Evaluation of unimodal and multimodal information in health communication on GGO-related lung cancer screening: An eye-tracking study

Aims: To find out a better propaganda and education plan for the popularization of the ground-glass opacities-related (GGO-related) lung cancer screening.Methods and Material: The control group took a lung cancer screening knowledge test directly before receiving the health education. By contrast, the experimental group took the same knowledge test after receiving health education. This study designed unimodal and multimodal materials about GGO-related lung cancer. The text and graph were considered unimodal information, while the video was multimodal information. According to the different information forms they were exposed to, the experimental group was further divided into text, graphic, and video groups. An eye-tracking system was performed to record eye-tracking data synchronously.Results: Compared with the control group, the knowledge test scores of each experimental group were remarkably improved. Furthermore, the graphic group had a significantly higher correct rate on the No. 7 question, while the video group had the lowest. In terms of saccades, the video group had significantly higher speed and amplitude of saccades than the other two groups. In terms of fixation, the interval duration, total duration of whole fixations, and a number of whole fixations of the graphic group were significantly lower than those of the other two groups, while the video group had the highest values for these variables.Conclusions: It was on the unimodal information, such as text and graphics, that people can spend less time and cost to achieve effective acquisition of GGO-related lung cancer screening knowledge

Machine learning‐based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

Abstract 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). 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