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首页> 外文期刊>European radiology >Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas
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Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas

机译:常规MRI图像的纹理分析精确地预测低级胶质瘤的早期恶性转化

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ObjectivesTexture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.MethodsA total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.ResultsThe ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.ConclusionTexture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.Key Points center dot Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment.center dot Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma.center dot Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.
机译:对ObjectiveStexture对MRI图像进行的分析可以提供对人类评估不可见的额外定量信息。本研究旨在评估术前常规MRI图像的纹理分析的可行性,以预测低至高等胶质瘤的早期恶性转化,并将其效用与直方图分析进行比较。方法总共68例低级胶质瘤患者(LGG)本研究包括在该研究中,其中15种显示出恶性转化。患者随机分为培训(60%)和测试(40%)。进行纹理分析以获得培训和测试数据的最判别因子(MDF)值。接收器操作特性(ROC)曲线分析在MDF值和9个直方图参数上执行训练数据,以获得用于确定测试数据中的两组之间的正确判断率的截止值.RESULTHE ROC分析MDF值导致一个区域在T2W Flair的0.90(敏感度85%,特异性84%)的曲线(AUC)下,ADC的0.92(86%,94%),T1W为0.96(97%,84%),0.82(78%,75 %)对于T1W + Gd,分别在93%,100%,93%和92%的测试数据中正确区分两组。在星形细胞瘤亚组中,T2W Flair的AUC为0.92(88%,83%),T1W + GD为0.90(92%,74%),并在100%和92%的病例中正确区分两组。 MDF优于直方图参数的所有9.传统的术前MRI图像上的曲线分析可以准确地预测LGG的早期恶性转化,这可能引导治疗计划。在MRI图像上进行的时,在MRI图像上执行的额外的定量信息可以提供额外的定量信息。可以提供不可见的额外定量信息对人类评估。基于常规术前的MR图像的Center Dot纹理分析可以准确地预测从低级到高级胶质瘤的早期恶性转化。Center Dot纹理分析是一种临床可行的技术,可以提供确定可能性的替代和有效的方法早期恶性转型和帮助指导治疗决策。

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