Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.

Background: The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC.

Methods: A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes.

assessment of the role of DpC in the treatment of head and neck squamous cell carcinoma.

The present study aimed to investigate the antitumor efficacy of di-2-pyridylketone-4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC) and di-2-pyridylketone-4,4,-dimethyl-3-thiosemicarbazone (Dp44mT) on head and neck squamous cell carcinoma (HNSCC) cells. The proliferation and apoptosis of HNSCC cells treated with the iron chelators DpC and Dp44mT were detected. The mechanism of DpC-induced apoptosis on HNSCC cells was investigated.