CO2 as an engine for neurofluid flow: Exploring the coupling between vascular reactivity, brain clearance, and changes in tissue properties.

The brain relies on an effective clearance mechanism to remove metabolic waste products for the maintenance of homeostasis. Recent studies have focused on elucidating the forces that drive the motion of cerebrospinal fluid (CSF), responsible for removal of these waste products. We demonstrate that vascular responses evoked using controlled manipulations of partial pressure of carbon dioxide (PaCO2) levels, serve as an endogenous driver of CSF clearance from the brain. To demonstrate this, we retrospectively surveyed our database, which consists of brain metastases patients from whom blood oxygen level-dependent (BOLD) images were acquired during targeted hypercapnic and hyperoxic respiratory challenges. We observed a correlation between CSF inflow signal around the fourth ventricle and CO2-induced changes in cerebral blood volume. By contrast, no inflow signal was observed in response to the nonvasoactive hyperoxic stimulus, validating our measurements. Moreover, our results establish a link between the rate of the hemodynamic response (to elevated PaCO2) and peritumoral edema load, which we suspect may affect CSF flow, consequently having implications for brain clearance. Our expanded perspective on the factors involved in neurofluid flow underscores the importance of considering both cerebrovascular responses, as well as the brain mechanical properties, when evaluating CSF dynamics in the context of disease processes.

Detectability and intra-fraction motion of individual elective lymph nodes in head and neck cancer patients on the Magnetic Resonance Image guided linear accelerator.

Background and purpose: Individual elective lymph node irradiation instead of elective neck irradiation is a new concept for head-and-neck cancer (HNC) patients developed for the Magnetic Resonance Image guided linear accelerator (MR-linac). To prepare this, the detectability, volume changes and intra-fraction motion of elective lymph nodes on the MR-linac was assessed. Materials and methods: A total of 15 HNC patients underwent diagnostic pre-treatment MRI. Additionally, two MR-linac scans were obtained with a 10-minute time difference in the first week of radiation treatment. Elective lymph node contours inside lymph node levels (Ib-V) were segmented on the pre-treatment MRI and the MR-linac scans and compared on number and maximal transversal diameter. Intra-fraction motion of elective lymph nodes on the MR-linac was estimated using Center of Mass (COM) distances and incremental isotropic expansion of lymph node segmentations. Results: Of all 679 detected lymph nodes on the pre-treatment MRI, eight lymph nodes were not detectable on the first MR-linac scan and 16 new lymph nodes were detected. Lymph node diameters between the pre-treatment MRI scan and the MR-linac scan varied from -0.19 to + 0.13 mm. COM distances varied from 1.2 to 1.7 mm and lymph node contours had to be expanded with 3 mm. Conclusions: Nearly all elective lymph nodes were detectable on the 1.5T MR-linac scan with no major changes in target volumes compared to the pre-treatment MRI. Simulated intra-fraction motion during the MR-linac scans was smaller than the 5-mm margin that will be used in the first elective lymph node radiation treatment.

Individualized trajectories in postradiotherapy neurocognitive functioning of patients with brain metastases.

Background: The increasing incidence of brain metastases (BMs) and improved survival rates underscore the necessity to investigate the effects of treatments on individuals. The aim of this study was to evaluate the individual trajectories of subjective and objective cognitive performance after radiotherapy in patients with BMs. Methods: The study population consisted of adult patients with BMs referred for radiotherapy. A semi-structured interview and comprehensive neurocognitive assessment (NCA) were used to assess both subjective and objective cognitive performance before, 3 months and ≥ 11 months after radiotherapy. Reliable change indices were used to identify individual, clinically meaningful changes. Results: Thirty-six patients completed the 3-month follow-up, and 14 patients completed the ≥ 11-months follow-up. Depending on the domain, subjective cognitive decline was reported by 11-22% of patients. In total, 50% of patients reported subjective decline in at least one cognitive domain. Intracranial progression 3 months postradiotherapy was a risk-factor for self-reported deterioration (P = .031). Objective changes were observed across all domains, with a particular vulnerability for decline in memory at 3 months postradiotherapy. The majority of patients (81%) experienced both a deterioration as well as improvement (eg, mixed response) in objective cognitive functioning. Results were similar for the long-term follow-up (3 to ≥11 months). No risk factors for objective cognitive change 3 months postradiotherapy were identified. Conclusions: Our study revealed that the majority of patients with BMs will show a mixed cognitive response following radiotherapy, reflecting the complex impact. This underscores the importance of patient-tailored NCAs 3 months postradiotherapy to guide optimal rehabilitation strategies.

Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study.

In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.

Improved delineation with diffusion weighted imaging for laryngeal and hypopharyngeal tumors validated with pathology.

OBJECTIVE: This study aims to determine the added value of a geometrically accurate diffusion-weighted (DW-) MRI sequence on the accuracy of gross tumor volume (GTV) delineations, using pathological tumor delineations as a ground truth. METHODS: Sixteen patients with laryngeal or hypopharyngeal carcinoma were included. After total laryngectomy, the specimen was cut into slices. Photographs of these slices were stacked to create a 3D digital specimen reconstruction, which was registered to the in vivo imaging. The pathological tumor (tumorHE) was delineated on the specimen reconstruction. Six observers delineated all tumors twice: once with only anatomical MR imaging, and once (a few weeks later) when DW sequences were also provided. The majority voting delineation of session one (GTVMRI) and session two (GTVDW-MRI), as well as the clinical target volumes (CTVs), were compared to the tumorHE. RESULTS: The mean tumorHE volume was 11.1 cm3, compared to a mean GTVMRI volume of 18.5 cm3 and a mean GTVDW-MRI volume of 15.7 cm3. The median sensitivity (tumor coverage) was comparable between sessions: 0.93 (range: 0.61-0.99) for the GTVMRI and 0.91 (range: 0.53-1.00) for the GTVDW-MRI. The CTV volume also decreased when DWI was available, with a mean CTVMR of 47.1 cm3 and a mean CTVDW-MRI of 41.4 cm3. Complete tumor coverage was achieved in 15 and 14 tumors, respectively. CONCLUSION: GTV delineations based on anatomical MR imaging tend to overestimate the tumor volume. The availability of the geometrically accurate DW sequence reduces the GTV overestimation and thereby CTV volumes, while maintaining acceptable tumor coverage.