ArticleClinical cancer research : an official journal of the American Association for Cancer Research2023
Patient-Reported Outcomes as Interradiographic Predictors of Response in Non-Small Cell Lung Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A Model-Based Meta-Analysis of Pembrolizumab Effects on Patient-Reported Quality of Life: Advancing Patient-Centered Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Longitudinal patient-reported outcomes and quality-of-life trajectories over 2 years in CAR-T therapy recipients.Blood neoplasia · 2026Trial
- Tutorial on Analyzing Patient-Reported Outcome Measures in Clinical Development.Clinical and translational science · 2026Review
- Predicting lung cancer stage at diagnosis based on self-reported symptoms and background factors using machine learning models.Scientific reports · 2026Article
- Article
- Patient-Reported Outcomes With First-Line Immunotherapy-Based Combination Treatment for Advanced Lung Cancer: A Prospective, Multicenter, Observational Study.Thoracic cancer · 2025Observational
- Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.JCO clinical cancer informatics · 2025Article
- Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model.NPJ systems biology and applications · 2025Article
- Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Quality-of-life scale machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer.Frontiers in immunology · 2025Article
- Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration.Cancers · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.