1
|
Berg T, Jensen MB, Rossing M, Axelsen CT, Kümler I, Søndergaard L, Vogsen M, Knoop AS, Ejlertsen B. Incidence and survival of primary metastatic breast cancer in Denmark; implication of breast cancer screening, classification, and staging practice. Acta Oncol 2024; 63:277-287. [PMID: 38711384 PMCID: PMC11332488 DOI: 10.2340/1651-226x.2023.37270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Primary metastatic breast cancer (pMBC) accounts for 5-10% of annual breast cancers with a median survival of 3-4 years, varying among subtypes. In Denmark, the incidence of breast cancer increased until 2010, followed by a stabilisation. Several factors influencing pMBC incidence and survival, including screening prevalence, staging methods, and classification standards, remain pivotal but inadequately documented. MATERIAL AND METHOD This retrospective observational study involving pMBC patients diagnosed between 2000 and 2020 encompassed all Danish oncology departments. Data from the Danish Breast Cancer Group database and the National Patient Register included diagnosis specifics, demographics, treatment, and follow-up. RESULTS Between 2000 and 2020, 3,272 patients were diagnosed with pMBC, a rise from 355 patients in 2000-2004 to 1,323 patients in 2015-2020. The increase was particularly observed in patients aged 70 years or older. Changes in tumour subtypes were observed, notably with a rise in human epidermal growth factor receptor 2 (HER2)-positive cases but a steady distribution of estrogen receptor (ER) status. Diagnostic practices changed over the two decades, with 6% evaluated with PET/CT (positron emission tomography-computed tomography) or CT (computed tomography) with a bone evaluation in 2000-2004 and 65% in 2015-2020. Overall survival (OS) improved from 23 months in 2000-2004 to 33 months in 2015-2020. In patients with ER-positive and HER2-positive disease, the multivariable model showed improved survival by year of diagnosis, and further, patients with ER-negative/HER2-negative disease fared worse the first 2 years after diagnosis. INTERPRETATION Our study delineates changes in the treatment and survival of pMBC over two decades. Stage migration, screening introduction, and changes in registration practice, however, prevent a valid assessment of a possible causal relationship.
Collapse
Affiliation(s)
- Tobias Berg
- Danish Breast Cancer Group, Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark.
| | - Maj-Britt Jensen
- Danish Breast Cancer Group, Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Maria Rossing
- Center for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Iben Kümler
- Department of Oncology, Copenhagen University Hospital, Herlev Hospital, Herlev, Denmark
| | - Lise Søndergaard
- Department of Oncology, Zealand University Hospital, Næstved Sygehus, Denmark
| | - Marianne Vogsen
- Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ann S Knoop
- anish Breast Cancer Group, Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Bent Ejlertsen
- anish Breast Cancer Group, Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
2
|
Zhao J, Bian S, Di X, Xiao C. A Nomogram and Risk Classification System Predicting the Prognosis of Patients with De Novo Metastatic Breast Cancer Undergoing Immediate Breast Reconstruction: A Surveillance, Epidemiology, and End Results Population-Based Study. Curr Oncol 2023; 31:115-131. [PMID: 38248093 PMCID: PMC10814717 DOI: 10.3390/curroncol31010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/10/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666-0.748] in the training group and 0.702 (95% CI, 0.639-0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770-0.943), 0.747 (95% CI, 0.689-0.804), and 0.700 (95% CI, 0.643-0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733-0.947), 0.763 (95% CI, 0.677-0.849), and 0.709 (95% CI, 0.623-0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making.
Collapse
Affiliation(s)
- Jingjing Zhao
- Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, Tianjin 300140, China; (J.Z.); (S.B.)
| | - Shichang Bian
- Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, Tianjin 300140, China; (J.Z.); (S.B.)
| | - Xu Di
- Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, Tianjin 300140, China; (J.Z.); (S.B.)
| | - Chunhua Xiao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China
| |
Collapse
|
3
|
Ning L, Liu Y, Hou Y, Wang M, Shi M, Liu Z, Zhao J, Liu X. Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort. CANCER PATHOGENESIS AND THERAPY 2023; 1:253-261. [PMID: 38327599 PMCID: PMC10846327 DOI: 10.1016/j.cpt.2023.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 02/09/2024]
Abstract
Background On average, 5-10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients. Methods The nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China. Results Multivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682-0.694) in the training cohort and 0.875 (95% CI, 0.816-0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels. Conclusions The nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.
Collapse
Affiliation(s)
- Lizhi Ning
- Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yaobang Liu
- Department of Surgical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yujin Hou
- Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Miaozhou Wang
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University & Affiliated Cancer Hospital of Qinghai University, Xining, Qinghai 810000, China
| | - Mingqiang Shi
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University & Affiliated Cancer Hospital of Qinghai University, Xining, Qinghai 810000, China
| | - Zhen Liu
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University & Affiliated Cancer Hospital of Qinghai University, Xining, Qinghai 810000, China
| | - Jiuda Zhao
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University & Affiliated Cancer Hospital of Qinghai University, Xining, Qinghai 810000, China
| | - Xinlan Liu
- Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| |
Collapse
|
4
|
Schiza A, Fredriksson I, Sund M, Valachis A. De novo metastatic breast cancer in men vs women: a Swedish population-based cohort study. JNCI Cancer Spectr 2023; 7:pkad050. [PMID: 37490458 PMCID: PMC10444027 DOI: 10.1093/jncics/pkad050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/29/2023] [Accepted: 07/17/2023] [Indexed: 07/27/2023] Open
Abstract
Current evidence on de novo metastatic breast cancer is based on data from women. This Swedish population-based cohort study compared the incidence over time and prognosis of de novo metastatic breast cancer between sexes using data from the Swedish National Quality Register for Breast Cancer. Joinpoint regression analysis was used to compare incidence trends in all stages (104 733 women, 648 men) and multivariate Cox regression analysis to investigate potential sex disparities in de novo metastatic breast cancer prognosis (6005 women, 41 men). For both sexes, increased trends were evident for cancer stages I and II, with a stabilizing trend at the later years for women, while stage III incidence remained stable. An increased trend for de novo metastatic breast cancer in women, and to a lesser extent in men, was observed. No difference in de novo metastatic breast cancer overall survival between sexes was observed (hazard ratio = 1.24; 95% confidence interval = 0.85 to 1.81). The comparable features in terms of incidence and prognosis of de novo metastatic breast cancer between sexes imply similarities, supporting the adoption of common treatment strategies.
Collapse
Affiliation(s)
- Aglaia Schiza
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Oncology, Uppsala University Hospital, Uppsala, Sweden
| | - Irma Fredriksson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Breast, Endocrine Tumours and Sarcoma, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Malin Sund
- Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden
- Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Antonios Valachis
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Oncology, Faculty of Medicine and Health, Orebro University, Orebro, Sweden
| |
Collapse
|
5
|
Im SA, Gennari A, Park YH, Kim JH, Jiang ZF, Gupta S, Fadjari TH, Tamura K, Mastura MY, Abesamis-Tiambeng MLT, Lim EH, Lin CH, Sookprasert A, Parinyanitikul N, Tseng LM, Lee SC, Caguioa P, Singh M, Naito Y, Hukom RA, Smruti BK, Wang SS, Kim SB, Lee KH, Ahn HK, Peters S, Kim TW, Yoshino T, Pentheroudakis G, Curigliano G, Harbeck N. Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer. ESMO Open 2023; 8:101541. [PMID: 37178669 PMCID: PMC10186487 DOI: 10.1016/j.esmoop.2023.101541] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 03/27/2023] [Accepted: 04/01/2023] [Indexed: 05/15/2023] Open
Abstract
The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer (MBC) was published in 2021. A special, hybrid guidelines meeting was convened by ESMO and the Korean Society of Medical Oncology (KSMO) in collaboration with nine other Asian national oncology societies in May 2022 in order to adapt the ESMO 2021 guidelines to take into account the differences associated with the treatment of MBC in Asia. These guidelines represent the consensus opinions reached by a panel of Asian experts in the treatment of patients with MBC representing the oncological societies of China (CSCO), India (ISMPO), Indonesia (ISHMO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), the Philippines (PSMO), Singapore (SSO), Taiwan (TOS) and Thailand (TSCO). The voting was based on the best available scientific evidence and was independent of drug access or practice restrictions in the different Asian countries. The latter were discussed when appropriate. The aim of these guidelines is to provide guidance for the harmonisation of the management of patients with MBC across the different regions of Asia, drawing from data provided by global and Asian trials whilst at the same time integrating the differences in genetics, demographics and scientific evidence, together with restricted access to certain therapeutic strategies.
Collapse
Affiliation(s)
- S-A Im
- Department of Internal Medicine, Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Seoul National University, Seoul, Republic of Korea.
| | - A Gennari
- Department of Translational Medicine, University Piemonte Orientale, Novara, Italy
| | - Y H Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - J H Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Z-F Jiang
- Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - S Gupta
- Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, India
| | - T H Fadjari
- Department of Internal Medicine, Hasan Sadikin General Hospital, Bandung, Indonesia
| | - K Tamura
- Department of Medical Oncology, Shimane University Hospital, Shimane, Japan
| | - M Y Mastura
- Cancer Centre, Pantai Hospital Kuala Lumpur, Kuala Lumpur, Malaysia
| | - M L T Abesamis-Tiambeng
- Section of Medical Oncology, Department of Internal Medicine, Cardinal Santos Cancer Center, San Juan, The Philippines
| | - E H Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - C-H Lin
- Department of Medical Oncology, National Taiwan University Hospital, Cancer Center Branch, Taipei, Taiwan
| | - A Sookprasert
- Department of Internal Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - N Parinyanitikul
- Medical Oncology Unit, Department of Medicine, Faculty of Medicine, King Chulalongkorn Memorial Hospital and Chulalongkorn University, Bangkok, Thailand
| | - L-M Tseng
- Taipei-Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - S-C Lee
- Department of Haematology-Oncology, National University Cancer Institute, Singapore (NCIS), Singapore, Singapore
| | - P Caguioa
- The Cancer Institute of St Luke's Medical Center, National Capital Region, The Philippines; The Cancer Institute of the University of Santo Tomas Hospital, National Capital Region, The Philippines
| | - M Singh
- Department of Radiotherapy, Pantai Cancer Institute, Pantai Hospital Kuala Lumpur, Kuala Lumpur, Malaysia; Department of Oncology, Pantai Cancer Institute, Pantai Hospital Kuala Lumpur, Kuala Lumpur, Malaysia
| | - Y Naito
- Department of General Internal Medicine, National Cancer Center Hospital East, Kashiwa, Japan
| | - R A Hukom
- Department of Hematology and Medical Oncology, Dharmais Hospital (National Cancer Center), Jakarta, Indonesia
| | - B K Smruti
- Medical Oncology, Lilavati Hospital and Research Centre and Bombay Hospital Institute of Medical Sciences, Mumbai, India
| | - S-S Wang
- Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - S B Kim
- Department of Oncology, Asan Medical Centre, Seoul, Republic of Korea
| | - K-H Lee
- Department of Internal Medicine, Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - H K Ahn
- Division of Medical Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - S Peters
- Oncology Department, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - T W Kim
- Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - T Yoshino
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | | | - G Curigliano
- Istituto Europeo di Oncologia, IRCCS, Milan, Italy; Department of Oncology and Haematology, University of Milano, Milan, Italy
| | - N Harbeck
- Breast Center, Department of Obstetrics and Gynaecology and Comprehensive Cancer Center Munich, LMU University Hospital, Munich, Germany
| |
Collapse
|
6
|
Almasri H, Erjan A, Abudawaba H, Ashouri K, Mheid S, Alnsour A, Abdel-Razeq H. Clinical Characteristics and Survival Outcomes of Patients with De Novo Metastatic Breast Cancer. BREAST CANCER (DOVE MEDICAL PRESS) 2022; 14:363-373. [PMID: 36340331 PMCID: PMC9628702 DOI: 10.2147/bctt.s383874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/01/2022] [Indexed: 11/17/2022]
Abstract
Purpose Though less than 5% of patients with breast cancer present with De Novo Metastasis (dnMBC) in Western societies, this percentage may reach 30% in developing countries. In this study, we present survival outcomes of patients diagnosed with dnMBC treated at a tertiary center in a developing country. Patients and Methods Using hospital-based database, consecutive patients with dnMBC diagnosed between 2013 and 2017 were identified. Demographic data, tumor characteristics, types of treatment, and survival data were retrospectively collected. Results A total of 435 patients were included; median age (range) at time of diagnosis was 51 (24-85) years. Most of the tumors expressed hormone receptors (81% Estrogen Receptor positive, 77% Progesterone Receptor positive). Human epidermal growth factor receptor-2 (HER2) overexpression was reported in 134 (30.9%) patients, while only 24 (5.5%) had Triple Negative (TN) disease. Bone, lung and liver were the most common sites of metastasis involved in 70.6%, 36.1%, and 32.0%, respectively. The median Overall Survival (OS) for all patients was 38 months, and 5-year OS was 32.6%. On univariate analysis, high tumor grade, advanced T-stage, TN-disease and metastasis to multiple sites, but not HER2 status, were associated with poor OS. On multivariate analysis, high tumor grade (Hazard Ratio =1.6, p=0.002), advanced T-stage (Hazard Ratio=1.6, p=0.003), and triple negative status (Hazard Ratio= 2.1, p=0.008) predicted poor OS. Conclusion The overall survival of patients with dnMBC remains poor. Better understanding of the disease behavior and factors affecting survival is required for optimal utilization of available regimens and new drugs to hopefully improve patients' outcomes.
Collapse
Affiliation(s)
- Hanan Almasri
- Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Ayah Erjan
- Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Hebah Abudawaba
- Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Khaled Ashouri
- Department of Internal Medicine, King Hussein Cancer Center, Amman, Jordan
| | - Sara Mheid
- Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Anoud Alnsour
- Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Hikmat Abdel-Razeq
- Department of Internal Medicine, King Hussein Cancer Center, Amman, Jordan,Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan,Correspondence: Hikmat Abdel-Razeq, Department of Internal Medicine, King Hussein Cancer Center, 202 Queen Rania Al-Abdulla St., P.O. Box 1269, Al-Jubeiha, Amman, 11941, Jordan, Tel +96265300460, Email
| |
Collapse
|
7
|
Dhillon SK, Ganggayah MD, Sinnadurai S, Lio P, Taib NA. Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis. Diagnostics (Basel) 2022; 12:2526. [PMID: 36292218 PMCID: PMC9601117 DOI: 10.3390/diagnostics12102526] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/26/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.
Collapse
Affiliation(s)
- Sarinder Kaur Dhillon
- Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Mogana Darshini Ganggayah
- Department of Econometrics and Business Statistics, School of Business, Monash University Malaysia, Kuala Lumpur 47500, Malaysia
| | - Siamala Sinnadurai
- Department of Population Medicine and Lifestyle Disease Prevention, Medical University of Bialystok, 15-269 Bialystok, Poland
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| |
Collapse
|
8
|
Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1249692. [PMID: 35509861 PMCID: PMC9060999 DOI: 10.1155/2022/1249692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/29/2022] [Indexed: 11/23/2022]
Abstract
Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.
Collapse
|
9
|
Tang X, Chen F, Xie LC, Liu SX, Mai HR. Targeting metabolism: A potential strategy for hematological cancer therapy. World J Clin Cases 2022; 10:2990-3004. [PMID: 35647127 PMCID: PMC9082716 DOI: 10.12998/wjcc.v10.i10.2990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/01/2021] [Accepted: 02/27/2022] [Indexed: 02/06/2023] Open
Abstract
Most hematological cancer-related relapses and deaths are caused by metastasis; thus, the importance of this process as a target of therapy should be considered. Hematological cancer is a type of cancer in which metabolism plays an essential role in progression. Therefore, we are required to block fundamental metastatic processes and develop specific preclinical and clinical strategies against those biomarkers involved in the metabolic regulation of hematological cancer cells, which do not rely on primary tumor responses. To understand progress in this field, we provide a summary of recent developments in the understanding of metabolism in hematological cancer and a general understanding of biomarkers currently used and under investigation for clinical and preclinical applications involving drug development. The signaling pathways involved in cancer cell metabolism are highlighted and shed light on how we could identify novel biomarkers involved in cancer development and treatment. This review provides new insights into biomolecular carriers that could be targeted as anticancer biomarkers.
Collapse
Affiliation(s)
- Xue Tang
- Department of Hematology and Oncology, Shenzhen Children’s Hospital, Shenzhen 518038, Guangdong Province, China
| | - Fen Chen
- Department of Hematology and Oncology, Shenzhen Children’s Hospital, Shenzhen 518038, Guangdong Province, China
| | - Li-Chun Xie
- Department of Hematology and Oncology, Shenzhen Children’s Hospital, Shenzhen 518038, Guangdong Province, China
| | - Si-Xi Liu
- Department of Hematology and Oncology, Shenzhen Children’s Hospital, Shenzhen 518038, Guangdong Province, China
| | - Hui-Rong Mai
- Department of Hematology and Oncology, Shenzhen Children’s Hospital, Shenzhen 518038, Guangdong Province, China
| |
Collapse
|
10
|
Zhou J, Wu L, Xu P, Li Y, Ji Z, Kang X. Filamin A Is a Potential Driver of Breast Cancer Metastasis via Regulation of MMP-1. Front Oncol 2022; 12:836126. [PMID: 35359350 PMCID: PMC8962737 DOI: 10.3389/fonc.2022.836126] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/14/2022] [Indexed: 01/01/2023] Open
Abstract
Recurrent metastasis is a major fatal cause of breast cancer. Regretfully, the driving force and the molecular beneath have not been fully illustrated yet. In this study, a cohort of breast cancer patients with locoregional metastasis was recruited. For them, we collected the matched samples of the primary tumor and metastatic tumor, and then we determined the mutation profiles with whole-exome sequencing (WES). On basis of the profiles, we identified a list of deleterious variants in eight susceptible genes. Of them, filamin A (FLNA) was considered a potential driver gene of metastasis, and its low expression could enhance 5 years’ relapse survival rate by 15%. To prove the finding, we constructed a stable FLNA knockout tumor cell line, which manifested that the cell abilities of proliferation, migration, and invasion were significantly weakened in response to the gene knockout. Subsequently, xenograft mouse experiments further proved that FLNA knockout could inhibit local or distal metastasis. Putting all the results together, we consolidated that FLNA could be a potential driver gene to metastasis of breast cancer, in particular triple-negative breast cancer. Additional experiments also suggested that FLNA might intervene in metastasis via the regulation of MMP-1 expression. In summary, this study demonstrates that FLNA may play as a positive regulator in cancer proliferation and recurrence. It provides new insight into breast cancer metastasis and suggests a potential new therapeutic target for breast cancer therapy.
Collapse
Affiliation(s)
- Jie Zhou
- Department of Oncology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lvying Wu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Pengyan Xu
- Department of Surgical Research, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Yue Li
- Department of Oncology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhiliang Ji
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- *Correspondence: Xinmei Kang, ; Zhiliang Ji,
| | - Xinmei Kang
- Department of Oncology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Xinmei Kang, ; Zhiliang Ji,
| |
Collapse
|
11
|
Epidemiology of De Novo Metastatic Breast Cancer. Clin Breast Cancer 2021; 21:302-308. [PMID: 33750642 DOI: 10.1016/j.clbc.2021.01.017] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/27/2020] [Accepted: 01/24/2021] [Indexed: 11/22/2022]
Abstract
Most cases of metastatic breast cancer (MBC) arise as a recurrence of a previously treated early breast cancer. Distinct from recurrent MBC is de novo MBC (dnMBC), which describes patients who present with distant sites of disease at initial diagnosis and is reviewed here. dnMBC represents approximately 3% to 6% of new breast cancer diagnoses in high-income countries. This incidence has not declined despite decades of widespread use of population-based mammography screening. Overrepresentation of both biologically aggressive tumors and patients negatively impacted by social determinants of health are characteristics of dnMBC. Survival has generally been superior for patients with dnMBC compared with those with recurrent MBC, although it is similar to that for patients with recurrent MBC with long disease-free intervals. Subgroups of patients with dnMBC who experience prolonged survival include those with human epidermal growth factor receptor-2-positive disease or hormone receptor-positive bone-only disease. Opportunities to decrease dnMBC presentation may include novel screening modalities suited for biologically aggressive breast tumors and improved access to health care. Recognizing that there will remain some women diagnosed with dnMBC, refining our ability to identify those likely to be long-term survivors could allow for appropriate escalation or de-escalation of care. Finally, evaluation of tumor genomics in robust sample sizes has the potential to advance our knowledge of the biology of dnMBC as an entity distinct from recurrent MBC.
Collapse
|
12
|
Locoregional Therapy for the Primary Tumour in Women with a De Novo Diagnosis of Metastatic Breast Cancer. CURRENT BREAST CANCER REPORTS 2021. [DOI: 10.1007/s12609-021-00408-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
13
|
Tang ELS, Sin PY, Chen JJC, Chan MYP, Seah MDW, Lu SQ, Goh MH, Tan EY. Understanding the Psychosocial Needs of Women who Present with Advanced Breast Cancer. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2020; 49:990-995. [PMID: 33463657 DOI: 10.47102/annals-acadmedsg.2020129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Advanced breast cancer (ABC) remains common in Singapore. In 2019, 22.1% of breast cancer patients presented with ABC in our institution. Despite increasing affluence and the advent of national mammographic screening, the incidence of ABC has not changed significantly. This suggests inherent differences in women who present late. We aim to explore the socio-economic background, knowledge and attitudes of women who present with ABC. METHODS Between December 2013 and July 2015, 100 patients who presented consecutively with ABC in a tertiary institution in Singapore were recruited to participate in an interviewer-led questionnaire exploring psychosocial and economic issues. RESULTS Among the 100 patients, 63 and 37 presented with stages 3 and 4 breast cancer respectively. Median age was 57 (27-86), 52% had at least secondary education, 53% had no formal employment and 71% were married; 88% were aware of breast cancer symptoms, 82% were aware that mammography can help detect cancer, 82% believed that current treatment modality for breast cancer is effective, 96% had never undergone a mammography and 52.9% felt mammograms were unnecessary. A total of 64% presented symptomatic from the breast tumour, with a median duration of 3 months. Many of the patients were aware of breast cancer symptoms and the utility of mammography. However, a group of patients did not comply with screening. This may be due to poor understanding about breast screening and detection in its asymptomatic phase. CONCLUSION Further public education to improve understanding of breast cancer and screening mammography may help to improve rates for earlier detection of breast cancer.
Collapse
|
14
|
Bossi P, Airoldi M, Aloe Spiriti MA, Antonuzzo A, Bonciarelli G, Campagna A, Cassano A, Murialdo R, Musio D, Silvano G. A multidisciplinary expert opinion on CINV and RINV, unmet needs and practical real-life approaches. Expert Opin Drug Saf 2020; 19:187-204. [PMID: 32005072 DOI: 10.1080/14740338.2020.1724955] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Introduction: A range of combination chemotherapy regimens are currently used in clinical practice. However, international antiemetic guidelines often only categorize the emetogenic potential of single agents rather than the emetogenicity of combination chemotherapy regimens. To manage the nausea and vomiting induced by antineoplastic combinations, guidelines suggest antiemetics that are appropriate for the component drug with the highest emetogenic potential. Furthermore, antiemetic guidelines generally do not consider the influence of other factors, including individual patient characteristics, on the emetic effects of cancer treatments. Similarly, the emetogenic potential of radiotherapy is stratified only according to the site of radiation, while other factors contributing to emetic risk are overlooked.Areas covered: An Expert Panel was convened to examine unresolved issues and summarize the current clinical research on managing nausea and vomiting associated with combination chemotherapy and radiotherapy.Expert opinion: The panel identified the incidence of nausea and vomiting induced by multi-drug combination therapies currently used to treat cancer at different anatomic sites and by radiotherapy in the presence of other risk factors. Based on these data and the clinical experience of panel members, several suggestions are made for a practical approach to prevent or manage nausea and vomiting due to chemotherapy regimens and radiation therapy.
Collapse
Affiliation(s)
- Paolo Bossi
- Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health University of Brescia, ASST-Spedali Civili, Brescia, Italy
| | - Mario Airoldi
- Oncology Departement, Città della Salute e della Scienza Hospital of Turin, Turin, Italy
| | - Maria Antonietta Aloe Spiriti
- Department of Clinical and Molecular Medicine, Azienda Universitaria Ospedaliera Sant'Andrea, "Sapienza" University of Rome, Rome, Italy
| | - Andrea Antonuzzo
- Medical Oncology Unit 1 SSN, Pisa University Hospital, Pisa, Italy
| | | | - Alessia Campagna
- Department of Hematology, Azienda Universitaria Ospedaliera Sant'Andrea, "Sapienza" University of Rome, Rome, Italy
| | - Alessandra Cassano
- Division of Medical Oncology, Catholic University of the Sacred Heart, Rome, Italy
| | - Roberto Murialdo
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Daniela Musio
- Department of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Giovanni Silvano
- Radiation Oncology Unit, San Giuseppe Moscati Hospital, Taranto, Italy
| |
Collapse
|
15
|
A review of the influence of mammographic density on breast cancer clinical and pathological phenotype. Breast Cancer Res Treat 2019; 177:251-276. [PMID: 31177342 DOI: 10.1007/s10549-019-05300-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/27/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context. METHODS With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival. RESULTS AND CONCLUSIONS There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.
Collapse
|
16
|
Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 2019; 19:48. [PMID: 30902088 PMCID: PMC6431077 DOI: 10.1186/s12911-019-0801-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/18/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate. METHODS A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis. RESULTS In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis. CONCLUSION Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.
Collapse
Affiliation(s)
- Mogana Darshini Ganggayah
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Yip Cheng Har
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, England
| | - Sarinder Kaur Dhillon
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
| |
Collapse
|
17
|
Al-Mahmood S, Sapiezynski J, Garbuzenko OB, Minko T. Metastatic and triple-negative breast cancer: challenges and treatment options. Drug Deliv Transl Res 2018; 8:1483-1507. [PMID: 29978332 PMCID: PMC6133085 DOI: 10.1007/s13346-018-0551-3] [Citation(s) in RCA: 307] [Impact Index Per Article: 51.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The major current conventional types of metastatic breast cancer (MBC) treatments include surgery, radiation, hormonal therapy, chemotherapy, or immunotherapy. Introducing biological drugs, targeted treatment and gene therapy can potentially reduce the mortality and improve the quality of life in patients with MBC. However, combination of several types of treatment is usually recommended. Triple negative breast cancer (TNBC) accounts for 10-20% of all cases of breast carcinoma and is characterized by the low expression of progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2). Consequently, convenient treatments used for MBC that target these receptors are not effective for TNBC which therefore requires special treatment approaches. This review discusses the occurrence of MBC, the prognosis and predictive biomarkers of MBC, and focuses on the novel advanced tactics for treatment of MBC and TNBC. Nanotechnology-based combinatorial approach for the suppression of EGFR by siRNA and gifitinib is described.
Collapse
Affiliation(s)
- Sumayah Al-Mahmood
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ, 08854-8020, USA
| | - Justin Sapiezynski
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ, 08854-8020, USA
| | - Olga B Garbuzenko
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ, 08854-8020, USA
| | - Tamara Minko
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ, 08854-8020, USA.
- Rutgers Cancer Institute, New Brunswick, NJ, 08903, USA.
- Environmental and Occupational Health Sciences Institute, Rutgers, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| |
Collapse
|
18
|
Survival analysis according to period and analysis of the factors influencing changes in survival in patients with recurrent breast cancer: a large-scale, single-center study. Breast Cancer 2018; 25:639-649. [PMID: 29786773 DOI: 10.1007/s12282-018-0869-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 04/29/2018] [Indexed: 01/13/2023]
Abstract
BACKGROUND We performed this study to analyze changing survival patterns regarding recurrent breast cancer in Korea during the last 16 years (1993-2008). We also sought to determine factors possibly influencing outcomes and changes over time in the duration of survival after recurrence. METHODS We retrospectively analyzed 9671 patients with breast cancer treated between January 1993 and December 2008, comparing the periods 1993-2002 and 2003-2008.We retrospectively reviewed the collected database including the age at diagnosis, clinical manifestations, pathology report, surgical methods, types of adjuvant treatment modalities, type of recurrence, and follow-up period. RESULTS There were 1944 cases (20.1%) of recurrence. Median age at the first recurrence was 49.5 years (range 21.8-92.9). Median follow-up was 28.8 months (range 0-228.0) from the time of relapse. Median survival time was 35.0 months. Survival after recurrence (SAR) significantly improved in 2003-2008 compared to that in 1993-2002. Median survival time increased from 27.6 months in the period I to 42.3 months in period II (p = 0.001). Independent prognostic factors after the first recurrence by multivariate analysis were age at diagnosis, tumor size, nodal status, tumor grade, subtype, anti-hormonal therapy, time at diagnosis, and disease-free interval. CONCLUSIONS Outcomes of breast cancer have been improving recently, and survival time after the first recurrence of breast cancer has steadily increased in recent decades. We confirmed that advances in treatments have contributed to this improvement in survival after the first recurrence.
Collapse
|
19
|
Mandal PK, Biswas S, Mandal G, Purohit S, Gupta A, Majumdar (Giri) A, Roy Chowdhury S, Bhattacharyya A. CCL2 conditionally determines CCL22-dependent Th2-accumulation during TGF-β-induced breast cancer progression. Immunobiology 2018; 223:151-161. [DOI: 10.1016/j.imbio.2017.10.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 10/14/2017] [Indexed: 12/12/2022]
|
20
|
Yip CH. Palliation and breast cancer. J Surg Oncol 2017; 115:538-543. [DOI: 10.1002/jso.24560] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 12/06/2016] [Accepted: 12/12/2016] [Indexed: 12/27/2022]
Affiliation(s)
- Cheng-Har Yip
- Department of Surgery; University of Malaya; Kuala Lumpur Malaysia
| |
Collapse
|