Differentiation of true-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventional MRI

Chen, Xin; Wei, Xinhua; Zhang, Zhongping; Ruimeng, Yang; Zhu, Yanjie; Jiang, Xinqing

doi:10.1016/j.clinimag.2015.04.003

Cited by 52 publications

(39 citation statements)

References 23 publications

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“…This fact can be due to the 3D analysis developed, which translates into an averaged nondirectional mapping of tumour heterogeneity, instead of most standard 2D analyses, where different distances or directions lead to different matrices with partial information. 15,16 However, according to Ng et al, 29 whole-tumour analysis was indeed better than single cross-section analysis in separating the Kaplan-Meier survival curve in colorectal cancer, for which they concluded that whole-tumour analysis seems to be more representative of tumour heterogeneity. 30 Moreover, among these texture features, entropy is found again, which reflects the unpredictability of the information content of an image.…”

Section: Correlationsmentioning

confidence: 99%

“…[11][12][13][14] However, to our knowledge, only two works have studied twodimensional (2D) textural features for outcome assessment. 15,16 METHODS AND MATERIALS Patients This retrospective, three-centre (blinded for review) study was approved by the local institutional review boards (blinded for review). All subjects provided written informed consent at hospital admittance for research and treatment.…”

Section: Introductionmentioning

confidence: 99%

“…13 It characterizes large areas within the tumour (groups of voxels) to provide information of regional heterogeneity. 8,9,15,21 Each cell in RLM (i,j) was computed as the number of runs of length j formed by voxels of intensity in box i in all the 13 possible directions in 3D. …”

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confidence: 99%

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Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Melodelima¹,

Pérez-Beteta²,

Luque³

et al. 2016

BJR

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Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient.Results: Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. INTRODUCTIONGlioblastoma (GBM) is the most frequent malignant brain tumour in adults and the most lethal type, with a median survival of 14.6 months for patients receiving the standard of care, i.e. maximal safe surgery plus radiotherapy and chemotherapy.1 Pre-operative MRI is routinely used for diagnosis, treatment planning, response evaluation and follow-up.The typical GBM appearance upon diagnosis on MRI consists of an enhanced ring with a central non-enhanced core of necrosis observed mainly on contrast-enhanced T 1 weighted images. Recently, there has been an increased use of advanced imaging techniques alone or in combination with conventional MRI modalities to characterize the connection of the so-called radio phenotype with the tumour genotype, the so-called radiogenomics (e.g. recent reviews for GBM 2,3 ). However, the use of those techniques requires further research and validation to achieve a broad clinical applicability. Thus, in clinical practice and trials, T 1 contrast-enhanced and T 2 /fluid-attenuated inversionrecovery images are still the gold standard for diagnosis and treatment planning. 4 One of the most important characteristics of GBM is its marked intratumour heterogeneity. In fact, these tumours are usually referred to as "glioblastoma multiforme", in order to highlight their heterogeneous nature. GBM tumours are formed by tumour cells which differ in their morphology, genetics and biological behaviour and may underlie the inability of conventional therapies to significantly impact patient outcomes.5 It is well known that ...

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Section: Correlationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Melodelima¹,

Pérez-Beteta²,

Luque³

et al. 2016

BJR

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“…Both volumetric plasma volume (Vp) and time-dependent leakage constant (Ktrans) volumetric histogram metrics have been developed using the dynamic enhancement MRI and a parametric response map (PRM) of dynamic susceptibility contrast PWI 17,18 . Chen et al generated 5 Gy-level co-occurrence matrix texture maps of contrast, energy, entropy, correlation, and homogeneity to differentiate PsP and TTP on MRI scans 19 . These studies directly extracted the imaging data from the selected region of interest (ROI) and selected one parameter or a combination for differentiation.…”

Section: Introductionmentioning

confidence: 99%

“…17,18 Chen et al generated 5 Gy-level co-occurrence matrix texture maps of contrast, energy, entropy, correlation, and homogeneity to differentiate PsP and TTP on MRI scans. 19 These studies directly extracted the imaging data from the selected region of interest (ROI) and selected one parameter or a combination for differentiation. However, these schemes had limited success, for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

et al. 2016

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Purpose:Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose.Methods:To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system.Results:The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance.Conclusions:The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP.

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Pseudoprogression as an adverse event of glioblastoma therapy

et al. 2017

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We explored predictive factors of pseudoprogression (PsP) and its impact on prognosis in a retrospective series of uniformly treated glioblastoma patients. Patients were classified as having PsP, early progression (eP) or neither (nP). We examined potential associations with clinical, molecular, and basal imaging characteristics and compared overall survival (OS), progression‐free survival (PFS), post‐progression survival (PPS) as well as the relationship between PFS and PPS in the three groups. Of the 256 patients studied, 56 (21.9%) were classified as PsP, 70 (27.3%) as eP, and 130 (50.8%) as nP. Only MGMT methylation status was associated to PsP. MGMT methylated patients had a 3.5‐fold greater possibility of having PsP than eP (OR: 3.48; 95% CI: 1.606–7.564; P = 0.002). OS was longer for PsP than eP patients (18.9 vs. 12.3 months; P = 0.0001) but was similar for PsP and nP patients (P = 0.91). OS was shorter–though not significantly so—for PsP than nP patients (OS: 19.5 vs. 27.9 months; P = 0.63) in methylated patients. PPS was similar for patients having PsP, eP or nP (PPS: 7.2 vs. 5.4 vs. 6.7; P = 0.43). Neurological deterioration occurred in 64.3% of cases at the time they were classified as PsP and in 72.8% of cases of eP (P = 0.14). PsP confounds the evaluation of disease and does not confer a survival advantage in glioblastoma.

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Differentiation of true-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventional MRI

Cited by 52 publications

References 23 publications

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

Pseudoprogression as an adverse event of glioblastoma therapy

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