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Building Statistical Models for the Prediction of Oral Cancer Recurrence

Tuesday, August 26, 2014 - 11:00
Yumian Hu, UBC Statistics MSc Student
Seminar
Room 4192, Earth Sciences Building (2207 Main Mall)

Oral cancer is a disease resulting from abnormal cell growth in the mouth, lips, tongue or throat, with high morbidity rate and high recurrence incidence.


Recently, Fluorescence Visualization (FV) has shown its value in identifying cancer tissues during surgeries, as well as facilitating early detection of cancer recurrence.

In a recent research project at BC Cancer Agency, oral cancer patients were recruited to study the means of predicting future recurrence. All the patients recruited in this program received surgery guided by the FV device and had follow-up visits regularly for many years. The lesion length and width under FV (FV measurement), as well as many other clinical risk factors were recorded at surgery and in follow-up visits.
 
In this project, we aim to build appropriate statistical models to link the risk factors and the cancer-free survival time of the patients after surgery. A Cox proportional hazards model is employed to fit the recurrence data against a selected subset of risk factors. Due to the existence of time-dependent risk factors (FV measurement), it is not appropriate to use the Cox model to predict future recurrence. Instead, we fitted logistic regression models between the 5-year cancer-free survival and three possible sets of constructed risk factors.  The prediction performance of the logistic models were assessed through a cross-validation study and based on the criteria of sensitivity, specificity and AUC.

Our data analysis reveals that the FV measurement is significantly associated with cancer recurrence based on the Cox regression model. It indicates that the FV measurement is informative about the cancer recurrence. The logistic regression analysis shows that it is possible to find an appropriate set of risk factors to give a prediction of the 5-year cancer free survival. The sensitivity, specificity and AUC of the fitted model are 0.449, 0.944, and 0.861, respectively.