Extent of tumor—brain interface: a new tool to predict evolution of malignant gliomas

Abstract

These findings indicate that STV may be a useful tool for predicting the evolution of malignant glioma. Moreover, in future gene therapy trials in which such in situ approaches are used, increasing density and improved distribution of transfer cells should be taken into consideration as an important issue for efficacy.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

“…Moreover, we repeated the process with Random Forest feature selection and Lasso classification and produced the same results. There was also indirect evidence that we were not overfitting the data as the progression phenotype was concordant with the literature,[18;41;44;76–78] which was selected repeatedly by the SVM and Lasso models. Furthermore, whilst undergoing feature estimation, a difference in heterogeneity between progressors and pseudoprogressors was demonstrated unequivocally by the large divergence of progressor MFs from pseudoprogressor MFs by 7 months (Fig 2d–2f).…”

“…The relationship between total lesion area and perimeter is shown in (Fig 4d), where data points above the curve represent tumours with more surface area than would be expected for a sphere. Tumours showing progression were generally above or on the curve, consistent with a longer contour length-per-unit-area of T 2 -hyperintensity compared to areas of pseudoprogression (second-order polynomial curves for progression and pseudoprogression both gave R 2 > 0.9; these two curves and the curve of a sphere were different from one another: P = 0.03, F = 3.1, 6 dfn, 21 dfd; extra sum-of-squares F test), and is compatible with progressors having a more irregular or frond-like shape than a spherical shape,[42–44] although this difference was rarely visible in the image (Fig 5a and 5b). …”

Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma

“…16 A larger threedimensional (3D) volume has been used to predict a worse prognosis. 17 In a more recent study, the MRI images of 321 patients were retrospectively analysed based on 11 MRI signs. 18 Only subependymal enhancement (Fig 2) was predictive for true tumour progression (p¼0.001) with 93.3% specificity, although the sensitivity and negative predictive values were only 38.1% and 41.8%, respectively.…”

Multivariate discriminant analysis of multiparametric brain MRI to differentiate high grade and low grade gliomas — A computer-aided diagnosis development study

“…Awasthi et al made discriminant function analysis based on dynamic contrast-enhanced (DCE) perfusion derived indices and immunohistochemical markers to classify low and high grade gliomas, and reported an overall sensitivity of %92.1 [4]. Valéry et al studied correlation between surface of tumor volume (STV) and clinical parameters [5]. Yei et al studied staging of gliomas using data mining techniques [6].…”