Associations between diagnostic patterns and stages in ovarian cancer

Issue title: Machine Learning in Applied Statistics

Guest editors: Jong-Min Kim

Article type: Research Article

Authors: Bogie, Kath^a | Xu, Yifan^b | Ma, Junheng^b | Zhang, Adah^{c; d} | Wang, Yuanyuan^e | Zanotti, Kristine^f | Sun, Jiayang^{b; *}

Affiliations: [a] Department of Orthopedics, Case Western Reserve University, Cleveland, OH, USA | [b] Center for Statistical Research, Computing and Collaboration (SR2c), Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland OH, USA | [c] Sandia National Laboratories, Albuquerque, NM, USA | [d] Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA | [e] Department of Biological Sciences, State University of New York at Binghamton, Binghamton, NY, USA | [f] Department of OB/GYN-Gynecological Oncology, University Hospitals, Cleveland, OH, USA

Correspondence: [*] Corresponding author: Jiayang Sun, Center for Statistical Research, Computing and Collaboration (SR2c), Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland OH 44106, USA. E-mail: [email protected].

Abstract: Ovarian cancer (OvCa) is the fifth leading cause of cancer deaths in women and remains the deadliest gynecological cancer. Our study goal is to examine associations between diagnostic patterns and OvCa stages. We used the data from a web-based survey in which more than 500 women diagnosed with OvCa provided both free text responses and staging information. We employed text mining and natural language processing (NPL) to extract information on clinical diagnostic characteristics, together with 21 dichotomous symptomatic variables, patient-centered advocacy, and polytomous disease severity, with internal validation. We conducted multivariate analyses and developed tree-based classification models with the confirmation of Random Forest to determine important factors in the relationships of the clinical diagnostic characteristics with OvCa stages. Models including the symptoms, patient advocacy tendency, disease severity and doctors’ responses as predictors, had a much better predictive power than those limited to doctors’ responses alone, indicating that OvCa stage at diagnosis depends on more than just doctors’ responses. Although effective early stage diagnosis and treatment remains a challenge, our analysis of patient-centered clinical diagnostic characteristics and symptoms shows that self-advocacy is essential for all women. The frontline physician is critically important in ensuring effective follow-up and timely treatment before diagnosis.

Keywords: Ovarian cancer, diagnosis, survey, follow-up, multivariate analysis, text mining, data mining, tree-based classification, random forest, structural and non-structural missing data, patient advocacy, symptoms, doctor’s responses

DOI: 10.3233/MAS-170402

Journal: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 275-285, 2017