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T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas.
Tang, W-T; Su, C-Q; Lin, J; Xia, Z-W; Lu, S-S; Hong, X-N.
Afiliación
  • Tang WT; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
  • Su CQ; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
  • Lin J; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
  • Xia ZW; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
  • Lu SS; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China. Electronic address: lushan1118@163.com.
  • Hong XN; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China. Electronic address: hongxunning@sina.com.
Clin Radiol ; 79(5): e750-e758, 2024 May.
Article en En | MEDLINE | ID: mdl-38360515
ABSTRACT

AIM:

To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs). MATERIALS AND

METHODS:

One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 73. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models.

RESULTS:

The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort.

CONCLUSION:

The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imágenes de Resonancia Magnética Multiparamétrica / Glioma Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Clin Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imágenes de Resonancia Magnética Multiparamétrica / Glioma Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Clin Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido