ReviewFrontiers in oncology2016
Big Data Analytics for Prostate Radiotherapy.
Review in Frontiers in oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Networks Models of Actin Dynamics during Spermatozoa Postejaculatory Life: A Comparison among Human-Made and Text Mining-Based Models.BioMed research international · 2016Pooled it
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- Radio-lncRNAs: Biological Function and Potential Use as Biomarkers for Personalized Oncology.Journal of personalized medicine · 2022Review
- Editorial: Medical Application and Radiobiology Research of Particle Radiation.Frontiers in public health · 2022Article
- Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.Frontiers in public health · 2022Article
- Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
- Article
- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Artificial Neural Network with Composite Architectures for Prediction of Local Control in Radiotherapy.IEEE transactions on radiation and plasma medical sciences · 2019Article
- Review
- Selection of external beam radiotherapy approaches for precise and accurate cancer treatment.Journal of radiation research · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Radiogenomics and radiotherapy response modeling.Physics in medicine and biology · 2017Review
- Clinical and histopathological characteristics of patients with prostate cancer in the BioBank Japan project.Journal of epidemiology · 2017Article
- Artificial intelligence in radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyReview
- National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.Practical radiation oncologyArticle
- Internet-based computer technology on radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyReview
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
Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.
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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.