Background Previous research has demonstrated a correlation between hand grip strength (HGS) and muscle strength. This study aims to determine the relationship between HGS and muscle mass in older Asian adults. Methods We retrospectively reviewed the dual-energy X-ray absorptiometry (DXA) records of 907 older adults (239 (26.4%) men and 668 (73.6%) women) at one medical institution in Taipei, Taiwan, from January 2019, to December 2020. Average age was 74.80 ± 9.43 and 72.93 ± 9.09 for the males and females respectively. The inclusion criteria were: 1) aged 60 and older, 2) underwent a full-body DXA scan, and 3) performed hand grip measurements. Patients with duplicate results, incomplete records, stroke history, and other neurological diseases were excluded. Regional skeletal muscle mass was measured using DXA. HGS was measured using a Jamar handheld dynamometer. Results Total lean muscle mass (kg) averaged 43.63 ± 5.81 and 33.16 ± 4.32 for the males and females respectively. Average HGS (kg) was 28.81 ± 9.87 and 19.19 ± 6.17 for the males and females respectively. In both sexes, HGS and regional muscle mass consistently declined after 60 years of age. The rates of decline per decade in upper and lower extremity muscle mass and HGS were 7.06, 4.95, and 12.30%, respectively, for the males, and 3.36, 4.44, and 12.48%, respectively, for the females. In men, HGS significantly correlated with upper (r = 0.576, p < 0.001) and lower extremity muscle mass (r = 0.532, p < 0.001). In women, the correlations between HGS and upper extremity muscle mass (r = 0.262, p < 0.001) and lower extremity muscle mass (r = 0.364, p < 0.001) were less strong, though also statistically significant. Conclusion Muscle mass and HGS decline with advancing age in both sexes, though the correlation is stronger in men. HGS measurements are an accurate proxy for muscle mass in older Asian adults, particularly in males.
Purpose: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). Methods: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Results: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76–0.91), an AUC of 0.93 (95% CI, 0.87–0.95), a specificity of 0.75 (95% CI, 0.67–0.83), and a sensitivity of 0.91 (95% CI, 0.76–0.94). Conclusion: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.
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