Patient Reported Outcomes as Indicators of Treatment Progression
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Mentor Information
Dr. Renee Brady-Nicholls
Description
Computed tomography (CT) scans remain a common modality for measuring tumor sizes in cancer patients across the board. However, diagnostic imaging can be invasive to patients through enclosed spaces, and discomfort in sterile clinical environments. Patient Reported Outcomes (PROs) offer a less intrusive avenue for gauging cancer progression through individualized measurements regarding the standard of living and appearance of symptoms. Past studies have proven their effectiveness as indicators of treatment response in Non-Small Cell Lung Cancer (NSCLC). More specifically, certain PROs such as insomnia have shown a high correlation to changes in tumor volume over time. In this study, PROs and tumor volumes collected from 75 NSCLC patients undergoing immunotherapy were analyzed to determine how PRO dynamics could be used to inform when volumetric treatment progression would occur. We calibrated the tumor growth inhibition (TGI) model to tumor volume dynamics and determined the model’s predictive capability. We then analyzed changes in PROs to determine how they correlated with model parameters and how they could be used to improve predictive ability. The TGI model can accurately describe patient-specific volume dynamics. Fitting to initial treatment response dynamics and propagating forward shows that the model can accurately predict volume dynamics (accuracy = 71%). Parameter analysis showed that changes in insomnia correlated well with model parameters. The TGI model can accurately predict patient response and model parameters show correlation to changes in insomnia. Future work will entail integrating changes in insomnia to enhance the predictive ability of the model.
Patient Reported Outcomes as Indicators of Treatment Progression
Computed tomography (CT) scans remain a common modality for measuring tumor sizes in cancer patients across the board. However, diagnostic imaging can be invasive to patients through enclosed spaces, and discomfort in sterile clinical environments. Patient Reported Outcomes (PROs) offer a less intrusive avenue for gauging cancer progression through individualized measurements regarding the standard of living and appearance of symptoms. Past studies have proven their effectiveness as indicators of treatment response in Non-Small Cell Lung Cancer (NSCLC). More specifically, certain PROs such as insomnia have shown a high correlation to changes in tumor volume over time. In this study, PROs and tumor volumes collected from 75 NSCLC patients undergoing immunotherapy were analyzed to determine how PRO dynamics could be used to inform when volumetric treatment progression would occur. We calibrated the tumor growth inhibition (TGI) model to tumor volume dynamics and determined the model’s predictive capability. We then analyzed changes in PROs to determine how they correlated with model parameters and how they could be used to improve predictive ability. The TGI model can accurately describe patient-specific volume dynamics. Fitting to initial treatment response dynamics and propagating forward shows that the model can accurately predict volume dynamics (accuracy = 71%). Parameter analysis showed that changes in insomnia correlated well with model parameters. The TGI model can accurately predict patient response and model parameters show correlation to changes in insomnia. Future work will entail integrating changes in insomnia to enhance the predictive ability of the model.
