ArticleFrontiers in oncology2020
Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 30 citations in OpenAlex.
- Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Added Value of Biological Effective Dose in Dosiomics-Based Modelling of Late Rectal Bleeding in Prostate Cancer.Cancers · 2024Article
- Breast cancer radiobiology: The renaissance of whole breast radiation fractionation (Review).Molecular and clinical oncology · 2024Review
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- CT-based radiomics for predicting breast cancer radiotherapy side effects.Scientific reports · 2024Article
- Article
- Article
- A review of radiomics and genomics applications in cancers: the way towards precision medicine.Radiation oncology (London, England) · 2022Review
- Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods.Heliyon · 2022Article
- Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model.Journal of personalized medicine · 2022Article
- Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI.Tomography (Ann Arbor, Mich.) · 2022Article
- Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer.Frontiers in oncology · 2022Article
- Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.Medical physics · 2021Article
- A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity.Cancers · 2021Article
- Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI.Biomolecules · 2021Article
- A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
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.