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A pinboard by
Jiun-Lin Yan

PhD Student, University of Cambridge

PINBOARD SUMMARY

Introduction The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast enhancing tumor. This usually leads to the high local progression rate despite of multi-discipline treatment. Our study aim to characterize and identify its progression from the preoperative MRI. Methods 51 newly diagnosed cerebral glioblastoma patients were included. All patients received standard treatment including 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative MRI data acquisition was performed using a 3T MRI. Imaging included volumetric post contrast T1-weighted, perfusion MR, and DTI. DTI was decomposed into isotropic (p) component and anisotropic (q) components. Voxel-based radiomics features were extracted from difference sequences. About 2/3 of the cohort were used in the convolutional neural network to train and as internal validation. An independent 1/3 of the cohort were tested blindly as external validation set. Results In the peritumoral progression area, compare to non-progression area within 10 mm around the contrast enhancing lesion, there were higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and there were lower intensity in ADC (p = 0.029) and p (p = 0.001). 35 first order features and 77 second order features were found significantly different between progression and non-progression area. By using supervised convoluted neural network, there was an overall accuracy of 92.4% in the training set (n = 37) and 78.5% in the validation set Conclusion Multimodal MR imaging, particularly diffusion tensor imaging, can demonstrate distinct characteristics in areas of potential progression on preoperative MRI. Besides, radiomics features can be a potential useful tool to further identify the tumor invasive margin.

4 ITEMS PINNED

Extent of resection of peritumoral diffusion tensor imaging-detected abnormality as a predictor of survival in adult glioblastoma patients.

Abstract: OBJECTIVE Diffusion tensor imaging (DTI) has been shown to detect tumor invasion in glioblastoma patients and has been applied in surgical planning. However, the clinical value of the extent of resection based on DTI is unclear. Therefore, the correlation between the extent of resection of DTI abnormalities and patients' outcome was retrospectively reviewed. METHODS A review was conducted of 31 patients with newly diagnosed supratentorial glioblastoma who underwent standard 5-aminolevulinic acid-aided surgery with the aim of maximal resection of the enhancing tumor component. All patients underwent presurgical MRI, including volumetric postcontrast T1-weighted imaging, DTI, and FLAIR. Postsurgical anatomical MR images were obtained within 72 hours of resection. The diffusion tensor was split into an isotropic (p) and anisotropic (q) component. The extent of resection was measured for the abnormal area on the p, q, FLAIR, and postcontrast T1-weighted images. Data were analyzed in relation to patients' outcome using univariate and multivariate Cox regression models controlling for possible confounding factors including age, O(6)-methylguanine-DNA-methyltrans-ferase methylation status, and isocitrate dehydrogenase-1 mutation. RESULTS Complete resection of the enhanced tumor shown on the postcontrast T1-weighted images was achieved in 24 of 31 patients (77%). The mean extent of resection of the abnormal p, q, and FLAIR areas was 57%, 83%, and 59%, respectively. Increased resection of the abnormal p and q areas correlated positively with progression-free survival (p = 0.009 and p = 0.006, respectively). Additionally, a larger, residual, abnormal q volume predicted significantly shorter time to progression (p = 0.008). More extensive resection of the abnormal q and contrast-enhanced area improved overall survival (p = 0.041 and 0.050, respectively). CONCLUSIONS Longer progression-free survival and overall survival were seen in glioblastoma patients in whom more DTI-documented abnormality was resected, which was previously shown to represent infiltrative tumor. This highlights the potential usefulness and the importance of an extended resection based on DTI-derived maps.

Pub.: 09 Apr '16, Pinned: 26 Jun '17

Subventricular zone involvement characterised by DTI in glioblastoma.

Abstract: Glioblastomas have a poor prognosis, possibly due to a subpopulation of therapy resistant stem cells within the heterogeneous glioblastoma. As the subventricular zone is the main source of neural stem cells, we aimed at characterising the subventricular zone using DTI to demonstrate subventricular zone involvement in glioblastoma.We prospectively included 93 patients with primary glioblastomas who underwent preoperative DTI. The non-enhancing high FLAIR signal was used to describe the infiltrative tumour margin. We used a 5 mm margin surrounding the lateral ventricles to define the subventricular zone. The subventricular zone with high FLAIR was compared with the subventricular zone without high FLAIR, control high FLAIR outside the subventricular zone and control contralateral normal appearing white matter. Normalised DTI parameters were calculated and compared between the different regions.The subventricular zone with high FLAIR showed elevated isotropic p values compared to the subventricular zone without high FLAIR (t(126)=3.9, p<0.001) and control regions (t(179)=1.9, p=0.046). Anisotropic q and fractional anisotropy values were lower in regions with high FLAIR compared to the subventricular zone without high FLAIR (t(181)=11.6, p<0.001 and t(184)=12.4, p<0.001, respectively).DTI data showed that the subventricular zone is involved in glioblastoma with elevated isotropic p values in the subventricular zone with high FLAIR, indicating tumour infiltration.

Pub.: 24 Jun '17, Pinned: 26 Jun '17

Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma.

Abstract: Purpose To determine whether regions of low apparent diffusion coefficient (ADC) with high relative cerebral blood volume (rCBV) represented elevated choline (Cho)-to-N-acetylaspartate (NAA) ratio (hereafter, Cho/NAA ratio) and whether their volumes correlated with progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma (GBM). Materials and Methods This retrospective analysis was approved by the local research ethics committee. Volumetric analysis of imaging data from 43 patients with histologically confirmed GBM was performed. Patients underwent preoperative 3-T magnetic resonance imaging with conventional, diffusion-weighted, perfusion-weighted, and spectroscopic sequences. Patients underwent subsequent surgery with adjuvant chemotherapy and radiation therapy. Overlapping low-ADC and high-rCBV regions of interest (ROIs) (hereafter, ADC-rCBV ROIs) were generated in contrast-enhancing and nonenhancing regions. Cho/NAA ratio in ADC-rCBV ROIs was compared with that in control regions by using analysis of variance. All resulting ROI volumes were correlated with patient survival by using multivariate Cox regression. Results ADC-rCBV ROIs within contrast-enhancing and nonenhancing regions showed elevated Cho/NAA ratios, which were significantly higher than those in other abnormal tumor regions (P < .001 and P = .008 for contrast-enhancing and nonenhancing regions, respectively) and in normal-appearing white matter (P < .001 for both contrast-enhancing and nonenhancing regions). After Cox regression analysis controlling for age, tumor size, resection extent, O-6-methylguanine-DNA methyltransferase-methylation, and isocitrate dehydrogenase mutation status, the proportional volume of ADC-rCBV ROIs in nonenhancing regions significantly contributed to multivariate models of OS (hazard ratio, 1.132; P = .026) and PFS (hazard ratio, 1.454; P = .017). Conclusion Volumetric analysis of ADC-rCBV ROIs in nonenhancing regions of GBM can be used to identify patients with poor survival trends after accounting for known confounders of GBM patient outcome.

Pub.: 28 Feb '17, Pinned: 26 Jun '17