ReviewOncotarget2018
Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models.
Review in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Medical digital twins: enabling precision medicine and medical artificial intelligence.The Lancet. Digital health · 2025Review
- Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025Article
- Mathematical Modeling Support for Lung Cancer Therapy-A Short Review.International journal of molecular sciences · 2023Review
- LSD1-targeted therapy-a multi-purpose key to unlock immunotherapy in small cell lung cancer.Translational lung cancer research · 2023Article
- A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.Biophysical journal · 2022Article
- Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer.eLife · 2021Article
- Comparison of Primary and Secondary Prophylaxis Using PEGylated Recombinant Human Granulocyte-Stimulating Factor as a Cost-Effective Measure in Malignant Neoplasms: A Multicenter Retrospective Study.Frontiers in pharmacology · 2021Article
- Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition.Biophysical journal · 2020Article
- Cost-Effectiveness of Tucatinib in Human Epidermal Growth Factor Receptor 2-Positive Metastatic Breast Cancer From the US and Chinese Perspectives.Frontiers in oncology · 2020Article
- Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution.Current opinion in systems biology · 2019Article
- Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology.Journal of clinical medicine · 2019Article
- Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment.Processes (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.
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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.