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Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

Enakshi D. Sunassee, Dean Tan, Tianlin Ji, Renee Brady, Eduardo G. Moros, Jimmy J. Caudell, Slav Yartsev, Heiko Enderling
doi: https://doi.org/10.1101/469957
Enakshi D. Sunassee
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Dean Tan
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Tianlin Ji
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Renee Brady
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Eduardo G. Moros
2Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
3Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Jimmy J. Caudell
2Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Slav Yartsev
4London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada.
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Heiko Enderling
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
2Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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  • For correspondence: Heiko.Enderling@moffitt.org
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Abstract

Purpose Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of nonidentifiability and clinically unrealistic results.

Materials and Methods We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients for predicting response to subsequent radiation doses.

Results Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index=0.89).

Conclusion The PSI model may be suited to forecast treatment response for individual patients and offer actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 14, 2018.
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Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses
Enakshi D. Sunassee, Dean Tan, Tianlin Ji, Renee Brady, Eduardo G. Moros, Jimmy J. Caudell, Slav Yartsev, Heiko Enderling
bioRxiv 469957; doi: https://doi.org/10.1101/469957
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Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses
Enakshi D. Sunassee, Dean Tan, Tianlin Ji, Renee Brady, Eduardo G. Moros, Jimmy J. Caudell, Slav Yartsev, Heiko Enderling
bioRxiv 469957; doi: https://doi.org/10.1101/469957

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