Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract.

Problem: Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions.

Aim: Our hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models.

The racial disparity of nasopharyngeal carcinoma based on the database analysis.

Objective: To investigate whether the racial/ethnical disparity of nasopharyngeal carcinoma exists among the four major ethical groups in the United States named Asians, Caucasians, African Americans and Hispanics between the years of 1973 to 2013 using the Surveillance, Epidemiology, and End Result (SEER) database.

Methods: The National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) database from 1973 to 2013 was utilized in this study to calculate survival trends for the four main ethical groups in the United States. The cases of nasopharyngeal carcinoma were extracted based on the SEER code cs0204schema.