Background: We compared the diagnostic performance of the short five-item and full seven-item Mini Sarcopenia Risk Assessment Questionnaire (MSRA-5 and MSRA-7) against the Strength, Assistance walking, Rise from a chair, Climb stairs, and Falls (SARC-F) and SARC-F with calf circumference (SARC-CalF) scales for sarcopenia in healthy community-dwelling older adults. Methods: We conducted a post-hoc cross-sectional secondary data analysis of a prospective cohort study, using data from 230 older adults (mean age 67.2±7.4 years, 92% Chinese, and 73% female) from the “Longitudinal Assessment of Biomarkers for characterization of early Sarcopenia and Osteosarcopenic Obesity in predicting frailty and functional decline in community-dwelling Asian older adults Study” (GeriLABS-2) conducted between December 2017 and March 2019 in Singapore. We performed receiver operating characteristic curve analysis to ascertain the area under the curve (AUC) for sarcopenia diagnosis using the Asian Working Group for Sarcopenia 2019 consensus criteria. We applied the Delong method to compare the AUCs of the four instruments. Results: The MSRA-5 and MSRA-7 demonstrated poor diagnostic performance (AUC of 0.511, 95% confidence interval [CI] 0.433–0.589 and AUC of 0.526, 95% CI 0.445–0.606, respectively), compared to that in SARC-CalF (AUC of 0.739, 95% CI 0.671–0.808) and SARC-F (AUC of 0.564, 95% CI 0.591–0.636). The SARC-CalF demonstrated significantly superior discriminatory ability compared to that in the SARC-F, MSRA-5, and MSRA-7 (all p<0.01). The MSRA-5 demonstrated lower sensitivity (0.464) and specificity (0.597) than in the SARC-CalF (0.661 and 0.738, respectively), whereas the MSRA-7 had higher specificity (0.887) and lower sensitivity (0.145). Conclusions The poor diagnostic performances of the MSRA-5 and MSRA-7 in our study suggest limitations of self-reported questionnaires for assessing general and dietary risk factors for sarcopenia in healthy and culturally diverse community-dwelling older adults. Studies in different populations are needed to ascertain the utility of the MSRA for the community detection of sarcopenia.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Background: Attitudes towards smoking, lung cancer screening, and perceived risk of lung cancer have not been widely studied in Malaysia. The primary objective of this study was to describe the factors affecting the willingness of high-risk current smokers and ex-smokers to undergo low-dose computed tomography (LDCT) screening for lung cancer. Methods: A prospective, cross-sectional questionnaire study was conducted in current smokers or ex-smokers aged between 55 and 80 years at three hospitals in Kota Kinabalu, Sabah, Malaysia. The questionnaire recorded the following parameters: perceived lung cancer risk; Prostate Lung Colon Ovarian Cancer 2012 risk prediction model excluding race and ethnicity predictor (PLCOm2012norace); demographic characteristics; psychosocial characteristics; and attitudes towards lung cancer and lung cancer screening. Results: A vast majority of the 95 respondents (94.7%) indicated their willingness to undergo screening. Stigma of lung cancer, low levels of knowledge about lung cancer symptoms, concerns about financial constraints, and a preference for traditional medication were still prevalent among the respondents, and they may represent potential barriers to lung cancer screening uptake. A desire to have an early diagnosis (odds ratio [OR], 11.33; 95% confidence interval [CI], 1.53 to 84.05; p=0.02), perceived time constraints (OR, 3.94; 95% CI, 1.32 to 11.73; p=0.01), and proximity of LDCT screening facilities (OR, 14.33; 95% CI, 1.84 to 111.4; p=0.01) had significantly higher odds of willingness to undergo screening. Conclusion Although high-risk current smokers and ex-smokers are likely to undergo screening for lung cancer, several psychosocial barriers persist. The results of this study may guide the policymakers and clinicians regarding the need to improve lung cancer awareness in our population.