ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2017
"Radio-oncomics" : The potential of radiomics in radiation oncology.
Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inclusion of dosimetric data as covariates in toxicity-related radiogenomic studies : A systematic review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2018Pooled it
- Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- CT-based radiomics for predicting breast cancer radiotherapy side effects.Scientific reports · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis.Heliyon · 2024Article
- Article
- Reduction of tumor volume during radiotherapy in patients with small-cell lung cancer and its prognostic significance.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image.BMC medical imaging · 2023Article
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study.Frontiers in oncology · 2022Article
- DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer.Frontiers in oncology · 2022Article
- Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.BMC cancer · 2021Article
- Risk Stratification UsingDiagnostics (Basel, Switzerland) · 2021Article
- Article
- Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.Cancers · 2021Article
- Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma.Frontiers in neuroscience · 2021Article
- A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients.European journal of nuclear medicine and molecular imaging · 2020Article
- Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Article
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
introductionRadiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow.
methodsAfter image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created.
resultsRetrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. DISCUSSION: Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches.
conclusionThis perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.
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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.