ArticleEuropean radiology2023
Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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
51 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Artificial-Intelligence-Based Radiologic, Histopathologic, and Molecular Models for the Diagnosis and Classification of Malignant Salivary Gland Tumors: A Systematic Review and Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Development and validation of an interpretable MRI-based multimodal fusion model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter study.Abdominal radiology (New York) · 2026Article
- Epidemiological Characterizations and Diagnostic Strategies Variation in Parotid Gland Tumors (2010-2024): Observations Based on 2133 Cases.Cancer medicine · 2026Article
- Automated periodontitis diagnosis and staging using an end-to-end deep learning model on panoramic dental radiographs.Oral radiology · 2026Article
- A fusion model of deep learning and conventional features based on computed tomography angiography of carotid plaque for predicting the risk of acute ischemic stroke.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Deep learning-assisted prediction of hydrocephalus in preoperative-subarachnoid hemorrhage: a multi-center study.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning prediction model based on multi-modal transvaginal ultrasound scan images for endometrial cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Improving prediction accuracy of radiation-induced temporal lobe injury in nasopharyngeal carcinoma using ADC-based deep learning and dosiomics.BMC cancer · 2026Article
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- 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
- A comprehensive model with dual plane ultrasound based deep learning radiomics to distinguish parotid pleomorphic adenoma from Warthin tumor.Frontiers in oncology · 2026Article
- Assessing an Automated Noncontrast CT-based Pipeline for Sacral Tumor Classification Using a Hip Bone Reference Frame.Radiology. Imaging cancer · 2026Article
- Deep-learning-based radiomics of intratumoral and peritumoral CT images to differentiate benign from malignant parotid tumors.American journal of cancer research · 2026Article
- Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.Frontiers in bioengineering and biotechnology · 2026Article
- Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A comprehensive magnetic resonance imaging-based model for predicting lymphovascular space invasion in endometrial cancer: a retrospective observational study.Quantitative imaging in medicine and surgery · 2025Article
- Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands.European journal of radiology open · 2025Article
- Prediction of the Ki-67 proliferation index in lung adenocarcinoma using an interpretable CT-based deep learning radiomics model: a two-center study.BMC pulmonary medicine · 2025Article
- CT-based radiomics and deep learning models for predicting thyroid cartilage invasion and patient prognosis in laryngeal carcinoma.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
11 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors.
methodsData from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model.
resultsMobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001).
conclusionsThe developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS: • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.
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.