Stereological Evaluation of Laryngeal Cancers Using Computed Tomography Via the Cavalieri Method: Correlation Between Tumor Volume and Number of Neck Lymph Node Metastases

Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer

Background

Cervical lymph node metastasis (LNM) is a significant factor that leads to a poor prognosis in laryngeal cancer. Early-stage supraglottic laryngeal cancer (SGLC) is prone to LNM. However, research on risk factors for predicting cervical LNM in early-stage SGLC is limited. This study seeks to create and validate a predictive model through the application of machine learning (ML) algorithms.

Methods

The training set and internal validation set data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data from 78 early-stage SGLC patients were collected from Fujian Provincial Hospital for independent external validation. We identified four variables associated with cervical LNM and developed six ML models based on these variables to predict LNM in early-stage SGLC patients.

Results

In the two cohorts, 167 (47.44%) and 26 (33.33%) patients experienced LNM, respectively. Age, T stage, grade, and tumor size were identified as independent predictors of LNM. All six ML models performed well, and in both internal and independent external validations, the eXtreme Gradient Boosting (XGB) model outperformed the other models, with AUC values of 0.87 and 0.80, respectively. The decision curve analysis demonstrated that the ML models have excellent clinical applicability.

Conclusions

Our study indicates that combining ML algorithms with clinical data can effectively predict LNM in patients diagnosed with early-stage SGLC. This is the first study to apply ML models in predicting LNM in early-stage SGLC patients.

Antitumor effect of sFlt-1 gene therapy system mediated by Bifidobacterium Infantis on Lewis lung cancer in mice

Soluble fms-like tyrosine kinase receptor (sFlt-1) is a soluble form of extramembrane part of vascular endothelial growth factor receptor-1 (VEGFR-1) that has antitumor effects. Bifidobacterium Infantis is a kind of non-pathogenic and anaerobic bacteria that may have specific targeting property of hypoxic environment inside of solid tumors. The aim of this study was to construct Bifidobacterium Infantis-mediated sFlt-1 gene transferring system and investigate its antitumor effect on Lewis lung cancer (LLC) in mice. Our results demonstrated that the Bifidobacterium Infantis-mediated sFlt-1 gene transferring system was constructed successfully and the system could express sFlt-1 at the levels of gene and protein. This system could not only significantly inhibit growth of human umbilical vein endothelial cells induced by VEGF in vitro, but also inhibit the tumor growth and prolong survival time of LLC C57BL/6 mice safely. These data suggest that Bifidobacterium Infantis-mediated sFlt-1 gene transferring system presents a promising therapeutic approach for the treatment of cancer.