Objective To explore the predictive value of CT signs combined with clinicopathological features for single cN0 papillary thyroid microcarcinoma (PTMC) central lymph node metastasis (CLNM). Methods A retrospective analysis of the CT signs and clinicopathological characteristics of 115 cases of single cN0 PTMC confirmed by surgery and pathology was performed, and univariate and multivariate logistic regression analysis were used to analyze the relationship between the contact between tumor and thyroid edge, tumor calcification, tumor location, tumor diameter, age, gender, thyroglobulin level and CLNM. According to the different contact range between tumor and thyroid edge in CT signs, the patients were divided into three groups: <1/4 group, 1/4–<1/2 group and ≥1/2 group. The proportion of CLNM positive patients in different contact areas between tumor body and thyroid edge was analyzed by using χ2 test. Results Among 115 cases of single cN0 PTMC, there were 26 cases and 89 cases with CLNM positive and negative, respectively. Univariate analysis showed that contact between tumor body and thyroid edge, tumor diameter, age, and gender were associated with CLNM positive (P<0.05). Further multivariate logistic regression analysis showed that thyroid marginal contact, age <45 years old and male were associated with CLNM positive (P<0.05). The proportion of CLNM positive patients in different contact areas between tumor body and thyroid edge (between the three groups ) was statistically different (P<0.05). The pairwise comparison among the three groups showed that the proportion of CLNM positive patients were statistically different (P<0.0167 after correction). Conclusions Tumor body contact with thyroid edge, age <45 years and male were independent risk factors for CLNM in patients with single cN0 PTMC. The combination of multiple risk factors can further improve the preoperative evaluation level of CLNM in patients with PTMC. Excluding clinical characteristic factors, the wider the contact area between the tumor and the thyroid edge, the higher the risk of CLNM, which provides a reasonable basis for selective central lymph node dissection.
Adverse drug reaction (ADR) signal detection serves as a core component of pharmacovigilance, with its methodological framework continuously enriched by advancements in data science and artificial intelligence. This article systematically reviews and elaborates on the principles, implementation pathways, and application scenarios of mainstream ADR signal detection methods, including traditional frequentist methods such as the proportional reporting ratio (PRR), reporting odds ratio (ROR), and the comprehensive standard method; Bayesian probabilistic models such as the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma-Poisson Shrinker (MGPS); as well as machine learning and data mining techniques such as association rules, random forest, and zero-inflated models. By systematically comparing the advantages, limitations, applicable conditions, and empirical studies of these methods in the monitoring of both traditional Chinese medicine and chemical drugs, this study aims to provide researchers and regulatory agencies with a comprehensive methodological reference framework to facilitate the selection, optimization, and integrated application of detection approaches. Moving forward, improving data quality and promoting multi-technology collaboration will be crucial directions for enhancing the sensitivity and specificity of ADR signal detection, thereby supporting more precise decision-making in clinical medication safety.
