ArticleJapanese journal of radiology2021
Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas.
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- 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
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer.Oncology letters · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR/MSI-H colorectal cancer.Journal for immunotherapy of cancer · 2025Article
- Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.European radiology · 2025Article
- Value of multiparametric magnetic resonance imaging in distinguishing sinonasal lymphoma from sinonasal carcinoma: a case control study.BMC medical imaging · 2024Article
- Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.Japanese journal of radiology · 2024Article
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.European radiology · 2024Article
- Value of radiomics and deep learning feature fusion models based on dce-mri in distinguishing sinonasal squamous cell carcinoma from lymphoma.Frontiers in oncology · 2024Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability.Frontiers in neurology · 2023Article
- A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images.BMC medical imaging · 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
- A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study.Frontiers in oncology · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop and validate an MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas (SCCs). MATERIALS AND
methodsOne-hundred-and-fifty-four patients were enrolled (74 individuals with SCCs and 80 with lymphomas). After feature analysis and feature selection with variance threshold and least absolute shrinkage and selection operator (LASSO) methods, an MRI-based radiomics model with the support vector machine (SVM) classifier was constructed in differentiation between lymphomas and SCCs. Areas under the receiver operating characteristic curves (AUCs) of the MRI-based radiomics model were compared with those of radiologists using Delong test.
resultsFive features (T1 original shape Compactness2, T1 wavelet-HHH first-order Total Energy, T2 wavelet-HLH GLCM Informational Measure of Correlation1, T1 wavelet-LHL GLCM Inverse Variance and T1 square GLRLM Long Run Low Gray Level Emphasis) were finally selected in the radiomics model. The AUC values in differentiation between lymphomas and SCCs were 0.94 for the training dataset and 0.85 for the validation dataset, respectively. For all the patient datasets, the AUC values of radiomics model, readers 1, 2 and 3 were 0.92, 0.76, 0.77 and 0.80, respectively. For the validation datasets, no significant difference was found between the AUCs of the radiomics model and those of the three radiologist (P = 0.459, 0.469, 0.738 for radiologist 1, 2 and 3, respectively).
conclusionAn MRI-based radiomics model can help to differentiate sinonasal lymphomas from SCCs with high accuracy.
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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.