SynthesisEuropean radiology experimental2023
Are deep models in radiomics performing better than generic models? A systematic review.
Synthesis in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 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
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.PLOS digital health · 2026Article
- Radiomics in musculoskeletal imaging: what is its role in the era of deep learning?Skeletal radiology · 2026Article
- An interpretable PET/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks.European journal of nuclear medicine and molecular imaging · 2026Article
- Comparing deep-learning, radiomics, and fusion models for parathyroid tumor classification using ultrasound: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- A novel deep learning and radiomics approach based on DCE-MRI for predicting the P53 mutation status in hepatocellular carcinoma.Journal of gastrointestinal oncology · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Medical image pretraining-based transfer learning for generalizable and robust diagnosis of bone tumors on radiographs: a multi-center study.Insights into imaging · 2026Article
- Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.The Indian journal of radiology & imaging · 2026Review
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- A practical 10-step recipe for conducting radiomic studies.European radiology experimental · 2026Review
- CT Radiomics Models Did Not Outperform Experts in Predicting [Current oncology (Toronto, Ont.) · 2026Article
- Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma.NPJ digital medicine · 2026Article
- Augmenting medical image specific foundation model with classical radiomic signatures for improved nasopharyngeal carcinoma stage classification.Frontiers in medical technology · 2026Article
- A comprehensive model with dual plane ultrasound based deep learning radiomics to distinguish parotid pleomorphic adenoma from Warthin tumor.Frontiers in oncology · 2026Article
- Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.NPJ precision oncology · 2025Article
- Article
- Development of a radiomics-3D deep learning fusion model for prognostic prediction in pancreatic cancer.BMC cancer · 2025Article
- In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApplication of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, and statistical features defined by formulas. Recently, deep learning methods were applied. It is unclear whether deep models (DMs) can outperform generic models (GMs).
methodsWe identified publications on PubMed and Embase to determine differences between DMs and GMs in terms of receiver operating area under the curve (AUC).
resultsOf 1,229 records (between 2017 and 2021), 69 studies were included, 61 (88%) on tumours, 68 (99%) retrospective, and 39 (56%) single centre; 30 (43%) used an internal validation cohort; and 18 (26%) applied cross-validation. Studies with independent internal cohort had a median training sample of 196 (range 41-1,455); those with cross-validation had only 133 (43-1,426). Median size of validation cohorts was 73 (18-535) for internal and 94 (18-388) for external. Considering the internal validation, in 74% (49/66), the DMs performed better than the GMs, vice versa in 20% (13/66); no difference in 6% (4/66); and median difference in AUC 0.045. On the external validation, DMs were better in 65% (13/20), GMs in 20% (4/20) cases; no difference in 3 (15%); and median difference in AUC 0.025. On internal validation, fused models outperformed GMs and DMs in 72% (20/28), while they were worse in 14% (4/28) and equal in 14% (4/28); median gain in AUC was + 0.02. On external validation, fused model performed better in 63% (5/8), worse in 25% (2/8), and equal in 13% (1/8); median gain in AUC was + 0.025.
conclusionsOverall, DMs outperformed GMs but in 26% of the studies, DMs did not outperform GMs.
Indexed as
Identifiers
What Socratic 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 Socratic graph.