Factors contributing to lymph node occult metastasis in supraglottic laryngeal carcinoma cT2-T4 N0M0 and metastasis predictive equation

Objective： To investigate factors that contribute to lymph node metastasis（LNM） from clinical cT2-T4 N0M0（cN0） supraglottic laryngeal carcinoma（SLC）, and to predict the risk of occult metastasis before surgery.Methods： A total of 121 patients who received surgery were retrospectively analyzed. Relevant factors regarding cervical LNM were analyzed. Multivariate analyses were conducted to predict the region where the metastasis occurred and prognosis. Results： The overall metastatic rate of c N0 SLC was 28.1%. Metastatic rates were 15.4%, 32.5% and 35.7% for T2, T3 and T4, respectively. Metastatic rates for SLC levels II, III and IV were 19.6%, 17.2% and 3.6%, respectively. A regression equation was formulated to predict the probability of metastasis in cN0 SLC as follows： Pn=e（–3.874＋0.749T3＋1.154T4＋1.935P1＋1.750P2）/[1＋e（–3.874＋0.749T3＋1.154T4＋1.935P1＋1.750P2）]. Approximately 0.2% of patients experienced LNM with no recurrence of laryngeal cancer. Comparison of the intergroup survival curves between patients with and without LNM indicated a statistically significant difference（P=0.029）.Conclusions： Cervical lymph node metastatic rates tended to increase in tandem with T stage in patients with LNM in cN0 SLC, and neck dissection is advised for these patients. Moreover, cervical LNM in cN0 SLC showed a sequential pattern and may be predicted.

Circulating lymphangiogenic growth factors in gastrointestinal solid tumors, could they be of any clinical significance?

Metastasis is the principal cause of cancer mortality, with the lymphatic system being the first route of tumor dissemination. The glycoproteins VEGF-C and VEGF-D are members of the vascular endothelial growth factor (VEGF) family, whose role has been recently recognized as lymphatic system regulators during embryogenesis and in pathological processes such as inflammation, lymphatic system disorders and malignant tumor metastasis. They are ligands for the VEGFR-3 receptor on the membrane of the lymphatic endothelial cell, resulting in dilatation of existing lymphatic vessels as well as in vegetation of new ones (lymphangiogenesis). Their determination is feasible in the circulating blood by immunoabsorption and in the tissue specimen by immunohistochemistry and reverse transcription polymerase chain reaction (RT-PCR). Experimental and clinicopathological studies have linked the VEGF-C, VEGF-D/VEGFR3 axis to lymphatic spread as well as to the clinical outcome in several human solid tumors. The majority of these data are derived from surgical specimens and malignant cell series, rendering their clinical application questionable, due to subjectivity factors and post-treatment quantification. In an effort to overcome these drawbacks, an alternative method of immunodetection of the circulating levels of these molecules has been used in studies on gastric, esophageal and colorectal cancer. Their results denotethat quantification of VEGF-C and VEGF-D in blood samples could serve as lymph node metastasis predictive biomarkers and contribute to preoperative staging of gastrointestinal malignancies.

Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer

With the support by the National Natural Science Foundation of China,the research team jointly led by Porf.Liu Zaiyi(刘再毅)at Guangdong General Hospital and Prof.Tian Jie(田捷)at the Key Laboratory of Molecular Imaging,Chinese Academy of Sciences,developed a CT-based radiomics prediction model to preoperatively predict the lymph node metastasis in colorectal cancer(CRC),which was published

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.