ArticleEuropean radiology2020
MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 19 citations in OpenAlex.
- Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Scientific reports · 2024Article
- Advancing Pediatric Sarcomas through Radiomics: A Systematic Review and Prospective Assessment Using Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS).Diagnostics (Basel, Switzerland) · 2024Review
- Arterial Spin-Labeling in the Assessment of Pediatric Nontraumatic Orbital Lesions.AJNR. American journal of neuroradiology · 2023Article
- Clot-based radiomics features predict first pass effect in acute ischemic stroke.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2022Article
- The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia.The British journal of radiology · 2022Article
- Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.Tomography (Ann Arbor, Mich.) · 2021Article
- The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy.European radiology · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the diagnostic performance of MRI texture analysis (TA) for differentiation of pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH).
methodsThis study included 15 patients with RMS and 42 patients with IH who underwent MRI before an invasive procedure. All patients had a solitary lesion. T2-weighted and fat-suppressed contrast-enhanced T1-weighted axial images were used for TA. Two readers delineated the tumor borders for TA independently and evaluated the qualitative MRI characteristics in consensus. The differences of the texture features' values between the groups were assessed and ROC curves were calculated. Logistic regression analysis was used to analyze the value of TA with and without the combination of the qualitative MRI characteristics. A p value < 0.05 was considered statistically significant.
resultsThirty-eight texture features were calculated for each tumor. Eighteen features on T2-weighted images and 25 features on contrast-enhanced T1-weighted images were significantly different between the RMSs and IHs. On contrast-enhanced T1-weighted images, the short-zone emphasis (SZE), which was a gray-level zone length matrix (GLZLM) parameter, had the largest area under the curve: 0.899 (sensitivity 93%, specificity 87%). The independent predictor for the RMS among the qualitative MRI characteristics was heterogeneous contrast enhancement (p < 0.001). Using only a GLZLM_SZE value of lower than 0.72 was found to be the best diagnostic parameter in predicting RMS (p < 0.001; 95% CI, 8.770-992.4).
conclusionMRI-based TA may contribute to differentiate RMS from IH without invasive procedures. KEY POINTS: • Texture analysis may help to distinguish between rhabdomyosarcoma and infantile hemangioma without invasive procedures. • The gray-level zone length matrix parameters, especially the short-zone emphasis, may be a potential predictor for rhabdomyosarcoma. • Using contrast-enhanced T1-weighted images may be superior to T2-weighted images to differentiate rhabdomyosarcoma from infantile hemangioma in texture analysis.
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