ArticlePrecision clinical medicine2020
Making radiation therapy more effective in the era of precision medicine.
Article in Precision clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Multi-omics analyses identify EZH2 as a central driver in rhabdomyosarcoma radioresistance and highlight Tazemetostat as an effective radiosensitizer in vitro and in vivo.Cell death & disease · 2026Article
- The role of the gut microbiota in radiation enteritis: from mechanistic insights to therapeutic applications.Communications biology · 2026Review
- Wnt signaling in cancer: from biomarkers to targeted therapies and clinical translation.Molecular cancer · 2025Review
- Comparison of custom-designed oral stents in radiotherapy for head and neck malignancies.Frontiers in bioengineering and biotechnology · 2025Article
- 3D-printed bolus ensures the precise postmastectomy chest wall radiation therapy for breast cancer.Frontiers in oncology · 2022Article
- The value of the tumour-stroma ratio for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer: a case control study.BMC cancer · 2021Article
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
Cancer has become a leading cause of death and constitutes an enormous burden worldwide. Radiation is a principle treatment modality used alone or in combination with other forms of therapy, with 50%-70% of cancer patients receiving radiotherapy at some point during their illness. It has been suggested that traditional radiotherapy (daily fractions of approximately 1.8-2 Gy over several weeks) might select for radioresistant tumor cell sub-populations, which, if not sterilized, give rise to local treatment failure and distant metastases. Thus, the challenge is to develop treatment strategies and schedules to eradicate the resistant subpopulation of tumorigenic cells rather than the predominant sensitive tumor cell population. With continued technological advances including enhanced conformal treatment technology, radiation oncologists can increasingly maximize the dose to tumors while sparing adjacent normal tissues, to limit toxicity and damage to the latter. Increased dose conformality also facilitates changes in treatment schedules, such as changes in dose per treatment fraction and number of treatment fractions, to enhance the therapeutic ratio. For example, the recently developed large dose per fraction treatment schedules (hypofractionation) have shown clinical advantage over conventional treatment schedules in some tumor types. Experimental studies suggest that following large acute doses of radiation, recurrent tumors, presumably sustained by the most resistant tumor cell populations, may in fact be equally or more radiation sensitive than the primary tumor. In this review, we summarize the related advances in radiotherapy, including the increasing understanding of the molecular mechanisms of radioresistance, and the targeting of these mechanisms with potent small molecule inhibitors, which may selectively sensitize tumor cells to radiation.
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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.