ArticleCancers2021
A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding.Radiological physics and technology · 2026Article
- Article
- Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.Journal of applied clinical medical physics · 2026Article
- Evaluation of Variability of Dosiomics Features with Varying Prescribed Dose in Prostate Cancer.Journal of biomedical physics & engineering · 2026Article
- Future of radiotheranostics.BMJ oncology · 2026Review
- Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.Radiation oncology (London, England) · 2025Article
- Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.Neuroradiology · 2025Article
- A machine learning approach for radiation pneumonitis prediction in elderly esophageal cancer patients by integrating baseline computed tomography radiomics, dosiomics, and clinical characteristics.Physics and imaging in radiation oncology · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Classification of collagen remodeling in asthma using second-harmonic generation imaging, supervised machine learning and texture-based analysis.Frontiers in bioinformatics · 2025Article
- Dosiomics-based detection of dose distribution variations in helical tomotherapy for prostate cancer patients: influence of treatment plan parameters.Physical and engineering sciences in medicine · 2024Article
- Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.BMC cancer · 2024Article
- Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.Radiation oncology (London, England) · 2024Article
- A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.European journal of medical research · 2024Article
- Article
- Dosiomics for intensity-modulated radiotherapy in patients with prostate cancer: survival analysis stratified by baseline prostate-specific antigen and Gleason grade group in a 2-institutional retrospective study.The British journal of radiology · 2024Article
- Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study.Frontiers in oncology · 2024Article
- Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.Journal of applied clinical medical physics · 2024Article
- Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors.Scientific reports · 2023Article
- Article
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Authors and funding
16 authors.
Funding
Abstract
Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features' stability to dose calculation settings and the features' capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose-volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features' variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features' families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.
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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.