ReviewNature reviews. Clinical oncology2014
Rapid learning for precision oncology.
Review in Nature reviews. Clinical oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text-mining clinically relevant cancer biomarkers for curation into the CIViC database.Genome medicine · 2019Pooled it
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.Cancer research communications · 2023Article
- Prioritizing Measures That Matter Within a Person-Centered Oncology Learning Health System.JNCI cancer spectrum · 2022Article
- Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider.Journal of personalized medicine · 2021Review
- Mitigating Burnout in an Oncological Unit: A Scoping Review.Frontiers in public health · 2021Article
- Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study.Frontiers in medicine · 2021Article
- Learning health care systems: Highly needed but challenging.Learning health systems · 2020Article
- Effect of Public Deliberation on Patient Attitudes Regarding Consent and Data Use in a Learning Health Care System for Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2019Article
- Late translational research: putting forward a new model for developing new anti-cancer treatments that addresses the needs of patients and society.Molecular oncology · 2019Review
- Artificial Intelligence and Personalized Medicine.Cancer treatment and research · 2019Article
- Prototyping a precision oncology 3.0 rapid learning platform.BMC bioinformatics · 2018Article
- Randomized clinical trials and personalized medicine: A commentary on deaton and cartwright.Social science & medicine (1982) · 2018Article
- Heuristic value-based framework for lung cancer decision-making.Oncotarget · 2018Article
- Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes.Nature communications · 2018Article
- Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis.Oncotarget · 2018Article
- Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures.PloS one · 2018Article
- Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity.Seminars in radiation oncology · 2017Review
- High performance of targeted next generation sequencing on variance detection in clinical tumor specimens in comparison with current conventional methods.Journal of experimental & clinical cancer research : CR · 2017Article
- Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based. Implementation of Rapid Learning Precision Oncology requires overcoming a host of challenges that include developing analytical tools, capturing the information from each patient encounter and rapidly extrapolating it to other patients, coordinating many patient encounters to efficiently search for effective treatments, and overcoming economic, social and structural impediments, such as obtaining access to, and reimbursement for, investigational drugs.
Indexed as
Identifiers
24445514What 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.