ReviewFrontiers in immunology2023
Incorporating lesion-to-lesion heterogeneity into early oncology decision making.
Review in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- What are RECIST 1.1 progressions made of? Variability in double-read oncology trials.European radiology · 2026Article
- Evaluating the tumor response assessment of modified-RECIST 1.1 in advance non-small cell lung cancer: aTranslational lung cancer research · 2025Article
- An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.CPT: pharmacometrics & systems pharmacology · 2025Article
- Dissection of Progressive Disease Patterns for a Modified Classification for Immunotherapy.JAMA oncology · 2025Article
- Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model.CPT: pharmacometrics & systems pharmacology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.
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Registered trials
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