A stochastic model of cancer progression and screening
- Cancer screening is critical in clinical practice and public health in order to improve patient prognosis and reduce cancer mortality. In this dissertation, we propose a new approach of assessing the potential of cancer screening, based on estimation of the characteristics of cancer progression. We developed a novel stochastic model of cancer progression and applied this model to lung cancer and breast cancer separately. Model parameters were estimated using data from the Surveillance, Epidemiology and End Results (SEER) cancer registry. The model reproduces SEER, validates against external clinical trials and produces estimates of tumor volume doubling times, likelihood of cure, and mortality reduction that are consistent with empirical data. When applied to lung cancer, the model suggests that under the current treatment regimes, only 6% of patients can be cured in the absence of screening. Despite the high mortality rates from lung cancer, we predict that the majority of lung cancer patients who develop lethal disease could be cured if their primary tumor were detected and treated while it is smaller than 1 cm. To attain a lung cancer mortality reduction of 20% or greater from annual screening, our model estimates that a screen detection threshold of 1.2 cm or lower is necessary, provided there were little to no delay between initial detection and treatment. When applied to breast cancer, our results indicate that likelihood of cure from breast cancer has been improved from 44% in 1975 to 67% in 1993, and a greater fraction of the improvement is attributed to adjuvant therapy than screening. In addition, we found a synergy between adjuvant therapy and screening, which suggests that patients receiving adjuvant therapies would benefit more from screening than those who were not treated by adjuvant therapy, even if there were no improvement in the screening technology. We found that this synergy enables a biannual mammographic screening program to provide benefits that are comparable to an annual program for women age 50 to 69 years. Our model achieves validity and generalizability across different disease types, different cancer characteristics, and different patient cohorts. In this dissertation, we demonstrate the usefulness of this model on estimation of cancer progression timeline and likelihood of cure and its ability to quantify the benefit of screening and treatment. Our approach can be used to provide useful insight for decision making in screening policy, facilitate the design of screening trials, and prioritize novel screening tests based on their potential benefits.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|2010, c2011; 2010
|Stanford University, Department of Biomedical Informatics.
|Das, Amar K. (Amar Kumar)
|Das, Amar K. (Amar Kumar)
|Statement of responsibility
|Submitted to the Department of Biomedical Informatics.
|Ph.D. Stanford University 2011
- © 2011 by Shih-jui Lin
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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