Worst lead ST deviation and resolution of ST elevation at one hour for prediction of myocardial salvage, infarct size, and microvascular obstruction in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention.

BACKGROUND: ECG changes after revascularization predicts improved outcome for patients with ST-elevation myocardial infarction (STEMI). Worst lead residual (WLR) ST deviation and resolution of worst lead ST elevation (rST elevation) are simple measures that can be obtained early after PCI. The objective of the current study was to investigate whether simple ECG measures, obtained one hour following PCI, could predict cardiac magnetic resonance (CMR)-derived myocardial salvage index (MSI), infarct size (IS), and microvascular obstruction (MVO) in patients with STEMI included in the MITOCARE trial. METHODS: The MITOCARE trial included 165 patients with a first-time STEMI presenting within six hours of symptom onset. The current analysis included patients that had an ECG recorded at baseline and one hour after PCI and underwent CMR imaging after 3-5 days. Independent core laboratories determined WLR ST deviation, rST elevation, and the CMR variables (MSI, IS, and MVO). RESULTS: 83 patients with a mean age of 61 years were included. 83.1% were males and 41% had anterior infarctions. In logistic regression models, WLR ST deviation was a statistically significant predictor of IS (OR 2.2, 95% CI 1.3-3.8) and MVO (OR 2.8, 95% CI 1.5-5.2), but not of MSI (OR 0.8, 95% CI 0.5-1.2). rST elevation showed a trend toward a significant association with IS (OR 0.3, 95% CI 0.1-1.0), but not with the other CMR variables. CONCLUSION: WLR ST deviation one hour after PCI was a predictor of IS and MVO. WLR ST deviation, a measure easily obtained from ECGs following PCI, may provide important prognostic information in patients with STEMI.

Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports.

BACKGROUND: Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation. METHODS: In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor. RESULTS: Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66-68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products. CONCLUSIONS: Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.

Angiogenesis and radiological tumor growth in patients with glioblastoma.

BACKGROUND: The preoperative growth of human glioblastomas (GBMs) has been shown to vary among patients. In animal studies, angiogenesis has been linked to hypoxia and faster growth of GBM, however, its relation to the growth of human GBMs is sparsely studied. We have therefore aimed to look for associations between radiological speed of growth and microvessel density (MVD) counts of the endothelial markers vWF (Factor VIII related antigen) and CD105 (endoglin). METHODS: Preoperative growth was estimated from segmented tumor volumes of two preoperative T1-weighted postcontrast magnetic resonance imaging scans taken ≥14 days apart in patients with newly diagnosed GBMs. A Gompertzian growth curve was computed from the volume data and separated the patients into two groups of either faster or slower tumor growth than expected. MVD counts of the immunohistochemical markers von Willebrand factor (vWF) (a pan-endothelial marker) and CD105 (a marker of proliferating endothelial cells) were assessed for associations with fast-growing tumors using Mann-Whitney U tests and a multivariable binary logistic regression analysis. RESULTS: We found that only CD105-MVD was significantly associated with faster growth in a univariable analysis (p = 0.049). However, CD105-MVD was no longer significant when corrected for the presence of thromboses and high cellular density in a multivariable model, where the latter features were significant independent predictors of faster growth with respective odds ratios 4.2 (95% confidence interval, 1.2, 14.3), p = 0.021 and 2.6 (95% confidence interval, 1.0, 6.5), p = 0.048. CONCLUSIONS: MVDs of neither endothelial marker were independently associated with faster growth, suggesting angiogenesis-independent processes contribute to faster glioblastoma growth.

Impacts of MR spectroscopic imaging on glioma patient management.

Magnetic resonance (MR) modalities are routine imaging tools in the diagnosis and management of gliomas. MR spectroscopic imaging (MRSI), which relies on the metabolic characteristics of tissues, has been developed to accelerate the understanding of gliomas and to aid in effective clinical decision making and development of targeted therapies. In this review, the potentials and practical challenges to frequently use this technique in clinical management of gliomas are discussed. The applications of new biomarkers detectable by MRSI in differential glioma diagnosis, pre- and post-treatment evaluations, and neurosurgery are also addressed.

MGMT Promoter Methylation Status Is Not Related to Histological or Radiological Features in IDH Wild-type Glioblastomas.

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important favorable predictive marker in patients with glioblastoma (GBM). We hypothesized that MGMT status could be a surrogate marker of pretreatment tumor biology observed as histopathological and radiological features. Apart from some radiological studies aiming to noninvasively predict the MGMT status, few studies have investigated relationships between MGMT status and phenotypical tumor biology. We have therefore aimed to investigate such relationships in 85 isocitrate dehydrogenase (IDH) wild-type GBMs. MGMT status was determined by methylation-specific PCR and was assessed for associations with 22 histopathological features, immunohistochemical proliferative index and microvessel density measurements, conventional magnetic resonance imaging characteristics, preoperative speed of tumor growth, and overall survival. None of the investigated histological or radiological features were significantly associated with MGMT status. Methylated MGMT status was a significant independent predictor of improved overall survival. In conclusion, our results suggest that MGMT status is not related to the pretreatment phenotypical biology in IDH wild-type GBMs. Furthermore, our findings suggest the survival benefit of MGMT methylated GBMs is not due to an inherently less aggressive tumor biology, and that conventional magnetic resonance imaging features cannot be used to noninvasively predict the MGMT status.

Stochastic growth pattern of untreated human glioblastomas predicts the survival time for patients.

Glioblastomas are highly malignant brain tumors. Knowledge of growth rates and growth patterns is useful for understanding tumor biology and planning treatment logistics. Based on untreated human glioblastoma data collected in Trondheim, Norway, we first fit the average growth to a Gompertz curve, then find a best fitted white noise term for the growth rate variance. Combining these two fits, we obtain a new type of Gompertz diffusion dynamics, which is a stochastic differential equation (SDE). Newly collected untreated human glioblastoma data in Seattle, US, re-verify our model. Instead of growth curves predicted by deterministic models, our SDE model predicts a band with a center curve as the tumor size average and its width as the tumor size variance over time. Given the glioblastoma size in a patient, our model can predict the patient survival time with a prescribed probability. The survival time is approximately a normal random variable with simple formulas for its mean and variance in terms of tumor sizes. Our model can be applied to studies of tumor treatments. As a demonstration, we numerically investigate different protocols of surgical resection using our model and provide possible theoretical strategies.

Microglia and macrophages in human glioblastomas: A morphological and immunohistochemical study.

Glioblastomas (GBMs), a type of highly malignant brain tumour, contain various macrophages/microglia that are known as tumour-associated macrophages (TAMs). These TAMs have various roles in tumour biology. Histopathological aspects of TAMs and associations with tumour growth assessed by magnetic resonance imaging (MRI) are poorly described. In the present study, 16 patients that had sufficient tumour tissue and histological hallmarks were examined. The tumours were classified as either slow- (n=7) or fast-growing (n=9) based on the segmented tumour volumes from MRI scans taken at diagnosis and preoperatively. Using cluster of differentiation (CD)68 and ionized calcium-binding adaptor molecule 1 (Iba1) antibodies, the number, morphology, localization and distribution of TAMs in the GBM tissue were studied. TAMs were significantly more immunoreactive for anti-Iba1 (TAMsIba1) compared with anti-CD68 (TAMsCD68; P<0.001). In central tumour areas and around vessels in the infiltration zone there were more TAMsCD68 in slow-growing tumours (P=0.003 and P=0.025, respectively). Central tumour areas contained more TAMs compared with the infiltration zone (P=0.001 for TAMsCD68 and P<0.001 for TAMsIba1). The majority of TAMs exhibited a ramified phenotype in the infiltration zone, whereas central TAMs were mostly amoeboid. TAMs were present in high numbers in most regions of the tumour, whereas there were few in necrotic areas. In conclusion, the present study demonstrated and confirmed that the high numbers of TAMs in GBMs assume a range of morphologies consistent with various activation states, and that slow-growing GBMs seem to contain a TAM-population different to their fast-growing counterparts.

The Direction of Tumour Growth in Glioblastoma Patients.

Generating MR-derived growth pattern models for glioblastoma multiforme (GBM) has been an attractive approach in neuro-oncology, suggesting a distinct pattern of lesion spread with a tendency in growing along the white matter (WM) fibre direction for the invasive component. However, the direction of growth is not much studied in vivo. In this study, we sought to study the dominant directions of tumour expansion/shrinkage pre-treatment. We examined fifty-six GBMs at two time-points: at radiological diagnosis and as part of the pre-operative planning, both with contrast-enhanced T1-weighted MRIs. The tumour volumes were semi-automatically segmented. A non-linear registration resulting in a deformation field characterizing the changes between the two time points was used together with the segmented tumours to determine the dominant directions of tumour change. To compute the degree of alignment between tumour growth vectors and WM fibres, an angle map was calculated. Our results demonstrate that tumours tend to grow predominantly along the WM, as evidenced by the dominant vector population with the maximum alignments. Our findings represent a step forward in investigating the hypothesis that tumour cells tend to migrate preferentially along the WM.

When did the glioblastoma start growing, and how much time can be gained from surgical resection? A model based on the pattern of glioblastoma growth in vivo.

OBJECTIVES: Observational data on the natural course of tumor growth in humans is sparse, and mathematical models of tumor growth are often needed to answer questions related to growth. In this study, a theoretical model of glioblastoma growth was used to investigate two questions often asked by patients and clinicians. First, when did the tumor start growing? Second, how much survival time can be gained from various extents of surgical resection (EOR)?. PATIENTS AND METHODS: A gompertzian growth curve was fitted from observational data of pre-treatment growth from 106 glioblastoma patients based on repeated volume segmentations. The curve was used to find the theoretical time since tumor initiation. In addition, as a proxy for the potential survival gain from surgery, the number of days until re-growth would reach the preoperative tumor volume were calculated for different extents of resection. RESULTS: The estimated age of the glioblastomas at diagnosis was median 330 days, but ranging from 156 days to 776 days, depending on the tumor volume at diagnosis. The median survival gains from 50%, 75%, 90%, 95% and 99% EOR were, 1.4, 2.5, 3.6, 4.3, and 5.6 months, respectively. However, survival benefit from surgery also depends on lesion volume. In theory, 100 days may be gained from 95% EOR in a 10 mL lesion or a 50% EOR in a 90 ml lesion. CONCLUSION: In conclusion, we postulate that glioblastoma might originate median 330 days before the diagnosis, assuming the same growth pattern and biology from day one. The theoretical survival benefit of glioblastoma resection is much higher with higher EORs, suggesting that the last milliliters of resection matter the most. Our data also suggest that gain from resection is higher in larger lesions, suggesting that lesion volume may be taken into account in clinical decision-making.

Histopathologic Features in Relation to Pretreatment Tumor Growth in Patients with Glioblastoma.

BACKGROUND: Rapid growth is a well-known property of glioblastoma (GBM); however, growth rates vary among patients. Mechanisms behind such variation have not been widely studied in human patients. We sought to investigate relationships between histopathologic features and tumor growth estimated from pretreatment magnetic resonance imaging scans. METHODS: In 106 patients with GBM, 2 preoperative T1-weighted magnetic resonance imaging scans obtained at least 14 days apart were segmented to assess tumor growth. A fitted Gompertzian growth curve based on the segmented volumes divided the tumors into 2 groups: faster and slower growth than expected based on the initial tumor volume. Histopathologic features were investigated for associations with these groups, using univariable and multivariable logistic regression analyses. RESULTS: The presence of high cellular density and thromboses was significantly associated with radiologic growth in the multivariable analysis (P = 0.018 and 0.019, respectively), with respective odds ratios of 3.0 (95% confidence interval, 1.2-7.4) and 4.3 (95% confidence interval, 1.3-14.5) for faster growing tumors. CONCLUSIONS: Our findings show that high cellular density and thromboses are significant independent predictors of faster growth in human GBM. This finding underlines the importance of hypercellularity as a criterion in glioma grading. Furthermore, our findings are concordant with hypotheses suggesting hypoxia triggered by thromboses to be relevant for growth of GBM.

Does Pretreatment Tumor Growth Hold Prognostic Information for Patients with Glioblastoma?

BACKGROUND: Glioblastomas are highly aggressive and heterogeneous tumors, both in terms of patient outcome and molecular profile. Magnetic resonance imaging of tumor growth could potentially reveal new insights about tumor biology noninvasively. The aim of this exploratory retrospective study was to investigate the prognostic potential of pretreatment growth rate of glioblastomas, after controlling for known prognostic factors. METHODS: A growth model derived from clinical pretreatment postcontrast T1-weighted magnetic resonance imaging images was used to divide 106 glioblastoma patients into 2 groups. The "faster growth" group had tumors growing faster than expected based on their volume at diagnosis, whereas the "slower growth" group had tumors growing slower than expected. Associations between tumor growth and survival were examined by the use of multivariable Cox regression and logistic regression. RESULTS: None of the known prognostic factors were significantly associated with tumor growth. An extended multivariable Cox model showed that during the first 12 months of follow-up, there was no significant difference in survival between faster and slower growing tumors. Beyond 12 months' follow-up, however, there was a significant, independent survival benefit in having a tumor with slower pretreatment growth. In a multiple logistic regression model including patients receiving both radiotherapy and chemotherapy (n = 82), slower pre-treatment growth of the tumor was shown to be a significant predictor of 2-year survival (odds ratio 4.4). CONCLUSIONS: Pretreatment glioblastoma growth harbors prognostic information. Patients with slower growing tumors have higher odds of survival beyond 2 years, adjusted for other prognostic factors.

Glioblastoma Segmentation: Comparison of Three Different Software Packages.

To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyagerTM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T1-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice's similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from -2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ≥ 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice's similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting.

Growth dynamics of untreated glioblastomas in vivo.

BACKGROUND: Glioblastomas are primary malignant brain tumors with a dismal prognosis. Knowledge of growth rates and underlying growth dynamics is useful for understanding basic tumor biology, developing realistic tumor models, and planning treatment logistics. METHODS: By using repeated pretreatment contrast-enhanced T1-weighted MRI scans from 106 patients (aged 26-83 years), we studied the growth dynamics of untreated glioblastomas in vivo. Growth rates were calculated as specific growth rates and equivalent volume doubling times. The fit of different possible growth models was assessed using maximum likelihood estimations. RESULTS: There were large variations in growth rates between patients. The median specific growth rate of the tumors was 1.4% per day, and the equivalent volume doubling time was 49.6 days. Exploring 3 different tumor growth models showed similar statistical fit for a Gompertzian growth model and a linear radial growth model and worse fit for an exponential growth model. However, large tumors had significantly lower growth rates than smaller tumors, supporting the assumption that glioblastomas reach a plateau phase and thus exhibit Gompertzian growth. CONCLUSION: Based on the fast growth rate of glioblastoma shown in this study, it is evident that poor treatment logistics will influence tumor size before surgery and can cause significant regrowth before adjuvant treatment. Since there is a known association between tumor volume, extent of surgical resection, and response to adjuvant therapy, it is likely that waiting times play a role in patient outcomes.