ArticleRadiographics : a review publication of the Radiological Society of North America, Inc2021
Radiomics in Oncology: A Practical Guide.
Article in Radiographics : a review publication of the Radiological Society of North America, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 233 papers, 10 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
233 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Role of Radiomics to Predict Disease Recurrence in Sinonasal Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2026Pooled it
- MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Radiogenomic profiling to determine BRCA alteration status-a systematic review and meta-analysis.The British journal of radiology · 2025Pooled it
- Global research trends in bone tumors: a bibliometric analysis of medical imaging.Frontiers in oncology · 2025Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Trial
- Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Trial
- Zero-shot segmentation and tumor diameter measurement of soft tissue sarcomas on MRI using a multimodal large language model.Skeletal radiology · 2026Article
- Impact of different pre-processing methods combined with combat harmonization on the reproducibility of radiomics features in healthy human brain MRI.Magma (New York, N.Y.) · 2026Article
- Differentiating high- and low-grade serous ovarian carcinoma using radiomics: a pilot study.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Machine learning prediction model for distant metastasis at diagnosis in pediatric neuroblastoma: development, interpretation, and temporal validation.Translational pediatrics · 2026Article
- Multi-parametric magnetic resonance imaging-based radiomics for differentiation of skull base osteomyelitis from locally advanced nasopharyngeal carcinoma: a multi-center external validation study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Integrating CT radiomics and morphologic features for preoperative risk stratification of gastrointestinal stromal tumours.Scientific reports · 2026Article
- SpecAtt-Net: Time-Frequency-Based Image Representations of Slice-Level Radiomic Features for Explainable Lung Cancer Classification via Slice-Wise Activation Mapping.Diagnostics (Basel, Switzerland) · 2026Article
- A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
173 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn.
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.