Trial reportCPT: pharmacometrics & systems pharmacology2020
A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.
Trial report in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.CPT: pharmacometrics & systems pharmacology · 2024Trial
- Predicting median survival times from early data in immunotherapy trials using exponential-decay modeling.Journal of the National Cancer Institute · 2026Article
- Novel Drug-Disease Modeling Framework for Oncology Benefit-Risk Evaluation: Application to Tusamitamab Ravtansine.CPT: pharmacometrics & systems pharmacology · 2026Article
- A novel Bayesian generative approach for estimating tumor dynamics from published studies.CPT: pharmacometrics & systems pharmacology · 2024Article
- Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique.BMC medical research methodology · 2024Article
- Model-informed drug development of autologous CAR-T cell therapy: Strategies to optimize CAR-T cell exposure leveraging cell kinetic/dynamic modeling.CPT: pharmacometrics & systems pharmacology · 2023Review
- Model-based prediction of progression-free survival for combination therapies in oncology.CPT: pharmacometrics & systems pharmacology · 2023Article
- Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma.NPJ systems biology and applications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.
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Registered trials
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.