ArticleCancers2023
Predicting Patient-Specific Tumor Dynamics: How Many Measurements Are Necessary?
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Multidimensional tumor heterogeneity and its role in therapeutic resistance.Frontiers in immunology · 2026Review
- Growth kinetics of high-grade serous ovarian cancer: implications for early detection.British journal of cancer · 2025Article
- Evolutionary unpredictability in cancer model systems.Scientific reports · 2025Article
- Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model.NPJ systems biology and applications · 2025Article
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth.Bulletin of mathematical biology · 2024Article
- Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues.NPJ systems biology and applications · 2024Article
- Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling.Bulletin of mathematical biology · 2024Article
- Minimally sufficient experimental design using identifiability analysis.NPJ systems biology and applications · 2024Article
- A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data.PloS one · 2024Article
- Guiding model-driven combination dose selection using multi-objective synergy optimization.CPT: pharmacometrics & systems pharmacology · 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
Acquiring sufficient data is imperative to accurately predict tumor growth dynamics and effectively treat patients. The aim of this study was to investigate the number of volume measurements necessary to predict breast tumor growth dynamics using the logistic growth model. The model was calibrated to tumor volume data from 18 untreated breast cancer patients using a varying number of measurements interpolated at clinically relevant timepoints with different levels of noise (0-20%). Error-to-model parameters and the data were compared to determine the sufficient number of measurements needed to accurately determine growth dynamics. We found that without noise, three tumor volume measurements are necessary and sufficient to estimate patient-specific model parameters. More measurements were required as the level of noise increased. Estimating the tumor growth dynamics was shown to depend on the tumor growth rate, clinical noise level, and acceptable error of the to-be-determined parameters. Understanding the relationship between these factors provides a metric by which clinicians can determine when sufficient data have been collected to confidently predict patient-specific tumor growth dynamics and recommend appropriate treatment options.
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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.