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Background: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. Methods: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical‐radiological semantic features; (2) The radiomics models (radiomics+ and radiomics−) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite−) incorporated both above. Results: In the testing set, the radiomics+/− models and the composite+/− models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/− models and the composite+/− models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/− and composite+/− models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics−, composite+ model vs. composite−) (p > 0.05). Conclusions: The present study established a machine learning‐based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability. [ABSTRACT FROM AUTHOR]

Loving‐kindness and compassion meditation (LKCM) have been well applied among employees to improve their health and well‐being. Existing studies on LKCM have also provided supportive evidence of its benefits and effectiveness under organizational contexts. The current meta‐analytical study aimed to systematically summarise the effects of LKCM in the workplace and to outline directions for future research and practice. Among 327 empirical studies on LKCM published until March 2022, 21 trials focussed on employees and provided sufficient information, which were included in the following meta‐analysis. The results showed that LKCM benefited eight categories of workplace outcomes. Specifically, LKCM effectively decreased employees' burnout (g = 0.395, k = 10) and stress (g = 0.544, k = 10) and facilitated their mindfulness (g = 0.558, k = 14), self‐compassion (g = 0.646, k = 12), personal mental health (g = 0.308, k = 13), job attitudes (g = 0.283, k = 4), interpersonal relationships (g = 0.381, k = 12), and psychological resources (g = 0.406, k = 6). The results of moderation analyses further indicated that the participants' job type, gender, and the focus of LKCM might differentially fluctuate the magnitude of LKCM effects. To advance research and best practice, we finally pointed out several issues that deserve attention, such as long‐term effects, underlying mechanisms, potential moderators, and outcomes or influential factors at the organizational level. [ABSTRACT FROM AUTHOR]