Patient-Reported Outcomes as Interradiographic Predictors of Response in Non–Small Cell Lung Cancer

Author:

Bhatt Ambika S.12ORCID,Schabath Matthew B.3ORCID,Hoogland Aasha I.4ORCID,Jim Heather S.L.4ORCID,Brady-Nicholls Renee2ORCID

Affiliation:

1. 1Department of Biostatistics, Gillings School of Global Public Health at the University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

2. 2Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.

3. 3Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.

4. 4Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.

Abstract

Abstract Purpose: Minimally invasive biomarkers have been used as important indicators of treatment response and progression in cancers such as prostate and ovarian. Unfortunately, all biomarkers are not prognostic in all cancer types and are often not routinely collected. Patient-reported outcomes (PRO) provide a non-obtrusive, personalized measure of a patient's quality of life and symptomatology, reported directly from the patient, and are increasingly collected as part of routine care. Previous literature has shown correlations between specific PROs (i.e., insomnia, fatigue) and overall survival. Although promising, these studies often only consider single time points and ignore patient-specific dynamic changes in individual PROs, which might be early predictors of treatment response or progression. Experimental Design: In this study, PRO dynamics were analyzed to determine if they could be used as interradiographic predictors of tumor volume changes among 85 patients with non–small cell lung cancer undergoing immunotherapy. PRO questionnaires and tumor volume scans were completed biweekly and monthly, respectively. Correlation and predictive analysis were conducted to identify specific PROs that could accurately predict patient response. Results: Changes in tumor volume over time were significantly correlated with dizziness (P < 0.005), insomnia (P < 0.05), and fatigue (P < 0.05). In addition, cumulative changes in insomnia could predict progressive disease with a 77% accuracy, on average 45 days prior to the next imaging scan. Conclusions: This study presents the first time that patient-specific PRO dynamics have been considered to predict how individual patients will respond to treatment. This is an important first step in adapting treatment to improve response rates.

Funder

National Cancer Institute

State of Florida Bankhead Coley Research Program

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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