Background: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms. Methods: Patients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors. Results: The areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P<0.05). In decision curve, most of the models also performed better than US. The gradient boosting decision tree model with 7 variables was identified as the best model because of its best performance in both ROC (AUC=0.731) and decision curves. Based on multivariate analysis and feature selection, young age, male sex, low serum thyroid peroxidase antibody and US features such as suspected lymph nodes, microcalcification and tumor size > 1.1 cm were the most contributing predictors for CLNM. Conclusions: It is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.

BACKGROUND The outbreak of coronavirus disease 2019 (COVID-19) happened in early December and it has affected China in more ways than one. The societal response to the pandemic restricted medical students to their homes. Although students cannot learn about COVID-19 through clinical practice, they can still pay attention to news of COVID-19 through various channels. Although, as suggested by previous studies, some medical students have already volunteered to serve during the COVID-19 pandemic, the overall willingness of Chinese medical students to volunteer for such has not been systematically examined. AIM To study Chinese medical students’ interest in the relevant knowledge on COVID-19 and what roles they want to play in the pandemic. METHODS Medical students at Peking Union Medical College were surveyed via a web-based questionnaire to obtain data on the extent of interest in the relevant knowledge on COVID-19, attitude towards volunteerism in the pandemic, and career preference. Logistic regression modeling was used to investigate possible factors that could encourage volunteerism among this group in a pandemic. RESULTS A total of 552 medical students responded. Most medical students showed a huge interest in COVID-19. The extent of students’ interest in COVID-19 varied among different student-classes ( P < 0.05). Senior students had higher scores than the other two classes. The number of people who were ‘glad to volunteer’ in COVID-19 represented 85.6% of the respondents. What these students expressed willingness to undertake involved direct, indirect, and administrative job activities. Logistic regression analysis identified two factors that negatively influenced volunteering in the pandemic: Student-class and hazards of the voluntary job. Factors that positively influenced volunteering were time to watch COVID-19 news, predictable impact on China, and moral responsibility. CONCLUSION More innovative methods can be explored to increase Chinese medical students’ interest in reading about the relevant knowledge on COVID-19 and doing voluntary jobs during the pandemic.

Background: Three-dimensional (3D) photography plays an important role in surgical planning and postoperative evaluation. Commercial 3D facial scanners are expensive, and they require patients to come to the clinics for 3D photography. To solve this problem, we developed an iPad/iPhone application to enable patients to capture 3D images of themselves on their own. This study aimed to evaluate the validity and reproducibility of this novel imaging system. Methods: 3D images were taken on 20 volunteers using the novel imaging system. Twenty-one anthropometric parameters were measured using calipers (direct measurement) and 3D photographs (3D photogrammetry). The results were compared to assess the accuracy and bias of 3D photogrammetry. The reproducibility was evaluated by testing intra-and interobserver reliabilities. Furthermore, 3D virtual models obtained by the novel imaging system and Vectra H1 camera were compared by performing heat map analysis. Results: The 3D photogrammetric results showed excellent correlations with direct measurements. Most anthropometric parameters did not show statistically significant differences between the two methods. The 95% limits of agreement exceeded 2 mm in some parameters, especially those with large numbers, although their relative error measurements were very small. Intra-and interobserver reliabilities were high enough to ensure good reproducibility. The comparison of 3D models obtained by the novel imaging system and Vectra H1 camera showed that the mean distance and the mean RMS were 0.08 and 0.67 mm, respectively. Conclusions: The novel 3D facial scanning system is validated to enable patients to take 3D images on their own. The imaging quality of the subnasale region needs further improvement. Future clinical applications include surgical planning, postoperative evaluation, and early diagnosis of diseases that affect facial appearance.