Mathematical models to predict clinically relevant events in cancer patients

9 Jul 2018, 18:00
Holme Building/--The Refectory (University of Sydney)

Holme Building/--The Refectory

University of Sydney

Board: 258
Poster Presentation Disease - non-infectious Poster Session


Dr Jeremy Mason (University of Southern California)


Cancer is one of the leading causes of morbidity and mortality worldwide. Many of the disease related events, such as metastatic progression, treatment resistance, and overall survival, generate much uncertainty and are oftentimes viewed as random. Applying current modelling techniques and methodologies to existing data can produce a forecasting framework that can be leveraged to predict these significant events that impact clinical decision making. We used a retrospective, longitudinal dataset of 3,505 patients with primary bladder cancer to build forecasting models that could be used prospectively in newly diagnosed patients. We built Markov models from individual patient progression pathways and used these models to simulate and predict future locations of metastatic spread. Additionally, we used machine learning techniques to temporally predict disease progression and overall survival. Analyzing the results of these models revealed that patterns of metastatic spread emerged in distinct subgroups of patients when stratified by gender and also by pathologic stage. Additionally, analysis of patient variables showed higher associations with both recurrence and survival for pathologic staging (post-operative) as compared to clinical staging (pre-operative). Incorporating additional longitudinal data such as treatment information and genomic data could lend to the predictions of therapy resistance and side effect development.

Primary author

Dr Jeremy Mason (University of Southern California)


Dr Zaki Hasnain (University of Southern California) Mr Gus Miranda (University of Southern California) Mr Karanvir Gill (University of Southern California) Dr Paul Newton (University of Southern California) Dr Inderbir Gill (University of Southern California) Dr Peter Kuhn (University of Southern California)

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