBP), Centre for Clinical Epidemiology and Evidence-Based Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; and Julius Center for Health Sciences and Primary Care (NBP), University Medical Center Utrecht, Utrecht, The Netherlands
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16
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Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models. Br J Cancer 2015; 112:912-7. [PMID: 25590666 PMCID: PMC4453945 DOI: 10.1038/bjc.2014.641] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 10/11/2014] [Accepted: 12/01/2014] [Indexed: 12/20/2022] Open
Abstract
Background: Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level. Methods: We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction. Results: Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71–0.77) for the Cancermath.net model and 0.72 (95% CI 0.69–0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.08 (range 0.06–0.10). Dispersion, however, reached 30% for the subgroups with a predicted survival between 10 and 50%. Conclusion: This study revealed that the three web-based predictors equally perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups.
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