ReviewCancers2023
Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Pooled it
- Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- CT-guided core needle biopsy in ultrasound-inaccessible suprahyoid deep neck spaces.European radiology · 2026Article
- Concordance of fine-needle aspiration cytology and histopathology in parotid masses: the added role of MRI.The Journal of laryngology and otology · 2026Article
- 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
- Could a multimodal fusion model integrating CT radiomics and systemic inflammatory markers improve preoperative risk stratification of parotid masses? A retrospective exploratory study.Frontiers in immunology · 2026Article
- Review
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Classification of Parotid Tumors with Robust Radiomic Features from DCE- and DW-MRI.Journal of imaging · 2025Article
- Exploring the Landscape of Salivary Gland Disorders: A Comprehensive Bibliometric Analysis.Journal of maxillofacial and oral surgery · 2025Article
- Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.Frontiers in oncology · 2025Article
- Radiomic and Clinical Model in the Prognostic Evaluation of Adenoid Cystic Carcinoma of the Head and Neck.Cancers · 2024Article
- The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.Translational oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis 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.