The impact of cancer patient pathway on timing of radiotherapy and survival: a cohort study in glioblastoma patients.

PURPOSE: Glioblastoma (GBM) is an aggressive brain tumor in which primary therapy is standardized and consists of surgery, radiotherapy (RT), and chemotherapy. However, the optimal time from surgery to start of RT is unknown. A high-grade glioma cancer patient pathway (CPP) was implemented in Norway in 2015 to avoid non-medical delays and regional disparity, and to optimize information flow to patients. This study investigated how CPP affected time to RT after surgery and overall survival. METHODS: This study included consecutive GBM patients diagnosed in South-Eastern Norway Regional Health Authority from 2006 to 2019 and treated with RT. The pre CPP implementation group constituted patients diagnosed 2006-2014, and the post CPP implementation group constituted patients diagnosed 2016-2019. We evaluated timing of RT and survival in relation to CPP implementation. RESULTS: A total of 1212 patients with GBM were included. CPP implementation was associated with significantly better outcomes (p < 0.001). Median overall survival was 12.9 months. The odds of receiving RT within four weeks after surgery were significantly higher post CPP implementation (p < 0.001). We found no difference in survival dependent on timing of RT below 4, 4-6 or more than 6 weeks (p = 0.349). Prognostic factors for better outcomes in adjusted analyses were female sex (p = 0.005), younger age (p < 0.001), solitary tumors (p = 0.008), gross total resection (p < 0.001), and higher RT dose (p < 0.001). CONCLUSION: CPP implementation significantly reduced time to start of postoperative RT. Survival was significantly longer in the period after the CPP implementation, however, timing of postoperative RT relative to time of surgery did not impact survival.

Incidence and outcome of pseudoprogression after radiation therapy in glioblastoma patients: A cohort study.

Background: Differentiating post-radiation MRI changes from progressive disease (PD) in glioblastoma (GBM) patients represents a major challenge. The clinical problem is two-sided; avoid termination of effective therapy in case of pseudoprogression (PsP) and continuation of ineffective therapy in case of PD. We retrospectively assessed the incidence, management, and prognostic impact of PsP and analyzed factors associated with PsP in a GBM patient cohort. Methods: Consecutive GBM patients diagnosed in the South-Eastern Norway Health Region from 2015 to 2018 who had received RT and follow-up MRI were included. Tumor, patient, and treatment characteristics were analyzed in relationship to re-evaluated MRI examinations at 3 and 6 months post-radiation using Response Assessment in Neuro-Oncology criteria. Results: A total of 284 patients were included in the study. PsP incidence 3 and 6 months post-radiation was 19.4% and 7.0%, respectively. In adjusted analyses, methylated O6-methylguanine-DNA methyltransferase (MGMT) promoter and the absence of neurological deterioration were associated with PsP at both 3 (p < .001 and p = .029, respectively) and 6 months (p = .045 and p = .034, respectively) post-radiation. For patients retrospectively assessed as PD 3 months post-radiation, there was no survival benefit of treatment change (p = .838). Conclusions: PsP incidence was similar to previous reports. In addition to the previously described correlation of methylated MGMT promoter with PsP, we also found that absence of neurological deterioration significantly correlated with PsP. Continuation of temozolomide courses did not seem to compromise survival for patients with PD at 3 months post-radiation; therefore, we recommend continuing adjuvant temozolomide courses in case of inconclusive MRI findings.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging.

BACKGROUND: Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a ß penalization factor. This study aimed to determine the optimal ß-factor for accurate quantitation of dynamic PET scans. METHODS: A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different ß-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different ß-factors on the pharmacokinetic analysis of clinical PET brain data. RESULTS: In general, RC and CVRC decreased with increasing ß-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low ß-factors resulted in high variability and an overestimation of measured activity. Increasing the ß-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased ß-factor; a change in ß-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. CONCLUSION: In a complex dynamic dataset with variable acquisition times, the optimal ß-factor provides a balance between accuracy and precision. Based on our results, we suggest a ß-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher ß-factors. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.

Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results: The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.

MR elastography identifies regions of extracellular matrix reorganization associated with shorter survival in glioblastoma patients.

Background: Biomechanical tissue properties of glioblastoma tumors are heterogeneous, but the molecular mechanisms involved and the biological implications are poorly understood. Here, we combine magnetic resonance elastography (MRE) measurement of tissue stiffness with RNA sequencing of tissue biopsies to explore the molecular characteristics of the stiffness signal. Methods: MRE was performed preoperatively in 13 patients with glioblastoma. Navigated biopsies were harvested during surgery and classified as "stiff" or "soft" according to MRE stiffness measurements (|G*|norm). Twenty-two biopsies from eight patients were analyzed by RNA sequencing. Results: The mean whole-tumor stiffness was lower than normal-appearing white matter. The surgeon's stiffness evaluation did not correlate with the MRE measurements, which suggests that these measures assess different physiological properties. Pathway analysis of the differentially expressed genes between "stiff" and "soft" biopsies showed that genes involved in extracellular matrix reorganization and cellular adhesion were overexpressed in "stiff" biopsies. Supervised dimensionality reduction identified a gene expression signal separating "stiff" and "soft" biopsies. Using the NIH Genomic Data Portal, 265 glioblastoma patients were divided into those with (n = 63) and without (n = 202) this gene expression signal. The median survival time of patients with tumors expressing the gene signal associated with "stiff" biopsies was 100 days shorter than that of patients not expressing it (360 versus 460 days, hazard ratio: 1.45, P < .05). Conclusion: MRE imaging of glioblastoma can provide noninvasive information on intratumoral heterogeneity. Regions of increased stiffness were associated with extracellular matrix reorganization. An expression signal associated with "stiff" biopsies correlated with shorter survival of glioblastoma patients.

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data.

Introduction: Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences. Methods: We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives. Results: The 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively. Discussion/Conclusion: Our results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.

MRI pulse sequence integration for deep-learning-based brain metastases segmentation.

PURPOSE: Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep-learning methods have been applied to segmentation tasks in medical images, with promising results for computer-aided diagnosis. For MR images, effectively integrating different pulse sequences is important to optimize performance. However, the best way to integrate different pulse sequences remains unclear. In addition, networks trained with a certain subset of pulse sequences as input are unable to perform when given a subset of those pulse sequences. In this study, we evaluate multiple architectural features and characterize their effects in the task of metastasis segmentation while creating a method to robustly train a network to be able to work given any strict subset of the pulse sequences available during training. METHODS: We use a 2.5D DeepLabv3 segmentation network to segment metastases lesions on brain MR's with four pulse sequence inputs. To study how we can best integrate MR pulse sequences for this task, we consider (1) different pulse sequence integration schemas, combining our features at early, middle, and late points within a deep network, (2) different modes of weight sharing for parallel network branches, and (3) a novel integration level dropout layer, which will allow the networks to be robust to performing inference on input with only a subset of pulse sequences available at the training. RESULTS: We find that levels of integration and modes of weight sharing that favor low variance work best in our regime of small amounts of training data (n = 100). By adding an input-level dropout layer, we could preserve the overall performance of these networks while allowing for inference on inputs with missing pulse sequences. We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences. Finally, we apply network visualization methods to better understand which input features are most important for network performance. CONCLUSIONS: Together, these results provide a framework for building networks with enhanced robustness to missing data while maintaining comparable performance in medical imaging applications.

Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study.

The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p < 0.001) compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences.

Responses in the diffusivity and vascular function of the irradiated normal brain are seen up until 18 months following SRS of brain metastases.

BACKGROUND: MRI may provide insights into longitudinal responses in the diffusivity and vascular function of the irradiated normal-appearing brain following stereotactic radiosurgery (SRS) of brain metastases. METHODS: Forty patients with brain metastases from non-small cell lung cancer (N = 26) and malignant melanoma (N = 14) received SRS (15-25 Gy). Longitudinal MRI was performed pre-SRS and at 3, 6, 9, 12, and 18 months post-SRS. Measures of tissue diffusivity and vascularity were assessed by diffusion-weighted and perfusion MRI, respectively. All maps were normalized to white matter receiving less than 1 Gy. Longitudinal responses were assessed in normal-appearing brain, excluding tumor and edema, in the LowDose (1-10 Gy) and HighDose (>10 Gy) regions. The Eastern Cooperative Oncology Group (ECOG) performance status was recorded pre-SRS. RESULTS: Following SRS, the diffusivity in the LowDose region increased continuously for 1 year (105.1% ± 6.2%; P < .001), before reversing toward pre-SRS levels at 18 months. Transient reductions in microvascular cerebral blood volume (P < .05), blood flow (P < .05), and vessel densities (P < .05) were observed in LowDose at 6-9 months post-SRS. Correspondingly, vessel calibers in LowDose transiently increased at 3-9 months (P < .01). The responses in HighDose displayed similar trends as in LowDose, but with larger interpatient variations. Vascular responses followed pre-SRS ECOG status. CONCLUSIONS: Our results imply that even low doses of radiation to normal-appearing brain following cerebral SRS induce increased diffusivity and reduced vascular function for up until 18 months. In particular, the vascular responses indicate the reduced ability of the normal-appearing brain tissue to form new capillaries. Assessing the potential long-term neurologic effects of SRS on the normal-appearing brain is warranted.

Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach.

PURPOSE: According to the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) of 2016, oligodendrogliomas are now defined primarily by a specific molecular signature (presence of IDH mutation and 1p19q codeletion). The purpose of our study was to assess the value of dynamic susceptibility contrast MR imaging (DSC-MRI) and diffusion-weighted imaging (DWI) to characterize oligodendrogliomas and to distinguish them from astrocytomas. METHODS: Seventy-one adult patients with untreated WHO grade II and grade III diffuse infiltrating gliomas and known 1p/19q codeletion status were retrospectively identified and analyzed using relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps based on whole-tumor volume histograms. The Mann-Whitney U test and logistic regression were used to assess the ability of rCBV and ADC to differentiate between oligodendrogliomas and astrocytomas both independently, but also related to the WHO grade. Prediction performance was evaluated in leave-one-out cross-validation (LOOCV). RESULTS: Oligodendrogliomas showed significantly higher microvascularity (higher rCBVMean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBVPeak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADCMean ≤ 1094 × 10-6 mm2/s, p = 0.009) were more likely to be oligodendrogliomas than astrocytomas. Histogram analysis of rCBV and ADC was able to differentiate between diffuse astrocytomas (WHO grade II) and anaplastic astrocytomas (WHO grade III). CONCLUSION: Histogram-derived rCBV and ADC parameter may be used as biomarkers for identification of oligodendrogliomas and may help characterize diffuse gliomas based upon their genetic characteristics.

Brain metastases with poor vascular function are susceptible to pseudoprogression after stereotactic radiation surgery.

PURPOSE: This study aimed to investigate the hemodynamic status of cerebral metastases prior to and after stereotactic radiation surgery (SRS) and to identify the vascular characteristics that are associated with the development of pseudoprogression from radiation-induced damage with and without a radionecrotic component. METHODS AND MATERIALS: Twenty-four patients with 29 metastases from non-small cell lung cancer or malignant melanoma received SRS with dose of 15 Gy to 25 Gy. Magnetic resonance imaging (MRI) scans were acquired prior to SRS, every 3 months during the first year after SRS, and every 6 months thereafter. On the basis of the follow-up MRI scans or histology after SRS, metastases were classified as having response, tumor progression, or pseudoprogression. Advanced perfusion MRI enabled the estimation of vascular status in tumor regions including fractions of abnormal vessel architecture, underperfused tissue, and vessel pruning. RESULTS: Prior to SRS, metastases that later developed pseudoprogression had a distinct poor vascular function in the peritumoral zone compared with responding metastases (P < .05; number of metastases = 15). In addition, differences were found between the peritumoral zone of pseudoprogressing metastases and normal-appearing brain tissue (P < .05). In contrast, for responding metastases, no differences in vascular status between peritumoral and normal-appearing brain tissue were observed. The dysfunctional peritumoral vasculature persisted in pseudoprogressing metastases after SRS. CONCLUSIONS: Our results suggest that the vascular status of peritumoral tissue prior to SRS plays a defining role in the development of pseudoprogression and that advanced perfusion MRI may provide new insights into patients' susceptibility to radiation-induced effects.

Survival Associations Using Perfusion and Diffusion Magnetic Resonance Imaging in Patients With Histologic and Genetic Defined Diffuse Glioma World Health Organization Grades II and III.

OBJECTIVE: According to the new World Health Organization 2016 classification for tumors of the central nervous system, 1p/19q codeletion defines the genetic hallmark that differentiates oligodendrogliomas from diffuse astrocytomas. The aim of our study was to evaluate whether relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) histogram analysis can stratify survival in adult patients with genetic defined diffuse glioma grades II and III. METHODS: Sixty-seven patients with untreated diffuse gliomas World Health Organization grades II and III and known 1p/19q codeletion status were included retrospectively and analyzed using ADC and rCBV maps based on whole-tumor volume histograms. Overall survival and progression-free survival (PFS) were analyzed by using Kaplan-Meier and Cox survival analyses adjusted for known survival predictors. RESULTS: Significant longer PFS was associated with homogeneous rCBV distribution-higher rCBVpeak (median, 37 vs 26 months; hazard ratio [HR], 3.2; P = 0.02) in patients with astrocytomas, and heterogeneous rCBV distribution-lower rCBVpeak (median, 46 vs 37 months; HR, 5.3; P < 0.001) and higher rCBVmean (median, 44 vs 39 months; HR, 7.9; P = 0.003) in patients with oligodendrogliomas. Apparent diffusion coefficient parameters (ADCpeak, ADCmean) did not stratify PFS and overall survival. CONCLUSIONS: Tumors with heterogeneous perfusion signatures and high average values were associated with longer PFS in patients with oligodendrogliomas. On the contrary, heterogeneous perfusion distribution was associated with poor outcome in patients with diffuse astrocytomas.

Is deep brain involvement in intracranial primary central nervous system lymphoma of importance for penetration of chemotherapeutic agents?

PURPOSE: The purposes of this study are to study the impact of deep brain involvement on overall survival (OS) and progression-free survival (PFS) in intracranial primary CNS lymphoma (PCNSL), and to explore possible mechanisms for this impact using advanced MRI techniques. METHODS: Seventy-nine patients with histologically verified PCNSL were identified from a prospective clinical database of patients treated at Oslo University Hospital between 2003 and 2014. Patients were treated per standard chemotherapeutic regimens (N = 57) or no chemotherapy (N = 22). Anatomical MRIs were available in all patients to assess tumor load and location based on contrast agent enhancement visible on T1-weighted images. Diffusion MRIs were available in 33 (42%) patients and perfusion MRI in 13 (16%) patients that received active treatment. RESULTS: Across all patients, OS and PFS were 16.4 and 9.8 months, respectively. In multivariate analysis, MRI-based deep brain involvement (80%) was the only negative significant factor of OS (OR = 14.2; P < 0.005). While a reduced Karnofsky performance status was associated with deep brain involvement (P < 0.05), neither chemotherapy regimen, neurologic status, nor patient age were independent significant factors for OS or PFS in this setting. Compared to other tumors and healthy tissue levels, MRI perfusion showed more pathologic hemodynamic flow signatures in tumors with deep brain involvement. CONCLUSION: In intracranial PCNSL, the only significant prognostic factor for OS and PFS in multivariate analysis was age and deep brain involvement. While contingent on a small study sample, we hypothesize this may in part be explained by regional differences in vascular supply and delivery from a dysfunctional perfusion signature.

Magnetic resonance imaging provides evidence of glymphatic drainage from human brain to cervical lymph nodes.

Pre-clinical research in rodents provides evidence that the central nervous system (CNS) has functional lymphatic vessels. In-vivo observations in humans, however, are not demonstrated. We here show data on CNS lymphatic drainage to cervical lymph nodes in-vivo by magnetic resonance imaging (MRI) enhanced with an intrathecal contrast agent as a cerebrospinal fluid (CSF) tracer. Standardized MRI of the intracranial compartment and the neck were acquired before and up to 24-48 hours following intrathecal contrast agent administration in 19 individuals. Contrast enhancement was radiologically confirmed by signal changes in CSF nearby inferior frontal gyrus, brain parenchyma of inferior frontal gyrus, parahippocampal gyrus, thalamus and pons, and parenchyma of cervical lymph node, and with sagittal sinus and neck muscle serving as reference tissue for cranial and neck MRI acquisitions, respectively. Time series of changes in signal intensity shows that contrast enhancement within CSF precedes glymphatic enhancement and peaks at 4-6 hours following intrathecal injection. Cervical lymph node enhancement coincides in time with peak glymphatic enhancement, with peak after 24 hours. Our findings provide in-vivo evidence of CSF tracer drainage to cervical lymph nodes in humans. The time course of lymph node enhancement coincided with brain glymphatic enhancement rather than with CSF enhancement.

Non-invasive assessment of pulsatile intracranial pressure with phase-contrast magnetic resonance imaging.

Invasive monitoring of pulsatile intracranial pressure can accurately predict shunt response in patients with idiopathic normal pressure hydrocephalus, but may potentially cause complications such as bleeding and infection. We tested how a proposed surrogate parameter for pulsatile intracranial pressure, the phase-contrast magnetic resonance imaging derived pulse pressure gradient, compared with its invasive counterpart. In 22 patients with suspected idiopathic normal pressure hydrocephalus, preceding invasive intracranial pressure monitoring, and any surgical shunt procedure, we calculated the pulse pressure gradient from phase-contrast magnetic resonance imaging derived cerebrospinal fluid flow velocities obtained at the upper cervical spinal canal using a simplified Navier-Stokes equation. Repeated measurements of the pulse pressure gradient were also undertaken in four healthy controls. Of 17 shunted patients, 16 responded, indicating high proportion of "true" normal pressure hydrocephalus in the patient cohort. However, there was no correlation between the magnetic resonance imaging derived pulse pressure gradient and pulsatile intracranial pressure (R = -.18, P = .43). Pulse pressure gradients were also similar in patients and healthy controls (P = .26), and did not differ between individuals with pulsatile intracranial pressure above or below established thresholds for shunt treatment (P = .97). Assessment of pulse pressure gradient at level C2 was therefore not found feasible to replace invasive monitoring of pulsatile intracranial pressure in selection of patients with idiopathic normal pressure hydrocephalus for surgical shunting. Unlike invasive, overnight monitoring, the pulse pressure gradient from magnetic resonance imaging comprises short-term pressure fluctuations only. Moreover, complexity of cervical cerebrospinal fluid flow and -pulsatility at the upper cervical spinal canal may render the pulse pressure gradient a poor surrogate marker for intracranial pressure pulsations.

Phase-contrast magnetic resonance imaging reveals net retrograde aqueductal flow in idiopathic normal pressure hydrocephalus.

OBJECT The objective of this study was to assess the net aqueductal stroke volume (ASV) and CSF aqueductal flow rate derived from phase-contrast MRI (PC-MRI) in patients with probable idiopathic normal pressure hydrocephalus (iNPH) before and after ventriculoperitoneal shunt surgery, and to compare observations with intracranial pressure (ICP) scores. METHODS PC-MRI at the level of the sylvian aqueduct was undertaken in patients undergoing assessment for probable iNPH. Aqueductal flow in the craniocaudal direction was defined as positive, or antegrade flow, and net ASV was calculated by subtracting retrograde from antegrade aqueductal flow. Aqueductal flow rate per minute was calculated by multiplying net ASV by heart rate. During the same hospital admission, clinical examination was performed using NPH score and overnight continuous ICP monitoring. Twelve patients were followed prospectively 12 months after shunt placement with clinical assessment and a second PC-MRI. The study also included 2 healthy controls. RESULTS Among 21 patients examined for iNPH, 17 (81%) received a shunt (shunt group), and 4 were treated conservatively (conservative group). Among the patients with shunts, a clinical improvement was observed in 16 (94%) of the 17. Net ASV was negative in 16 (76%) of 21 patients before shunt placement and in 5 (42%) of 12 patients after shunt placement, and increased from a median of -5 µl (range -175 to 27 µl) to a median of 1 µl (range -61 to 30 µl; p = 0.04). Among the 12 patients with PC-MRI after shunt placement, 11 were shunt responders, and in 9 of these 11 either a reduced magnitude of retrograde aqueductal flow, or a complete reversal from retrograde to antegrade flow, occurred. Net ASV was significantly lower in the shunt group than in the conservative group (p = 0.01). The aqueductal flow rate increased from -0.56 ml/min (range -12.78 to 0.58 ml/min) to 0.06 ml/min (range -4.51 to 1.93 ml/min; p = 0.04) after shunt placement. CONCLUSIONS In this cohort of patients with iNPH, retrograde net aqueductal flow was observed in 16 (76%) of 21 patients. It was reversed toward the antegrade direction after shunt placement either by magnitude or completely in 9 (75%) of 12 patients examined using PC-MRI both before and after shunt placement (p = 0.04); 11 of the 12 were shunt responders. The study results question previously established concepts with respect to both CSF circulation pathways and CSF formation rate.

Age-related cortical thickness differences in adolescents with early-onset schizophrenia compared with healthy adolescents.

The aim of the present study was to investigate the influence of age on cerebral cortical thickness in adolescents with early-onset schizophrenia (EOS) (n=22, aged 12-18 years), as compared to an age-matched healthy control group (n=32). All participants were scanned with magnetic resonance imaging. Whereas in the healthy control group there was a negative association between increasing age and cortical thickness measures in widespread brain regions, including frontal and parietal cortices, the patient group showed no significant effects of age when the groups were studied separately. There was a trend towards an age-by-group effect in the left supramarginal gyrus and the right pre- and postcentral gyri. The between-group statistical analysis indicated similar cortical thickness in the patients as in the healthy controls. There were no significant effects of medication on cortical thickness, nor was there any significant sex-by-group interaction. The results suggest that patients with EOS have a deficiency of the expected cortical thinning to occur during adolescence development. The findings are discussed in context of neurobiological processes known to be involved in brain maturation, including synaptic reorganization, pruning and myelination.

Assessment of pituitary adenoma volumetric change using longitudinal MR image registration.

INTRODUCTION: Change detection is a crucial factor in monitoring of slowly evolving pathologies. The objective of the study was to test a semi-automatic method applied on longitudinal MRI monitoring of volume change in pituitary macroadenomas. METHODS: The proposed method is based on a visual comparison of geometrically corrected, co-registered, intensity-normalized contrast-enhanced (CE) 3D GRE T1-weighted images. Qualitative volume changes based on this applied method were compared with experts' readings of conventional pre- and post-CE 2D T1-weighted images. Magnetic resonance (MR) imaging was performed two to four times in 13 patients with a total combination of 29 time points. RESULTS: Compared to conventional 2D MR readings, a diagnosis of tumor growth (yes/no) was changed in 5 of 13 patients (38%) at 9 of the 29 combinations of time points (31%) using the 3D-based semi-automatic method. With manual tumor tracings as reference, McNemar's test showed a significant difference between the two methods. CONCLUSION: Visual comparison of geometrically corrected, intensity-normalized, and affine-aligned longitudinal 3D images may enable more accurate assessment of qualitative volumetric change in pituitary adenomas than conventional reading of 2D images.

Prefrontal hyperactivation during a working memory task in early-onset schizophrenia spectrum disorders: an fMRI study.

Working memory (WM) dysfunction is increasingly recognized as a core feature of schizophrenia, but few studies have investigated prefrontal activation during WM tasks in early-onset schizophrenia spectrum disorder (EOS). Our aim was to explore prefrontal activation during a WM-task in EOS patients compared to healthy controls using functional magnetic resonance imaging (fMRI). Fifteen patients with EOS and 15 matched healthy controls performed a 0-back and a 2-back task while fMRI data were acquired. Results indicated that even though performance between patients and controls was comparable on both tasks, there was a hyperactivation in patients' ventrolateral prefrontal cortex (VLPFC) during the 2-back task compared to healthy controls. This pattern of activation suggests that, in patients with EOS, the VLPFC compensated in order to match performance of the controls. The activations in the EOS group may reflect the use of a compensatory, cognitive strategy while solving WM-tasks.