Purpose Lymph node metastasis (LNM) is a vital prognosis factor in patients with papillary thyroid microcarcinoma (PTMC). The study tried to identify clinicopathological factors for LNM of PTMC. Methods The clinicopathological data of 1031 patients with PTMC were extracted and analyzed. Univariate and multivariate analyses were used to identify risk factors associated with cervical lymph node metastasis. ROC analysis was used to determine the ideal critical points of the sum of the maximum diameter of multifocal in a unilateral lobe. Results The probability of LNM, central lymph node metastasis (CLNM) and lateral lymph node metastasis(LLNM)of PTMC patients were 35.6, 33.7 and 5.6%, respectively. In addition, 1.9% PTMC had LLNM only. Male, age ≤ 40 years, tumor largest diameter ≥ 5 mm, multifocal, non-uniform echoic distribution, the sum of the maximum diameter of multifocal in a unilateral lobe ≥ 8.5 mm, tumors in the lower pole location were prone to CLNM. Ultrasound mix-echo, the sum of the maximum diameter of the multifocal ≥ 10.75 mm, tumors in the upper pole location were extremely prone to LLNM. T3 were prone to LLNM or skip LLNM. Conclusions According to the clinicopathological characteristics of PTMC, the cervical lymph nodes should be correctly evaluated to guide the surgical treatment.

No abstract

Urban region profiling can benefit urban analytics. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still three limitations: (1) Most related methods focused merely on global-level inter-region relations while overlooking local-level geographical contextual signals and intra-region information; (2) Most previous works failed to develop an effective yet integrated fusion module which can deeply fuse multi-graph correlations; (3) State-of-the-art methods do not perform well in regions with high variance socioeconomic attributes. To address these challenges, we propose a multi-graph representative learning framework, called Re-gion2Vec, for urban region profiling. Specifically, except that human mobility is encoded for inter-region relations, geographic neighborhood is introduced for capturing geographical contextual information while POI side information is adopted for representing intra-region information by knowledge graph. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. To consider the discriminative properties of multiple graphs, an encoder-decoder multi-graph fusion module is further proposed to jointly learn comprehensive representations. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines. Particularly, Region2Vec has better performance than previous studies in regions with high variance socioeconomic attributes.