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PURPOSEGranular data on breast cancer (BC) are pertinent for surveillance, planning, and monitoring of cancer care delivery. We determined the trends in clinical presentation, management, and survival of women with BC in a multiethnic middle-income Asian setting over 15 years.METHODSData of 7,478 Malaysian women newly diagnosed with invasive BC between 2005 and 2019 from three hospital-based cancer registries were included. Trends in demographic, tumor, and treatment characteristics were compared across period 1 (P1): 2005-2009, period 2 (P2): 2010-2014, and period 3 (P3): 2015-2019. Overall survival and net survival were determined.RESULTSMore women in P3 than P1 were older than 60 years at diagnosis. Only a marginal increase in proportion of women with stage I disease was observed (23.7% v 27.2% in P1 and P3, respectively, P = .004). Nonetheless, patients were increasingly presenting with smaller tumors, fewer axillary node involvement, well-differentiated tumors, and hormone receptor expression in recent times. Proportion of women with human epidermal growth factor receptor 2 (HER2)–overexpressed tumors significantly decreased. Among indicated patients, receipt of anticancer therapies was somewhat similar over the calendar periods, except for neoadjuvant chemotherapy and anti-HER2 therapy, where increases in administration were noted. Significant improvements in survival were observed over the 15 years, particularly for HER2-overexpressed BCs.CONCLUSIONAlthough the improvements in BC survival that we have observed validate ongoing cancer control efforts and treatment advances, study findings suggest that more could be done for earlier detection and improved access to effective therapies in our settings.

Abstract Background In the competitive health care environment, patient satisfaction and quality of life (QoL) have become the subject of interest to evaluate the efficacy of therapeutic interventions as we experience improved breast cancer survival in modern times. The knowledge of the long-term effects of surgery on the QoL in breast cancer patients is currently limited in the Asian setting. The purpose of this longitudinal study is to evaluate the QoL of early-stage breast cancer patients undergoing mastectomy and breast-conserving surgery (BCS). Methods In this prospective cohort study, the QoL of 208 patients who underwent mastectomy and the BCS treatment were assessed, using the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire. The questionnaire was administered at the baseline, 6 and 12 months following diagnosis. One-way ANCOVA was used for statistical analysis. Results A total of 208 female survivors of Stage 0–II breast cancer were included, among them 47.1% underwent BCS and 52.9% underwent mastectomy. Older (63.3%), Chinese women (63.6%), and patients with primary education (71.7%) were more likely to undergo mastectomy. At baseline, no significant differences were observed for QoL in both treatment groups. At 6 months, patients who underwent BCS had better social functioning scales( P = 0.006) and worse symptom scales for dyspnoea (P = 0.031), compared to mastectomy patients. One year after diagnosis, the role functioning score of the mastectomy group was significantly higher than the BCS group, specifically among patients who had undergone chemotherapy (P = 0.034). Conclusion Patients who underwent BCS had better social functioning and worse dyspnoea symptoms compared to patients undergoing mastectomy at six months. During one year, there were only significant improvements in the role functioning among the mastectomy groups compared to the BCS groups. After further stratification, only mastectomy patients who received chemotherapy exhibited improved role functioning compared to patients those who did not undergo chemotherapy. Providing social and physical support postoperatively and monitoring patients for cancer worry, or other symptoms in the long-term survivorship period would be important to ensure optimal QoL.

Abstract Background Shared decision‐making has been shown to improve the quality of life in metastatic breast cancer patients in high‐literacy and high‐resource settings. However, limited studies have examined the cultural preferences of metastatic breast cancer patients with shared decision‐making implementation and the barriers encountered in an Asian setting where societal norms predominate and physician decision‐making is at the forefront. This paper aims to identify (1) barriers to practising shared decision‐making faced by healthcare professionals and patients and (2) strategies for implementing shared decision‐making in the context of metastatic breast cancer management in Malaysia. Methods We conducted a qualitative study involving 12 patients diagnosed with metastatic breast cancer, 16 healthcare professionals and 5 policymakers from surgical and oncology departments at public healthcare centres in Malaysia. Semi‐structured in‐depth interviews and focus group discussions were conducted. The interviews were recorded, transcribed verbatim and analysed using the thematic approach. Nvivo software was used to manage and analyse the data. Results Five main themes emerged from the study: healthcare provider–patient communication, workforce availability, cultural and belief systems, goals of care and paternalism versus autonomy. Other strategies proposed to overcome barriers to implementing shared decision‐making were training of healthcare professionals and empowering nurses to manage patients' psychosocial issues. Conclusion This study found that practising shared decision‐making in the public health sector remains challenging when managing patients with metastatic breast cancer. The utilization of decision‐making tools, patient empowerment and healthcare provider training may help address the system and healthcare provider–patient barriers identified in this study. Patient or Public Contribution Patients were involved in the study design, recruitment and analysis.

IntroductionBreast cancer (BC) screening uptake in Malaysia is low and a high number of cases present at a late stage. Community navigation and mobile health (mHealth) may increase screening attendance, particularly by women from rural communities. This randomized controlled study evaluated an intervention that used mHealth and community health workers to educate women about BC screening and navigate them to clinical breast examination (CBE) services in the context of the COVID-19 pandemic.MethodsWomen aged 40-74 years, from Segamat, Malaysia, with a mobile phone number, who participated in the South East Asian Community Observatory health survey, (2018) were randomized to an intervention (IG) or comparison group (CG). The IG received a multi-component mHealth intervention, i.e. information about BC was provided through a website, and telephone calls and text messages from community health workers (CHWs) were used to raise BC awareness and navigate women to CBE services. The CG received no intervention other than the usual option to access opportunistic screening. Regression analyses were conducted to investigate between-group differences over time in uptake of screening and variable influences on CBE screening participation.ResultsWe recruited 483 women in total; 122/225 from the IG and 144/258 from the CG completed the baseline and follow-up survey. Uptake of CBE by the IG was 45.8% (103/225) whilst 3.5% (5/144) of women from the CG who completed the follow-up survey reported that they attended a CBE during the study period (adjusted OR 37.21, 95% CI 14.13; 98.00, pConclusionThe results suggested that the bespoke multicomponent mHealth intervention may be used to address the significant public health problem of low uptake of BC screening in rural Malaysia.

BackgroundCancer screening provision in resource-constrained settings tends to be opportunistic, and uptake tends to be low, leading to delayed presentation and treatment and poor survival. ObjectiveThe aim of this study was to identify, review, map, and summarize findings from different types of literature reviews on the use of mobile health (mHealth) technologies to improve the uptake of cancer screening. MethodsThe review methodology was guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Ovid MEDLINE, PyscINFO, and Embase were searched from inception to May 2021. The eligible criteria included reviews that focused on studies of interventions that used mobile phone devices to promote and deliver cancer screening and described the effectiveness or implementation of mHealth intervention outcomes. Key data fields such as study aims, types of cancer, mHealth formats, and outcomes were extracted, and the data were analyzed to address the objective of the review. ResultsOur initial search identified 1981 titles, of which 12 (0.61%) reviews met the inclusion criteria (systematic reviews: n=6, 50%; scoping reviews: n=4, 33%; rapid reviews: n=1, 8%; narrative reviews: n=1, 8%). Most (57/67, 85%) of the interventions targeted breast and cervical cancer awareness and screening uptake. The most commonly used mHealth technologies for increasing cancer screening uptake were SMS text messages and telephone calls. Overall, mHealth interventions increased knowledge about screening and had high acceptance among participants. The likelihood of achieving improved uptake-related outcomes increased when interventions used >1 mode of communication (telephone reminders, physical invitation letters, and educational pamphlets) together with mHealth. ConclusionsmHealth interventions increase cancer screening uptake, although multiple modes used in combination seem to be more effective.

PURPOSEThe purpose of this study was to describe follow-up care for breast and colorectal cancer survivors in countries with varying levels of resources and highlight challenges regarding posttreatment survivorship care.METHODSWe surveyed one key stakeholder from each of 27 countries with expertise in survivorship care on questions including the components/structure of follow-up care, delivery of treatment summaries and survivorship care plans, and involvement of primary care in survivorship. Descriptive analyses were performed to characterize results across countries and variations between the WHO income categories (low, middle, high). We also performed a qualitative content analysis of narratives related to survivorship care challenges to identify major themes.RESULTSSeven low- or /lower-middle-income countries (LIC/LMIC), seven upper-middle-income countries (UMIC), and 13 high-income countries (HICs) were included in this study. Results indicate that 44.4% of countries with a National Cancer Control Plan currently address survivorship care. Additional findings indicate that HICs use guidelines more often than those in LICs/LMICs and UMICs. There was great variation among countries regardless of income level. Common challenges include issues with workforce, communication and care coordination, distance/transportation issues, psychosocial support, and lack of focus on follow-up care.CONCLUSIONThis information can guide researchers, providers, and policy makers in efforts to improve the quality of survivorship care on a national and global basis. As the number of cancer survivors increases globally, countries will need to prioritize their long-term needs. Future efforts should focus on efforts to bridge oncology and primary care, building international partnerships, and implementation of guidelines.

Regular physical activity (PA) after a breast cancer diagnosis is associated with reduced mortality and better quality of life. In this prospective cohort study, we aimed to explore the trends of PA among breast cancer survivors over three years and identify factors associated with low PA. Interviews on 133 breast cancer patients were conducted at baseline, one and three years after the diagnosis of breast cancer at University Malaya Medical Centre in Kuala Lumpur. Physical activity was measured by using the Global Physical Activity Questionnaire. PA was categorised as active (≥ 600 MET-min/week) and inactive (

BACKGROUND: Breast cancer (BC) is the most common cancer in Malaysia, with many diagnosed at late stages. The “Know Your Lemons” (KYL) visual educational tools were developed by KYL Foundation. This study aimed to evaluate participants' confidence levels and perceived knowledge in identifying BC symptoms before and after exposure to KYL tools. MATERIALS AND METHODS: A cross-sectional study was carried out among 788 participants in three KYL health campaigns from 2017 to 2020. Perceived knowledge (a 5-item Likert scale was used, zero means “very poor” and 4 means “excellent knowledge”) and confidence in identifying BC symptoms were studied. A Wilcoxon Matched-Paired Signed-Rank Test was performed to assess the perceived knowledge. RESULTS: There was a significant improvement in the perceived knowledge Mean (±SD) score (2.84 ± 1.02) versus (4.31 ± 0.66) before and after the campaign (P < 0.01). About 95.6% agreed that the language used in KYL materials was clear and understandable, 89.8% agreed it is acceptable in Malaysian culture, and 80% felt more confident in identifying BC symptoms. Therefore, 90.8% had the intention of breast self-examination and 90.8% would consult a doctor if symptomatic. The majority (92.7%) agreed that the KYL tools clarified the BC tests needed. CONCLUSION: The KYL tools enhanced perceived BC symptom recognition knowledge and confidence levels.

Background: Needs assessment tools may be used to guide the optimisation of cancer survivorship services. We sought to develop and validate a dual-language needs assessment tool for women with breast cancer. Methods: The study comprised two phases; (I) co-design, and (II) psychometric testing. In Phase I, items were generated based on focus group discussions with patients and a literature review. These items were then translated into the Malay language. Content and face validation were undertaken with an expert panel. In phase II, 315 Malaysian women living with breast cancer were recruited. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to determine construct validity and reliability. Criterion validity was assessed using the EORTC QLQ-C30 questionnaire. Results: We co-designed and validated a 48-item dual-language needs assessment tool for breast cancer (NeAT-BC). Five underlying constructs were revealed in the EFA: (1) emotional support, (2) social and intimate relationships, (3) hospital appointments, (4) personal care and health, and (5) information and services. The NeAT-BC demonstrated good reliability across all constructs (Cronbach’s alpha: 0.90 to 0.96). CFA also demonstrated acceptable convergent and divergent validity, composite reliability ≥ 0.87, and Heterotrait–Monotrait index < 0.85 for all constructs. Criterion validity was established given the significant negative correlation between overall needs and quality of life (r = −0.14; p = 0.02). The NeAT-BC took approximately 25 min to be completed and could be interviewer-administered or self-administered. Conclusion: The utilization of the NeAT-BC is expected to guide establishment of evidence-based cancer survivorship services in Malaysia, with wider potentials for adoption in other multi-ethnic and/or low-and-middle income settings.

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.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, partly due to the lack of targeted therapy available. Cancer cells heavily reprogram their metabolism and acquire metabolic plasticity to satisfy the high-energy demand due to uncontrolled proliferation. Accumulating evidence shows that deregulated lipid metabolism affects cancer cell survival, and therefore we sought to understand the function of fatty acid binding protein 7 (FABP7), which is expressed predominantly in TNBC tissues. As FABP7 was not detected in the TNBC cell lines tested, Hs578T and MDA-MB-231 cells were transduced with lentiviral particles containing either FABP7 open reading frame or red fluorescent protein. During serum starvation, when lipids were significantly reduced, FABP7 decreased the viability of Hs578T, but not of MDA-MB-231, cells. FABP7-overexpressing Hs578T (Hs-FABP7) cells failed to efficiently utilize other available bioenergetic substrates such as glucose to sustain ATP production, which led to S/G2 phase arrest and cell death. We further showed that this metabolic phenotype was mediated by PPAR-α signaling, despite the lack of fatty acids in culture media, as Hs-FABP7 cells attempted to survive. This study provides imperative evidence of metabolic vulnerabilities driven by FABP7 via PPAR-α signaling.

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.

Abstract Background Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future. Results Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act. Conclusion The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.

Pathology reports represent a primary source of information for cancer registries. University Malaya Medical Centre (UMMC) is a tertiary hospital responsible for training pathologists; thus narrative reporting becomes important. However, the unstructured free-text reports made the information extraction process tedious for clinical audits and data analysis-related research. This study aims to develop an automated natural language processing (NLP) algorithm to summarize the existing narrative breast pathology report from UMMC to a narrower structured synoptic pathology report with a checklist-style report template to ease the creation of pathology reports. The development of the rule-based NLP algorithm was based on the R programming language by using 593 pathology specimens from 174 patients provided by the Department of Pathology, UMMC. The pathologist provides specific keywords for data elements to define the semantic rules of the NLP. The system was evaluated by calculating the precision, recall, and F1-score. The proposed NLP algorithm achieved a micro-F1 score of 99.50% and a macro-F1 score of 98.97% on 178 specimens with 25 data elements. This achievement correlated to clinicians’ needs, which could improve communication between pathologists and clinicians. The study presented here is significant, as structured data is easily minable and could generate important insights.

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

BACKGROUND:Breast cancer patients often experience a high level of distress. Psychological distress is a broad construct encompass both depression and anxiety. Previous studies in examining which of these psychological symptoms (either anxiety or depression) were more significantly associated with the distress level in breast cancer patients is lacking. This study aims to compare the level of depression and anxiety between patients with different level of distress. The correlation between the changes in distress level with depression or anxiety over 12 months was also examined. METHODS:This study is from the MyBCC cohort study. Two hundred and twenty one female breast cancer patients were included into the study. They were assessed at the time of diagnosis, 6 months and 12 month using Hospital Anxiety and Depression Scale (HADS) and distress thermometer. The information on age, ethnicity, treatment types and staging of cancer were collected. RESULTS:50.2%, 51.6% and 40.3% of patients had perceived high level of distress at baseline, 6 months and 1 year after diagnosis. Those with high perceived level of distress had significant higher anxiety scores even after adjusted for the underlying depressive scores (Adjusted OR at baseline = 1.28, 95% CI = 1.13-1.44; adjusted OR at 6 months = 1.27, 95% CI = 1.11-1.45; adjusted OR at 12 months = 1.51, 95% CI = 1.29-1.76). There were no significant differences in the depressive scores between the subjects with either low or high distress level. There was reduction in perceived level of distress, anxiety and depression scores at 12 months after the diagnosis. The decrease of distress was positively correlated with the reduction of anxiety scores but not the changes of depressive scores (r' = 0.25). CONCLUSION:Anxiety is a more significant psychological state that contributed to the feeling of distress in breast cancer as compared with depression. Levels of anxiety at diagnosis in this study would justify screening for anxiety, early identification and therapy for maintaining the psychological well-being of breast cancer patients. Further studies will be needed to measure the effectiveness of therapeutic interventions.

Direct tumour extension into the internal jugular veins (IJV) and right atrium in thyroid cancer is extremely uncommon. We report three cases of advanced thyroid cancer invading into the IJV and right atrium. All three patients had well-differentiated thyroid cancer and presented with typical clinical picture of the superior cava syndrome coupled with significant compressive symptoms. Two patients had obvious tumour thrombus extending to the superior vena cava (SVC) and right atrium, whilst in one patient, the thrombus extended to the SVC close to the edge of the right atrium. In all three patients, the intraluminal tumour thrombus was clinically palpable. Involvement of the IJV, SVC and right atrium was confirmed with computed tomography (CT) scan and echocardiography. The decision to extract the thrombus transcervically was made on the basis of the positive “ring sign”, which is a thin rim of contrast surrounding the tumour thrombus documented on CT. All three patients underwent total thyroidectomy with segmental resection and ligation of the IJV. The tumour thrombus was successfully extracted transcervically. The histopathology report confirmed follicular carcinoma in all three patients. Two patients had radioiodine ablation therapy postoperatively, and were well 18 months after operation. One patient who had lung metastases documented on chest CT succumbed to the disease due to massive haemoptysis 3 weeks after operation.

BackgroundComplementary and alternative medicine (CAM) use has become increasingly popular among patients with cancer. The purposes of this study were to compare the QOL in CAM users and non-CAM users and to determine whether CAM use influences QOL among breast cancer patients during chemotherapy.MethodologyA cross-sectional survey was conducted at two outpatient chemotherapy centers. A total of 546 patients completed the questionnaires on CAM use. QOL was evaluated based on the European Organization for Research and Treatment of Cancer (EORTC) core quality of life (QLQ-C30) and breast cancer-specific quality of life (QLQ-BR23) questionnaires.ResultsA total of 70.7% of patients were identified as CAM users. There was no significant difference in global health status scores and in all five subscales of the QLQ C30 functional scales between CAM users and non-CAM users. On the QLQ-C30 symptom scales, CAM users (44.96±3.89) had significantly (p = 0.01) higher mean scores for financial difficulties than non-CAM users (36.29±4.81). On the QLQ-BR23 functional scales, CAM users reported significantly higher mean scores for sexual enjoyment (6.01±12.84 vs. 4.64±12.76, p = 0.04) than non-CAM users. On the QLQ-BR23 symptom scales, CAM users reported higher systemic therapy side effects (41.34±2.01 vs. 37.22±2.48, p = 0.04) and breast symptoms (15.76±2.13 vs. 11.08±2.62, p = 0.02) than non-CAM users. Multivariate logistic regression analysis indicated that the use of CAM modality was not significantly associated with higher global health status scores (p = 0.71).ConclusionWhile the findings indicated that there was no significant difference between users and non-users of CAM in terms of QOL, CAM may be used by health professionals as a surrogate to monitor patients with higher systemic therapy side effects and breast symptoms. Furthermore, given that CAM users reported higher financial burdens (which may have contributed to increased distress), patients should be encouraged to discuss the potential benefits and/or disadvantages of using CAM with their healthcare providers.

BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

INTRODUCTION: Lymph node ratio (LNR, i.e. the ratio of the number of positive nodes to the total number of nodes excised) is reported to be superior to the absolute number of nodes involved (pN stage) in classifying patients at high versus low risk of death following breast cancer. The added prognostic value of LNR over pN in addition to other prognostic factors has never been assessed. METHODS: All patients diagnosed with lymph node positive, non-metastatic invasive breast cancer at the National University Hospital (Singapore) and University of Malaya Medical Center (Kuala Lumpur) between 1990-2007 were included (n = 1589). Overall survival of the patients was estimated by the Kaplan Meier method for LNR [categorized as low (>0 and 0.65-1)] and pN staging [pN1, pN2 and pN3]. Adjusted overall relative mortality risks associated with LNR and pN were calculated by Cox regression. The added prognostic value of LNR over pN was evaluated by comparing the discriminating capacity (as indicated by the c statistic) of two multivariate models, one including pN and one including LNR. RESULTS: LNR was superior to pN in categorizing mortality risks for women ≥60 years, those with ER negative or grade 3 tumors. In combination with other factors (i.e. age, treatment, grade, tumor size and receptor status), substituting pN by LNR did not result in better discrimination of women at high versus low risk of death, neither for the entire cohort (c statistic 0.72 [0.70-0.75] and 0.73 [0.71-0.76] respectively for pN versus LNR), nor for the subgroups mentioned above. CONCLUSION: In combination with other prognosticators, substitution of pN by LNR did not provide any added prognostic value for South East Asian breast cancer patients.

BACKGROUND: The burden of breast cancer in Asia is escalating. We evaluated the impact of ethnicity on survival after breast cancer in the multi-ethnic region of South East Asia. METHODOLOGY/PRINCIPAL FINDINGS: Using the Singapore-Malaysia hospital-based breast cancer registry, we analyzed the association between ethnicity and mortality following breast cancer in 5,264 patients diagnosed between 1990 and 2007 (Chinese: 71.6%, Malay: 18.4%, Indian: 10.0%). We compared survival rates between ethnic groups and calculated adjusted hazard ratios (HR) to estimate the independent effect of ethnicity on survival. Malays (n = 968) presented at a significantly younger age, with larger tumors, and at later stages than the Chinese and Indians. Malays were also more likely to have axillary lymph node metastasis at similar tumor sizes and to have hormone receptor negative and poorly differentiated tumors. Five year overall survival was highest in the Chinese women (75.8%; 95%CI: 74.4%-77.3%) followed by Indians (68.0%; 95%CI: 63.8%-72.2%), and Malays (58.5%; 95%CI: 55.2%-61.7%). Compared to the Chinese, Malay ethnicity was associated with significantly higher risk of all-cause mortality (HR: 1.34; 95%CI: 1.19-1.51), independent of age, stage, tumor characteristics and treatment. Indian ethnicity was not significantly associated with risk of mortality after breast cancer compared to the Chinese (HR: 1.14; 95%CI: 0.98-1.34). CONCLUSION: In South East Asia, Malay ethnicity is independently associated with poorer survival after breast cancer. Research into underlying reasons, potentially including variations in tumor biology, psychosocial factors, treatment responsiveness and lifestyle after diagnosis, is warranted.