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

• Published in: Clinical cancer research 2023-08, Vol.29 (16), p.3142-3150
• Main Authors: Bhatt, Ambika S., Schabath, Matthew B., Hoogland, Aasha I., Jim, Heather S.L., Brady-Nicholls, Renee
• Format: Article
• Language: English
• Subjects: Translational Cancer Mechanisms and Therapy
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1078-0432.CCR-23-0396

PURPOSEMinimally 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 DESIGNIn 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. RESULTSChanges 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. CONCLUSIONSThis 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.