ArticleScientific reports2019
Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Quantitative CT analysis in lung cancer research: a bibliometric analysis based on Web of Science [2005-2025].Journal of thoracic disease · 2026Article
- Model-Driven Optimization of Subcutaneous Polymer Prodrugs Achieves Cancer Remission in Mice.Advanced healthcare materials · 2026Article
- Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.Clinical & experimental metastasis · 2024Article
- Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics.Frontiers in immunology · 2024Article
- Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach.PLoS computational biology · 2024Article
- Circulating tumour cells for early detection of clinically relevant cancer.Nature reviews. Clinical oncology · 2023Review
- Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma.PLoS computational biology · 2022Article
- Current challenges in metastasis research and future innovation for clinical translation.Clinical & experimental metastasis · 2022Article
- How mathematical modeling could contribute to the quantification of metastatic tumor burden under therapy: insights in immunotherapeutic treatment of non-small cell lung cancer.Theoretical biology & medical modelling · 2021Article
- Prognosis of patients treated in a single neurosurgical reference centre for brain metastasis caused by dormant disseminated cells.Oncology letters · 2021Article
- The Case for Biocalculus: Improving Student Understanding of the Utility Value of Mathematics to Biology and Affect toward Mathematics.CBE life sciences education · 2021Article
- Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors.PLoS computational biology · 2020Article
- Metastasis Initiation Precedes Detection of Primary Cancer-Analysis of Metastasis GrowthFrontiers in physiology · 2020Article
- Therapeutic effect of whole brain radiotherapy on advanced NSCLC between EGFR TKI-naïve and TKI-resistant.Radiation oncology (London, England) · 2019Article
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Authors and funding
9 authors.
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Abstract
Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC. Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were further assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4-5.7 months and onset of BMs 14-19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.
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