METHOD: This was a cross-sectional study that recruited 503 healthy males from 3 community-based clinics in Selangor, Malaysia. Genital and anal samples were collected from each participant for 14 high risk and 2 low risk HPV DNA detection and genotyping. All participants responded to a set of detailed sociodemographic and sexual behaviour questionnaire.
RESULTS: The median age at enrolment was 40 years old (IQR: 31-50). The anogenital HPV6/11 prevalence was 3.2% whereas high risk HPV prevalence was 27.1%. The genital HPV prevalence for HPV6/11 was 2.9% while high risk HPV was 18.8%. HPV6/11 prevalence in the anal canal was 1.6% and high risk HPV was 12.7%. HPV 18 was the most prevalent genotype detected in the anogenital area. There was a significant independent association between genital and anal HPV infections.
CONCLUSION: Anogenital HPV infection is common among Malaysian men. These findings emphasize the ubiquity of HPV infection and thus the value of population-wide access to HPV prevention.
METHODS: Twenty focus group discussions were conducted with 102 Asian patients with cancer from diverse sociodemographic backgrounds. Thematic analysis was performed.
RESULTS: While most participants, especially younger patients with young children, experienced intense emotional distress upon receiving a cancer diagnosis, those with a family history of cancer were relatively calm and resigned. Nonetheless, the prior negative experience with cancer in the family made affected participants with a family history less eager to seek cancer treatment and less hopeful for a cure. Although a majority viewed the presence of family members during the breaking of bad news as important, a minority opted to face it alone to lessen the emotional impact on their family members. Difficulties disclosing the news of a cancer diagnosis to loved ones also emerged as an important need. Sensitive and empathetic patient-physician communication during the breaking of news of a cancer diagnosis was stressed as paramount.
CONCLUSION: A patient-centered communication approach needs to be developed to reduce the emotional distress to patients and their families after the breaking of bad news of a cancer diagnosis. This is expected to positively affect the patients' subsequent coping skills and attitudes toward cancer, which may improve adherence to cancer therapy.
METHODS: Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).
RESULTS: The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).
CONCLUSIONS: The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.
METHODS: In this prospective cohort study, data on newly diagnosed patients with cancer were derived from the ASEAN Costs in Oncology (ACTION) cohort study, a prospective longitudinal study in 47 centres located in eight countries in southeast Asia. The ACTION study measured household expenditures on complementary medicine in the immediate year after cancer diagnosis. Participants were given cost diaries at baseline to record illness-related payments that were directly incurred and not reimbursed by insurance over the 12-month period after study recruitment. We assessed incidence of financial catastrophe (out-of-pocket cancer-related costs ≥30% of annual household income), medical impoverishment (reduction in annual household income to below poverty line following subtraction of out-of-pocket cancer-related costs), and economic hardship (inability to make necessary household payments) at 1 year.
FINDINGS: Between March, 2012, and September, 2013, 9513 participants were recruited into the ACTION cohort study, of whom 4754 (50·0%) participants were included in this analysis. Out-of-pocket expenditures on complementary medicine were reported by 1233 households. These payments constituted 8·6% of the annual total out-of-pocket health costs in lower-middle-income countries and 42·9% in upper-middle-income countries. Expenditures on complementary medicine significantly increased risks of financial catastrophe (adjusted odds ratio 1·52 [95% CI 1·23-1·88]) and medical impoverishment (1·75 [1·36-2·24]) at 12 months in upper-middle-income countries only. However, the risks were significantly higher for economically disadvantaged households, irrespective of country income group.
INTERPRETATION: Integration of evidence-supported complementary therapies into mainstream cancer care, along with interventions to address use of non-evidence-based complementary medicine, might help alleviate any associated adverse financial impacts.
FUNDING: None.
Methods: Data were derived from 20 focus group discussions that were conducted in five public and private Malaysian hospitals, which included 102 adults with breast, cervical, colorectal or prostate cancers. The discussions were segregated by type of healthcare setting and gender. Thematic analysis was performed.
Results: Five major themes related to cancer costs emerged: 1) cancer therapies and imaging services, 2) supportive care, 3) complementary therapies, 4) non-medical costs and 5) loss of household income. Narratives on out-of-pocket medical costs varied not only by type of healthcare setting, clinical factors and socioeconomic backgrounds, but also by private health insurance ownership. Non-health costs (e.g. transportation, food) and loss of income were nonetheless recurring themes. Coping mechanisms that were raised included changing of cancer treatment decisions, continuing work despite ill health and seeking financial assistance from third parties. Unmet needs in coping with financial distress were especially glaring among the women.
Conclusion: The long-term costs of cancer (medications, cancer surveillance, supportive care, complementary medicine) should not be overlooked even in settings where there is access to highly subsidised cancer care. In such settings, patients may also have unmet needs related to non-health costs of cancer and loss of income.
Method: From 2016 to 2017, 2,127 women newly-diagnosed with breast cancer were prospectively recruited. Participants' cardiovascular biomarkers were measured prior to adjuvant treatment decision-making. Clinical data and medical histories were obtained from hospital records. Adjuvant treatment decisions were collated 6-8 months after recruitment. A priori risk of cardiotoxicity was predicted using the Cardiotoxicity Risk Score.
Results: Mean age was 54 years. Eighty-five patients had pre-existing cardiac diseases and 30 had prior stroke. Baseline prevalence of hypertension was 47.8%. Close to 20% had diabetes mellitus, or were obese. Dyslipidaemia was present in 65.3%. The proportion of women presenting with ≥2 modifiable CVD risk factors at initial cancer diagnosis was substantial, irrespective of age. Significant ethnic variations were observed. Multivariable analyses showed that pre-existing CVD was consistently associated with lower administration of adjuvant breast cancer therapies (odds ratio for chemotherapy: 0.32, 95% confidence interval: 0.17-0.58). However, presence of multiple risk factors of CVD did not appear to influence adjuvant treatment decision-making. In this study, 63.6% of patients were predicted to have high risks of developing cardiotoxicities attributed to a high baseline burden of CVD risk factors and anthracycline administration.
Conclusion: While recent guidelines recommend routine assessment of cardiovascular comorbidities in cancer patients prior to initiation of anticancer therapies, this study highlights the prevailing gap in knowledge on how such data may be used to optimise cancer treatment decision-making.
METHODS: This was a cross-sectional study where 1293 healthy women aged between 18 and 60 years were recruited via convenience sampling from five community-based clinics in Selangor, Malaysia. Cervicovaginal self-samples were obtained and DNA was extracted for HPV detection and genotyping. A comprehensive questionnaire was administered to determine the sociodemographics and behavioural patterns of participants.
RESULTS: The median age at enrolment was 37 years old (IQR: 30-47). In total, 86/1190 (7.2%) of the samples collected were positive for HPV infection, with the highest HPV prevalence (11.9%) detected in the subgroup of 18-24 years old. The top three most prevalent HPV genotypes were HPV 16, 52 and 58. The independent risk factors associated with higher rates of HPV infection included Indian ethnicity, widowed status and women with partners who are away from home for long periods and/or has another sexual partner.
CONCLUSIONS: The overall prevalence of HPV infection in this Malaysian multiethnic population was 7.2%, with 6.5% being high-risk genotypes. The top three most common high-risk HPV types were HPV 16, 52 and 58. This information is important for the planning of primary (HPV vaccination) and secondary (screening) cervical cancer prevention programmes in Malaysia.
AIM: This study aimed to compare the performance of BMI, waist circumference (WC) and waist-to-height ratio (WtHR) in predicting Malaysians with excess body fat defined by dual-energy X-ray absorptiometry (DXA).
SUBJECTS AND METHODS: A total of 399 men and women aged ≥40 years were recruited from Klang Valley, Malaysia. The body composition of the subjects, including body fat percentage, was measured by DXA. The weight, height, WC and WHtR of the subjects were also determined.
RESULTS: BMI [sensitivity = 55.7%, specificity = 86.1%, area under curve (AUC) = 0.709] and WC (sensitivity = 62.7%, specificity = 90.3%, AUC = 0.765) performed moderately in predicting excess adiposity. Their performance and sensitivity improved with lower cut-off values. The performance of WHtR (sensitivity = 96.6%, specificity = 36.1, AUC = 0.664) was optimal at the standard cut-off value and no modification was required.
CONCLUSION: The performance of WC in identifying excess adiposity was greater than BMI and WHtR based on AUC values. Modification of cut-off values for BMI and WC could improve their performance and should be considered by healthcare providers in screening individuals with excess adiposity.
METHODS: After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 µg of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot.
RESULTS: A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve).
CONCLUSIONS: The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.
METHODS: In total, 2,008 Malaysian adults with no previous cancer were surveyed using a 42-item questionnaire adapted from the Awareness Measure and the Cancer Awareness Measure-Mythical Causes Scale. Partial least squares structural equation modeling was used to evaluate measurement models.
RESULTS: Despite high educational attainment, only about half of the respondents believed that 7 of the 21 listed established risk factors caused cancer. Factors associated with accurate beliefs included higher socioeconomic status (SES) and having family or friends with cancer. However, 14 of the 21 listed mythical/unproven factors were correctly believed as not cancer-causing by the majority. Women and those with lower SES were more likely to hold misconceptions. Beliefs on established risk factors were significantly associated with perceived risk of cancer. Individuals with stronger beliefs in established risk factors were less likely to be associated with healthy behaviors. Conversely, stronger beliefs in mythical or unproven factors were more likely to be associated with healthy lifestyles.
CONCLUSION: Findings highlight the importance of prioritizing cancer literacy as a key action area in national cancer control plans. The counterintuitive associations between cancer beliefs and lifestyle emphasize the complexity of this relationship, necessitating nuanced approaches to promote cancer literacy and preventive behaviors.
METHODS: Through the Association of Southeast Asian Nations Costs in Oncology study, 1490 newly diagnosed cancer patients were followed-up in Malaysia for 1 year. Health-related quality of life was assessed by using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and EuroQol-5 (EQ-5D) dimension questionnaires at baseline, 3 and 12 months. Psychological distress was assessed by using Hospital Anxiety and Depression Scale. Data were modeled by using general linear and logistic regressions analyses.
RESULTS: One year after diagnosis, the mean EORTC QLQ-C30 Global Health score of the cancer survivors remained low at 53.0 over 100 (SD 21.4). Fifty-four percent of survivors reported at least moderate levels of anxiety, while 27% had at least moderate levels of depression. Late stage at diagnosis was the strongest predictor of low HRQoL. Increasing age, being married, high-income status, hospital type, presence of comorbidities, and chemotherapy administration were also associated with worse HRQoL. The significant predictors of psychological distress were cancer stage and hospital type.
CONCLUSION: Cancer survivors in this middle-income setting have persistently impaired HRQoL and high levels of psychological distress. Development of a holistic cancer survivorship program addressing wider aspects of well-being is urgently needed in our settings.
METHODS: Through the Association of Southeast Asian Nations Costs in Oncology study, 1,294 newly diagnosed patients with cancer (Ministry of Health [MOH] hospitals [n = 577], a public university hospital [n = 642], private hospitals [n = 75]) were observed in Malaysia. Cost diaries and questionnaires were used to measure incidence of financial toxicity, encompassing financial catastrophe (FC; out-of-pocket costs ≥ 30% of annual household income), medical impoverishment (decrease in household income from above the national poverty line to below that line after subtraction of cancer-related costs), and economic hardship (inability to make necessary household payments). Predictors of financial toxicity were determined using multivariable analyses.
RESULTS: One fifth of patients had private health insurance. Incidence of FC at 1 year was 51% (MOH hospitals, 33%; public university hospital, 65%; private hospitals, 72%). Thirty-three percent of households were impoverished at 1 year. Economic hardship was reported by 47% of families. Risk of FC attributed to conventional medical care alone was 18% (MOH hospitals, 5%; public university hospital, 24%; private hospitals, 67%). Inclusion of expenditures on nonmedical goods and services inflated the risk of financial toxicity in public hospitals. Low-income status, type of hospital, and lack of health insurance were strong predictors of FC.
CONCLUSION: Patients with cancer may not be fully protected against financial hardships, even in settings with universal health coverage. Nonmedical costs also contribute as important drivers of financial toxicity in these settings.
MATERIALS AND METHODS: Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.
RESULTS: AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]).
CONCLUSION: The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.