A Bayesian Network Model of Head and Neck Squamous Cell Carcinoma Incorporating Gene Expression Profiles
Radiation therapy allows precision targeting of certain groups of lymph nodes and is a treatment for metastatic head and neck squamous cell carcinoma.In current practice,there is approximately 15%probability that physicians inadvertently treat healthy tissue or leave the cancerous lymph nodes untreated.The aim of this work is to improve the accuracy of medical decision-making by extending existing predictive models to capture the probabilities of finding cancerous lymph nodes at each of the six image-based surgical neck level using a patients genetic profile,primary tumor site and tumor size.Our model was trained with publicly available data extracted from the Cancer Genome Atlas(TCGA)and validated against the TCGA dataset both with and without genetic information.Results show that genetic profile data improves model accuracy.These findings suggest that our predicitive model may improve the accuracy of clinical decision-making,especially for patients with more advanced metastasis.However,more data is needed to ensure significance of the proposed effects,as well as to improve accuracy of the overall model.
Carcinoma,Squamous Cell Clinical Decision-Making Lymph Nodes
Shuyang Wu Anthony Law Mark E.Whipple
Department of Biomedical Informatics and Medical Education,University of Washington,Seattle,WA,USA Department of Otolaryngology-Head and Neck Surgery,University of Washington,Seattle,WA,USA Department of Biomedical Informatics and Medical Education,University of Washington,Seattle,WA,USA;D
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
634-638
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）