ArticleFrontiers in oncology2021
Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Automatic Segmentation of Primary Central Nervous System Lymphoma at Clinical Routine Postcontrast T1-weighted MRI.Radiology. Imaging cancer · 2025Article
- Deep Learning MRI Models for the Differential Diagnosis of Tumefactive Demyelination versusAJNR. American journal of neuroradiology · 2025Article
- Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.Neuroradiology · 2024Article
- MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas.Journal of imaging informatics in medicine · 2024Article
- Two decades of progress in glioma methylation research: the rise of temozolomide resistance and immunotherapy insights.Frontiers in neuroscience · 2024Article
- The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases.Frontiers in neurology · 2023Article
- Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review).Oncology reports · 2023Article
- Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors.Journal of neurosciences in rural practiceArticle
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Authors and funding
10 authors.
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Abstract
objectivesTo explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL). MATERIALS AND
methodsThis retrospective study analyzed the MRI data of 261 patients with pathologically diagnosed solitary and multiple cerebral GBM (n = 97), PCNSL (n = 92), and TDL (n = 72). The 3D segmentation model was trained to capture the lesion. Different enhancement data were generated by changing the pixel ratio of the lesion and non-lesion areas. The 3D classification network was trained by using the enhancement data. The accuracy, sensitivity, specificity, and area under the curve (AUC) were used to assess the value of different enhancement data on the discrimination performance. These results were then compared with the neuroradiologists' diagnoses.
resultsThe diagnostic performance fluctuated with the ratio of lesion to non-lesion area changed. The diagnostic performance was best when the ratio was 1.5. The AUCs of GBM, PCNSL, and TDL were 1.00 (95% confidence interval [CI]: 1.000-1.000), 0.96 (95% CI: 0.923-1.000), and 0.954 (95% CI: 0.904-1.000), respectively.
conclusionsDeep learning with data enhancement is useful for the accurate identification of GBM, PCNSL, and TDL, and its diagnostic performance is better than that of the neuroradiologists.
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