Extended Abstract
Authors: Andreea Mihai Newtson (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Rebecca K Chung (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Eric J Devor (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242 & Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Erin A Salinas (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Megan E McDonald (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Kristina W. Thiel (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Michael J. Goodheart (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242 & Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Kimberly K. Leslie (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242 & Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242) , Brian J Smith (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242 & Department of Biostatistics, University of Iowa College of Public Health, Iowa City, IA, 52242) , Jesus Gonzalez-Bosquet (Department of Obstetrics and Gynecology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242 & Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242)
One of the prognostic factors most highly associated with ovarian cancer survival is response to initial chemotherapy. Current prediction models of chemo-response built with comprehensive molecular datasets, like The Cancer Genome Atlas (TCGA), could be improved by including clinical and outcomes data designed to study response to treatment. The objective of this study was to create a prediction model of ovarian cancer chemo-response using clinical-pathological features, and to compare its performance with a similar TCGA clinical model.
Keywords: Ovarian cancer, serous epithelial ovarian cancer, chemotherapy, chemotherapy response, prediction model, clinical predictors, TCGA
How to Cite: Newtson, A. M. , Chung, R. K. , Devor, E. J. , Salinas, E. A. , McDonald, M. E. , Thiel, K. W. , Goodheart, M. J. , Leslie, K. K. , Smith, B. J. & Gonzalez-Bosquet, J. (2018) “A predictive model for serous epithelial ovarian cancer chemo-response using clinical characteristics”, Proceedings in Obstetrics and Gynecology. 8(1). doi: https://doi.org/10.17077/2154-4751.1390