ArticleScientific reports2021
Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Detection of breast cancer lesions using APT weighted MRI: a systematic review.Journal of translational medicine · 2025Pooled it
- Deep learning for differential diagnosis of parotid tumors based on 2.5D magnetic resonance imaging.Annals of medicine · 2025Article
- Multimodality Imaging of Warthin's Tumor: PET/CT, Scintigraphy, MRI, and CT.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Maximum signal intensity ratio on T1-weighted images for differentiating parotid gland tumors: a comparative study across two medical centers.BMC oral health · 2025Article
- Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.Frontiers in oncology · 2025Article
- Enhanced diagnostic accuracy of contrast-enhanced ultrasound in liver space-occupying lesions: superior sensitivity and specificity over conventional ultrasound.American journal of cancer research · 2025Article
- Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions.Japanese journal of radiology · 2024Article
- Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study.Dento maxillo facial radiology · 2024Article
- Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.Cancers · 2023Review
- A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences.The Laryngoscope · 2023Article
- Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions.BMC medical imaging · 2023Article
- Pre-styloid parapharyngeal space masses-Tumor margins as a predictor of benign versus malignant histology on pre-operative CT or MRI.The neuroradiology journal · 2022Article
- Article
- Pseudocontinuous Arterial Spin Labeling: Clinical Applications and Usefulness in Head and Neck Entities.Cancers · 2022Review
- Article
- Tumor blood flow and apparent diffusion coefficient histogram analysis for differentiating malignant salivary tumors from pleomorphic adenomas and Warthin's tumors.Scientific reports · 2022Article
- Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.European journal of radiology open · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study is to determine whether multiparametric non-contrast MR imaging including diffusion-weighted imaging (DWI), arterial spin labeling (ASL), and amide proton transfer (APT) weighted imaging can help differentiate malignant from benign salivary gland lesions. The study population consisted of 42 patients, with 31 benign and 11 malignant salivary gland lesions. All patients were evaluated using DWI, three-dimensional pseudo-continuous ASL, and APT-weighted imaging on 3 T MR imaging before treatment. Apparent diffusion coefficient (ADC), tumor blood flow (TBF), and APT-related signal intensity (APTSI) values within the lesion were compared between the malignant and benign lesions by Mann-Whitney U test. For each parameter, optimal cutoff values were chosen using a threshold criterion that maximized the Youden index for predicting malignant lesions. The performance of ADC, TBF, APTSI, individually and combined, was evaluated in terms of diagnostic ability for malignant lesions. Diagnostic performance was compared by McNemar test. APTSI was significantly higher in malignant lesions (2.18 ± 0.89%) than in benign lesions (1.57 ± 1.09%, p = 0.047). There was no significant difference in ADC or TBF between benign and malignant lesions (p = 0.155 and 0.498, respectively). The accuracy of ADC, TBF, and APTSI for diagnosing malignant lesions was 47.6%, 50.0%, and 66.7%, respectively; whereas the accuracy of the three parameters combined was 85.7%, which was significantly higher than that of each parameter alone (p = 0.001, 0.001, and 0.008, respectively). Therefore, the combination of ADC, TBF, and APTSI can help differentiate malignant from benign salivary gland lesions.
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.