ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2020
Radiomics in radiation oncology-basics, methods, and limitations.
Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 3 of them syntheses 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
64 citing papers in PubMed, 3 syntheses or guidelines pooled it, 115 citations in OpenAlex.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Patient outcome prognosis for external beam radiation therapy using CBCT-based radiomics: a systematic review.Biomedical physics & engineering express · 2026Pooled it
- Pooled it
- Radiomics analysis of 3D spheroids captures irradiation-induced structural and textural changes.Scientific reports · 2026Article
- The Evolving Role of Radiation Therapy Technologists in Head and Neck Cancer: A Narrative Review and Operational Framework.Journal of imaging · 2026Review
- Machine Learning-Based Prognosis Prediction in Glioblastoma Multiforme Patients by Integrating Clinical Data with Multimodal Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Clinical and radiomics parameter prognostication in metastatic uveal melanoma patients treated with hepatic arterial infusion chemotherapy.The oncologist · 2025Article
- Using Machine Learning to Predict the Prognosis in Endometrial Cancer Patients Undergoing Fertility-Sparing Treatment.Cancer medicine · 2025Article
- Review
- Cross-regional radiomics: a novel framework for relationship-based feature extraction with validation in Parkinson's disease motor subtyping.BioData mining · 2025Article
- 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.Medical oncology (Northwood, London, England) · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Constructing different machine learning models for identifying pelvic lipomatosis based on AI-assisted CT image feature recognition.Abdominal radiology (New York) · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Frontiers in oncology · 2025Article
- Clinical value of the nomogram model based on endoscopic ultrasonography radiomics and clinical indicators in identifying benign and malignant lesions of the pancreas.Frontiers in oncology · 2025Article
4 more citing papers are in PubMed but not listed here.
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.
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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.