ArticleBMC medical imaging2025
Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-b-value DWI-based habitat radiomics analysis for differentiating sinonasal small round cell malignant tumors from non-small round cell malignant tumors.Oral radiology · 2026Article
- Beyond whole-image learning: anatomically partitioned deep learning models for superior sinonasal disease classification.European radiology · 2026Article
- Review
- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- Interpretable clinical-radiomics fusion model for noninvasive prediction of INI1 deficiency and prognosis in sinonasal undifferentiated carcinoma (SNUC): a two-center study.BMC medical imaging · 2026Article
- A multimodal deep learning model for predicting impending rupture in symptomatic abdominal aortic aneurysms using CTA and clinical data.Frontiers in cardiovascular medicine · 2026Article
- [Skull base surgery-where do we stand?]HNO · 2025Review
- Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Chinese expert consensus on imaging examination and diagnosis of nasal cavity and paranasal sinus tumors.Frontiers in oncology · 2025Review
- Predicting the growth of asymptomatic small abdominal aortic aneurysms (AAA) based on deep learning.Frontiers in physiology · 2025Article
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6 authors.
Funding
Abstract
purposeTo develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and olfactory neuroblastoma (ONB) and to evaluate whether the DL models could improve the diagnostic performance of Senior radiologist (SR) and Junior radiologist (JR).
methodsThis retrospective analysis consisted of 465 patients (229 sinonasal SCCs, 128 ACCs and 108 ONBs). The training and validation cohorts included 325 and 47 patients and the independent external testing cohort consisted of 93 patients. MRI images included T2-weighted image (T2WI), contrast-enhanced T1-weighted image (CE-T1WI) and apparent diffusion coefficient (ADC). We analyzed the conventional MRI features to choose the independent predictors and built the conventional MRI model. Then we compared the macro- and micro- area under the curves (AUCs) of different sequences and different DL networks to formulate the best DL model [artificial intelligence (AI) model scheme]. With AI assistance, we observed the diagnostic performances between SR and JR. The diagnostic efficacies of SR and JR were assessed by accuracy, Recall, precision, F1-Score and confusion matrices.
resultsThe independent predictors of conventional MRI included intensity on T2WI and intracranial invasion of sinonasal malignancies. With ExtraTrees (ET) classier, the conventional MRI model owned AUC of 78.8%. For DL models, ResNet101 network showed better performance than ResNet50 and DensNet121, especially for the mean fusion sequence (macro-AUC = 0.892, micro-AUC = 0.875, Accuracy = 0.810), and also good for the ADC sequence (macro-AUC = 0.872, micro-AUC = 0.874, Accuracy = 0.814). Grad-CAM showed that DL models focused on solid component of lesions. With the best AI scheme (ResNet101-mean sequence-based DL model) assistance, the diagnosis performances of SR (accuracy = 0.957, average Recall = 0.962, precision = 0.955, F1-Score = 0.957) and JR (accuracy = 0.925, average Recall = 0.917, precision = 0.931, F1-Score = 0.923) were significantly improved.
conclusionThe ResNet101 network with mean sequence based DL model could effectively differential between sinonasal SCC, ACC and ONB and improved the diagnostic performances of both senior and junior radiologists.
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