ArticleAJNR. American journal of neuroradiology2017
MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma.
Article in AJNR. American journal of neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Establishment and validation of a model for predicting postoperative recurrence of sinonasal tumors based on multimodality MRI parameters.Translational cancer research · 2026Article
- Radiologic Features for Differentiating Sinonasal Inverted Papilloma and Squamous Cell Carcinoma Arising From Inverted Papilloma Without Bone Destruction.Journal of rhinology : official journal of the Korean Rhinologic Society · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Utilizing a publicly accessible automated machine learning platform to enable diagnosis before tumor surgery.Communications medicine · 2025Article
- Role of Radiomics to Predict Malignant Transformation of Sinonasal Inverted Papilloma: A Systematic Review.Cancers · 2025Review
- Chinese expert consensus on imaging examination and diagnosis of nasal cavity and paranasal sinus tumors.Frontiers in oncology · 2025Review
- Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.European journal of medical research · 2024Article
- Review
- Texture analysis using short-tau inversion recovery magnetic resonance images to differentiate squamous cell carcinoma of the gingiva from medication-related osteonecrosis of the jaw.Oral radiology · 2024Article
- Evaluation of cortical and trabecular bone structure of the mandible in patients with ankylosing spondylitis.Scientific reports · 2023Article
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
- The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Review
- Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst.Scientific reports · 2022Article
- Preoperative Prediction of Malignant Transformation of Sinonasal Inverted Papilloma Using MR Radiomics.Frontiers in oncology · 2022Article
- Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation.PloS one · 2022Article
- Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.Frontiers in neuroimaging · 2022Article
- MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeBecause sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review. MATERIALS AND
methodsAdult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus.
resultsThe inverted papilloma (
conclusionsMR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.
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