ReviewSeminars in radiation oncology2023
The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.
Review in Seminars in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Explicit modeling of beam geometry improves three-dimensional dose prediction for esophageal cancer radiotherapy under heterogeneous beam configurations.Radiation oncology (London, England) · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Radiomics features combined with clinical and CT features for predicting the Ki-67 index in stage T1 non-small cell lung cancer patients: a multicenter study.Journal of thoracic disease · 2026Article
- A CT-based radiomics model for predicting pain relief after radiotherapy in patients with bone metastases: a dual-center study.Frontiers in oncology · 2026Article
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Exploring the Role of CT-Based Delta-Radiomics in Unresectable Vulvar Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- RapidPlan Knowledge-Based Radiotherapy Planning Compared to Manual Planning in Locally Advanced Non-Small-Cell Lung Cancer.Cancers · 2025Article
- Diffusion-weighted imaging-derived parameters as quantitative imaging biomarkers in magnetic resonance-guided radiotherapy: a systematic review.Physics and imaging in radiation oncology · 2025Review
- Prognostic significance of the mEPE score in intermediate-risk prostate cancer patients undergoing ultrahypofractionated robotic SBRT.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Dual-radiomics based on SHapley additive explanations for predicting hematologic toxicity in concurrent chemoradiotherapy patients.Discover oncology · 2025Article
- A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure.Radiation research · 2024Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.
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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.