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Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging

The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC). Baseline IVIM-DWI wa...

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Detalles Bibliográficos
Autores principales: Qin, Yuhui, Yu, Xiaoping, Hou, Jing, Hu, Ying, Li, Feiping, Wen, Lu, Lu, Qiang, Fu, Yi, Liu, Siye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6078652/
https://www.ncbi.nlm.nih.gov/pubmed/30045324
http://dx.doi.org/10.1097/MD.0000000000011676
Descripción
Sumario:The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC). Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case–control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D(∗), and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response. The nonresidue group had lower tumor volume, ADC, D, Correlat(ADC), Correlat(D), InvDfMom(ADC), InvDfMom(D) and InvDfMom(D)(∗) values, together with higher Contrast(D), Contrast(f), SumAverg(ADC), SumAverg(D), and SumAverg(D)(∗) values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMom(ADC) (P = .033) and SumAverg(D) (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%. Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.