Publications by authors named "Meng-Yuan Mao"
Article Synopsis
- Traditional clinical indicators have limited effectiveness in predicting survival for patients with locally recurrent nasopharyngeal carcinoma due to tumor variance.
- A machine learning-based radiomic signature, developed from MRI features, was validated in a large study and showed strong prognostic ability for overall survival.
- This radiomic signature not only improved patient risk classification but also highlighted differences in immune response, suggesting potential for personalized treatment approaches.
Objective: To establish a nasopharyngeal carcinoma-specific big data platform based on electronic health records (EHRs) to provide data support for real-world study of nasopharyngeal carcinoma.
Methods: A multidisciplinary expert team was established for this project. Based on industry standards and practical feasibility, the team designed the nasopharyngeal carcinoma data element standards including 14 modules and 640 fields.
Background: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions.
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