ReviewTranslational oncology2018
Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer.
Review in Translational oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Introducing carbon quantum dot-Capivasertib drug carrier complex for enhanced treatment of breast cancer.PloS one · 2025Article
- DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research.Computational and structural biotechnology journal · 2024Article
- Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism.Molecular cancer therapeutics · 2024Article
- Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.AIMS public health · 2023Review
- Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules.PLoS computational biology · 2022Article
- Polymer-Based Nanosystems-A Versatile Delivery Approach.Materials (Basel, Switzerland) · 2021Review
- Chemotherapeutic drugs: Cell death- and resistance-related signaling pathways. Are they really as smart as the tumor cells?Translational oncology · 2021Review
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model.Veterinary and comparative oncology · 2021Article
- The efficacy of chemotherapeutic drug combinations may be predicted by concordance of gene response to the single agents.Oncology letters · 2020Article
- Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.Physical biology · 2020Article
- How and why to build a mathematical model: A case study using prion aggregation.The Journal of biological chemistry · 2020Review
- Article
- Upregulated Circular RNA circ-UBE2D2 Predicts Poor Prognosis and Promotes Breast Cancer Progression by Sponging miR-1236 and miR-1287.Translational oncology · 2019Article
- Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer.Scientific reports · 2019Article
- Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect.PLoS biology · 2019Article
- Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.Frontiers in physiology · 2019Article
- A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.
Identifiers
What OpenQuestion holds
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.