MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma

Creative Commons License

Sarioglu F. C., Sarioglu O., Güleryüz Uçar H., Özer E., İnce D., Olgun H. N.

EUROPEAN RADIOLOGY, sa.10, ss.5227-5236, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00330-020-06908-4
  • Dergi Adı: EUROPEAN RADIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.5227-5236
  • Anahtar Kelimeler: Child, Hemangioma, Magnetic resonance imaging, Rhabdomyosarcoma, Computer-assisted image analysis, INTERNATIONAL-SOCIETY, NECK TUMORS, CLASSIFICATION, HEAD, CHILDHOOD, CARCINOMA, BENIGN, IMAGES, RISK
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Objectives To evaluate the diagnostic performance of MRI texture analysis (TA) for differentiation of pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH). Methods This 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. Results Thirty-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). Conclusion MRI-based TA may contribute to differentiate RMS from IH without invasive procedures.