Jiun-Lin Yan

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

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.

Less Invasive Phenotype Found in Isocitrate Dehydrogenase-mutated Glioblastomas than in Isocitrate Dehydrogenase Wild-Type Glioblastomas: A Diffusion-Tensor Imaging Study

Purpose To explore the diffusion-tensor (DT) imaging-defined invasive phenotypes of both isocitrate dehydrogenase (IDH-1)-mutated and IDH-1 wild-type glioblastomas. Materials and Methods Seventy patients with glioblastoma were prospectively recruited and imaged preoperatively. All patients provided signed consent, and the local research ethics committee approved the study. Patients underwent surgical resection, and tumor samples underwent immunohistochemistry for IDH-1 R132H mutations. DT imaging data were coregistered to the anatomic magnetic resonance study and reconstructed to provide the anisotropic and isotropic components of the DT. The invasive phenotype was determined by using previously published criteria and correlated with IDH-1 mutation status by using the Freeman-Halton extension of the Fisher exact probability test. Results Nine patients had an IDH-1 mutation and 61 had IDH-1 wild type. All of the patients with IDH-1 mutation had a minimally invasive DT imaging phenotype. Among the IDH-1 wild-type tumors, 42 of 61 (69%) were diffusively invasive glioblastomas, 14 of 61 (23%) were locally invasive, and five of 61 (8%) were minimally invasive (P < .001). Conclusion IDH-mutated glioblastomas have a less invasive phenotype compared with IDH wild type. This finding may have implications for individualizing the extent of surgical resection and radiation therapy volumes.

Intratumoral Heterogeneity of Tumor Infiltration of Glioblastoma Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging

Purpose The purpose of this study is to propose a novel interpretation method of diffusion tensor imaging (DTI) using the joint histogram analysis of DTI-p and -q. With this method we explored the heterogeneity of tumor infiltration and examined the prognostic value of tumor infiltrative patterns for patient survival. Materials and methods A total of 115 primary glioblastoma patients (mean age 59.3 years, 87 males) were prospectively recruited from July 2010 to August 2015. Patients underwent preoperative MRI scans and maximal safe resection. DTI was processed and decomposed into p and q components. The univariate and joint histograms of DTI-p and -q were constructed using the pixels of contrast-enhancing and non-enhancing regions respectively. Eight joint histogram features were obtained and correlated with tumor progression rate and patient survival using cox-regression model. Their prognostic values were compared with clinical factors using receiver operating characteristic curves. Results The subregion of increased DTI-p and decreased DTI-q accounted for the largest proportion. Additional diffusion patterns can be identified via joint histogram analysis. Particularly, higher proportion of decreased DTI-p and increased DTI-q in non-enhancing region contributed to worse progression-free survival and overall survival (both HR = 1.12, p < 0.001); its proportion showed a positive correlation (p = 0.010, r = 0.35) with tumor progression rate. Conclusion Joint histogram analysis of DTI can provide a comprehensive measure of heterogeneity in infiltration, which showed prognostic values for glioblastoma patients. The subregion of decreased DTI-p and increased DTI-q in non-enhancing region may indicate a more invasive habitat. Funding This study was funded by a National Institute for Health Research (NIHR) Clinician Scientist Fellowship (SJP, project reference NIHR/CS/009/011); CRUK core grant C14303/A17197 and A19274 (FM lab); Cambridge Trust and China Scholarship Council (CL & SW); the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung, Taiwan (JLY); CRUK & EPSRC Cancer Imaging Centre in Cambridge & Manchester (FM & TT, grant C197/A16465); Royal College of Surgeons of England (RS); NIHR Cambridge Biomedical Research Centre (TM & SJP). The Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre. We would like to acknowledge the support of National Institute for Health Research, the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Conflict of Interest none Advances in knowledge Joint histogram analysis of the isotropic (p) and anisotropic (q) components of the diffusion tensor imaging can reflect the intratumoral heterogeneity of glioblastoma infiltration. Incremental prognostic values for the prediction of overall survival and progression-free survival can be achieved by the joint histogram features, when integrated with IDH-1 mutation, MGMT methylation status and other clinical factors. The non-enhancing tumor subregion in which water molecules display decreased isotropic movement and increased anisotropic movement are potentially representative of a more invasive tumor habitat. Implications for patient care This study helps us to understand how the infiltrative patterns of glioblastoma contribute to patient outcomes. The invasive subregion identified by this approach may have clinical implications for personalized surgical resection and targeted radiation therapy. Summary Statement The joint histogram analysis may help to better understand the heterogeneity of tumor infiltration. The decreased DTI-p and increased DTI-q in non-enhancing region may be able to define an invasive subregion responsible for tumor progression.

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

Introduction Glioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge. Methods A total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS). Results The MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013). Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region. Discussion This study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.

Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging: Correlation with Patient Survival

Glioblastoma exhibits profound intratumoral heterogeneity in blood perfusion, which may cause inconsistent therapy response. Particularly, low perfusion may create a hypoxic microenvironment and induce resistant clones. Thus, developing validated imaging approaches that define low perfusion compartments is crucial for clinical management. Here we present a novel method identifying two low perfusion compartments in glioblastoma using multiparametric physiological magnetic resonance imaging. Both compartments displayed hypoxic and pro-inflammatory metabolic signatures, suggesting selective stress, and affected progression-free survival (PFS) and overall survival (OS). The lactate level in the low perfusion and restricted diffusion compartment showed increased hazard ratio (PFS: HR 2.995, CI 1.012-8.861, P = 0.047; OS: HR 4.974, CI 1.608-15.39, P = 0.005). This compartment may represent a treatment resistant sub-region contributing to glioblastoma invasiveness. This approach was based on clinically available imaging modalities and could thus provide crucial pretreatment information for clinical decision making.

Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up.

There is an expanding research interest in high‐grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two‐stage semi‐automatic method to co‐register MRI scans at different time points before and after surgical and adjuvant treatment of high‐grade gliomas. This two‐stage co‐registration includes a linear co‐registration of the semi‐automatically derived mask of the preoperative contrast‐enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non‐linear manner to co‐register conventional contrast‐enhanced T1‐weighted images. Targeted registration errors were calculated and compared with linear and non‐linear co‐registered images. Targeted registration errors were smaller for the semi‐automatic non‐linear co‐registration compared with both the non‐linear and linear co‐registered images. This was further visualized using a three‐dimensional structural similarity method. The semi‐automatic non‐linear co‐registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright