RESUMO
In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volumes (GTVs). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM) were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). The Radiomic model, clinical factor model, and combined radiomic clinical probability-weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949, respectively. The combined radiomics clinical probability-weighted enhanced model achieved significantly better performance than the radiomic model in 1-year survival prediction (chi-square test, p < 0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.
RESUMO
Studies on work-related musculoskeletal symptoms (WRMSs) have been conducted mainly on different types of workforce but not many on low-skilled workers. The purpose of this study was to evaluate the effectiveness of a multidisciplinary exercise program in decreasing the number of body parts with WRMSs for low-skilled workers. This study used a repeated-measures, single-group design. One hundred and five (105) workers participated in eight weekly 90-min sessions (including 45-min workshops and 45-min exercises) in low-income community settings. The exercise program involved a 21-movement stretching exercise and a 10-movement muscle-strengthening exercise. Questionnaire and health-assessment data were collected at the baseline (N = 105) and immediately after the 8-week program (n = 86). The average age of the 105 participants was 50.5 ± 8.7 years (ranging from 31 to 67). Over 80% (n = 87) of them were female, 68.6% (n = 72) were married, and 68.6% (n = 72) had completed secondary school. They reported an average of three body parts with WRMSs at baseline (T0). By the end of the eight weeks (T1), the participants had reduced the number of WRMS-affected body parts, job stress, and incidences of working through pain, and had improved spine flexibility and handgrip strength. The factors significantly affecting the reduction in the number of body parts with WRMSs were change in the workstyle of working through pain, and self-rated health status. Our study has demonstrated that a community-based multidisciplinary program can reduce the number of body parts affected by WRMSs in low-skilled workers in low-income communities.
