ReviewKorean journal of radiology2019
Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives.
Review in Korean journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 233 papers, 7 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, 7 syntheses or guidelines pooled it.
- Pre-operative MRI-Based Radiomics for Predicting Telomerase Reverse Transcriptase Promoter Mutation Status in Glioma Patients: A Systematic Review and Meta-analysis.Neurosurgical review · 2026Pooled it
- Diagnostic performance of artificial intelligence in detecting bladder carcinoma.World journal of urology · 2025Pooled it
- Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review.Skeletal radiology · 2023Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Quality assessment of radiomics research in cardiac CT: a systematic review.European radiology · 2022Pooled it
- Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas.Radiology. Imaging cancer · 2021Trial
- Limited incremental value and cross-cohort transferability of CT radiomics for survival prediction after TACE in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Predicting advanced-stage recurrence in resected hepatocellular carcinoma using preoperative gadoxetic acid-enhanced MRI radiomics.Abdominal radiology (New York) · 2026Article
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- Externally validated yet undertrained: sample size deficits in machine learning-based radiomics.European radiology · 2026Article
- AI- and Deep Learning-Based Radiomics: A State-of-the-Art Review.Journal of the Korean Society of Radiology · 2026Review
- The cult of the external validation set: undertraining is only half of the radiomics validation crisis.European radiology · 2026Article
- Radiomics in musculoskeletal imaging: what is its role in the era of deep learning?Skeletal radiology · 2026Article
- Article
- Artificial Intelligence for Personalized Prediction of Post-TIPS Outcomes: Integrating Clinical, Biochemical, and Radiomics Data-A Narrative Review.Journal of clinical medicine · 2026Review
- Precision Risk Stratification in Prostate Cancer: Unveiling MRI-Based Habitat-Driven Radiomics Model for Enhanced Treatment Guidance.Journal of imaging informatics in medicine · 2026Article
- Imaging-based stratification of Claudin 18.2-positive gastric adenocarcinoma usingEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Preoperative CT-based radiomics of suprapancreatic adipose tissue for predicting high-difficulty lymph node dissection in gastric cancer.Abdominal radiology (New York) · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics, which involves the use of high-dimensional quantitative imaging features for predictive purposes, is a powerful tool for developing and testing medical hypotheses. Radiologic and statistical challenges in radiomics include those related to the reproducibility of imaging data, control of overfitting due to high dimensionality, and the generalizability of modeling. The aims of this review article are to clarify the distinctions between radiomics features and other omics and imaging data, to describe the challenges and potential strategies in reproducibility and feature selection, and to reveal the epidemiological background of modeling, thereby facilitating and promoting more reproducible and generalizable radiomics research.
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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.