Background: Peripheral blood-derived inflammation-based scores such as the neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) have recently been proposed as prognostic markers in solid tumours. Although evidence to support these markers as unfavourable prognostic factors is more compelling in gastrointestinal cancers, very little is known of their impact on breast cancer. We investigated the association between the NLR and PLR, and overall survival after breast cancer. Methods: Data from the University of Malaya Medical Centre Breast Cancer Registry was used. Of 2059 consecutive patients diagnosed from 2000 to 2008, we included 1435 patients with an available pre-treatment differential blood count (∼70%). Patients were stratified into quintiles of the NLR/PLR. Multivariable Cox regression was used to determine the independent prognostic significances of the NLR/PLR. Results: Compared with the first quintile of the NLR, women in quintile 5 were younger, had bigger tumours, nodal involvement, distant metastases and higher tumour grades. Higher NLR quintiles were significantly associated with poorer survival with a 5-year relative survival ratio (RSR) of 76.4% (95% CI: 69.6–82.1%) in quintile 1, 79.4% (95% CI: 74.4–83.7%) in quintile 2, 72.1% (95% CI: 66.3–77.3%) in quintile 3, 65.6% (95% CI: 59.8–70.8%) in quintile 4 and 51.1% (95% CI: 43.3–58.5%) in quintile 5. Following adjustment for demography, tumour characteristics, treatment and the PLR, the adjusted hazard ratio (HR) for quintile 5 vs quintile 1 was 1.50 (95% CI: 1.08–1.63); P_trend=0.004. Results were unchanged when the NLR was analysed as a dichotomous variable using different cutoff points. Although patients in PLR quintile 5 had lower survival than in quintile 1 (5-year RSR: 53.2% (95% CI: 46.9–59.2%) vs 77.0% (95% CI: 70.9–82.2%)), this association was not significant after multivariable adjustment. However, a PLR >185 was significantly associated with poorer survival; adjusted HR: 1.25 (95% CI: 1.04–1.52). Conclusions: Both the NLR and PLR are independently associated with an increased risk of mortality in breast cancer. Their added value in the prognostication of breast cancer in clinical practice warrants investigation.

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. 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. 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. 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.

Background: Cancer 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. Objective: The 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. Methods: The 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. Results: Our 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. Conclusions: mHealth interventions increase cancer screening uptake, although multiple modes used in combination seem to be more effective.

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.

The inclusion of prayer-for-health (PFH) in the definition of complementary alternative medicine (CAM) has resulted in higher levels of CAM use. The objective of this study was to assess PFH and CAM use among breast cancer patients undergoing chemotherapy. A cross-sectional study was performed at two chemotherapy providers. Patients were questioned about use of three categories of CAM, mind-body practices (MBPs), natural products (NPs) and traditional medicine (TM). PFH was also examined separately from CAM to better characterise the patterns of CAM and PFH used during chemotherapy. A total of 546 eligible patients participated in the study; 70.7% (n = 386) reported using some form of CAM, and 29.3% (n = 160) were non-CAM users. When PFH was excluded as a CAM, fewer patients reported the use of CAM (66.1%; n = 361). The total number of patients who used MBPs decreased from 342 to 183. The most common CAM use category was NPs (82.8%), followed by MBPs (50.7%), and TM (35.7%). CAM users were more likely to have a tertiary education (OR 2.11, 95% CI 1.15–3.89 vs. primary/lower), have household incomes > RM 3,000 (≈944 USD) per month (OR 2.32, 95% CI 1.40–3.84 vs. ≤RM 3,000 (≈944 USD)), and have advanced cancer (OR 1.75, 95% CI 1.18–2.59 vs. early stage cancer), compared with non-CAM users. The CAM users were less likely to have their chemotherapy on schedule (OR 0.24, 95% CI 0.10–0.58 vs. chemotherapy postponed) than non-CAM users. Most MBPs were perceived to be more helpful by their users, compared with the users of NPs and TM. CAM use was prevalent among breast cancer patients. Excluding PFH from the definition of CAM reduced the prevalence of overall CAM use. Overall, CAM use was associated with higher education levels and household incomes, advanced cancer and lower chemotherapy schedule compliance. Many patients perceived MBP to be beneficial for improving overall well-being during chemotherapy. These findings, while preliminary, clearly indicate the differences in CAM use when PFH is included in, and excluded from, the definition of CAM.

Introduction: Germline TP53 mutations cause an increased risk to early-onset breast cancer in Li-Fraumeni syndrome (LFS) families and the majority of carriers identified through breast cancer cohorts have LFS or Li-Fraumeni-like (LFL) features. However, in Asia and in many low resource settings, it is challenging to obtain accurate family history and we, therefore, sought to determine whether the presence of early-onset breast cancer is an appropriate selection criteria for germline TP53 testing. Methods: A total of 100 patients with early-onset breast cancer (≤ 35 years) treated at University Malaya Medical Centre between 2003 and 2009, were analyzed for germline mutations in BRCA1, BRCA2 and TP53 by full DNA sequencing. Of the mutations identified, we examined their likely pathogenicity on the basis of prevalence in a case-control cohort, co-segregation analyses and loss of heterozygosity (LOH) in tumor tissues. Results: We identified 11 BRCA1 (11%) and 6 BRCA2 (6%) germline carriers among early-onset breast cancer patients. Of the 83 BRCA-negative patients, we identified four exonic variants and three intronic variants in TP53. Of these, two exonic variants are clinically relevant (E346X and p. G334_R335dup6) and two novel missense mutations (A138V and E285K) are likely to be clinically relevant, on the basis of co-segregation and loss of heterozygosity (LOH). Notably, E285K was found in two unrelated individuals and haplotype analyses suggest a founder effect. Two of the three intronic variants are likely benign based on their prevalence in a control population. Clinically relevant TP53 germline mutations were identified in three of the four patients (75%) with a family history of at least two LFS-linked cancers (breast, bone or soft tissue sarcoma, brain tumors or adrenocortical cancer); 1 of the 17 patients (6%) with a family history of breast cancer only, and 1 of the 62 patients (< 2%) with no family history of breast or LFS-linked cancers. Conclusions: Our study reports germline BRCA1, BRCA2 and TP53 mutations are found in early-onset breast cancer patients at 11%, 6% and 5% respectively, suggesting that TP53 mutation screening should be considered for these patients. However, we find that even in low resource Asian settings where family history is poorly reported, germline TP53 mutations are found predominantly among breast cancer patients with a family history of LFS-linked cancers.