With advances in cancer treatments, long-term impairments of survivors have become more apparent. Radiotherapy of tumors in or near the brain can potentially induce cognitive impairments, impacting the quality of life of survivors. Currently, there is a lack of comprehensive information on the timeline of cognitive impairments following radiotherapy for head and neck cancer (HNC). To address this gap, we conducted a literature review on cognitive impairments observed after radiotherapy for HNC. The literature review was conducted using PubMed, Web of Science, PsycINFO, and Google Scholar. Search terms included the following keywords: head and neck tumors, radiotherapy, treatment responses, cognitive impairments, as well as variants and related subcategories. Our review encompassed 23 studies involving a total of 1059 HNC patients, predominantly nasopharyngeal carcinoma. Overall, studies indicated a decline in cognitive performance post-radiotherapy compared to baseline scores, control groups, or normative data. The literature on acute effects is scarce and studies with complete neuropsychological assessments are missing. Cognitive impairments were prevalent in the majority of patients at six to 12 months post-radiotherapy, with memory deficits being the most prominent. Long-term assessments demonstrated that these cognitive deficits persisted even beyond seven years, suggesting a potentially irreversible decline in cognition following radiotherapy. Cognitive impairments are frequently observed at least six months after radiotherapy. Standardized cognitive assessments are imperative to evaluate impairments in individual patients. Future research in HNC should integrate neuropsychological evaluations to enhance our understanding of domain-specific impairments and the complete timeline of cognitive changes after radiotherapy.

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans.

Background: Pseudoprogression is known to occur after immune-checkpoint inhibitor (ICI) therapy in brain metastasis and can complicate clinical decision-making. Still, its incidence, timing, and clinical presentation remain unclear. A retrospective cohort study in melanoma and non-small cell lung cancer brain metastasis patients was conducted to address this. Materials and Methods: Brain metastasis patients showing progression on MRI according to response assessment in neuro-oncology brain metastases criteria after starting ICI therapy were included, irrespective of prior irradiation. Lesions were classified as tumour progression (TP) or pseudoprogression based on three-month radiological follow-up or histopathology. TP was assigned if progression was again shown at three months. Pseudoprogression was assigned if lesions showed stability, partial, or complete response at three months. ‘Non-classified’ lesions were those with new or changed treatment during follow-up. Results: A cohort of 98 patients with 233 lesions was included over a 13-year period; 170 lesions were considered non-classified, and 41 and 22 lesions were classified as TP and pseudoprogression respectively. This resulted in a lesion- and patient-specific incidence for pseudoprogression of 9.4% and 17.3% respectively. Due to the large number of lesions that could not be classified, as is the case in clinical practice, the reported incidence in this study is likely an underestimation and can be seen as a ‘minimum’ incidence rate. Ten pseudoprogression (45.5%) and 13 (31.7%) TP lesions were previously irradiated. Pseudoprogression occurred at a median of 2.7 months after starting ICI therapy. The only clinical feature distinguishing patients with TP from pseudoprogression was that TP patients were more likely to need dexamethasone for neurological symptoms. Conclusions: Pseudoprogression has a lesion-specific incidence rate of at least 9.4% and occurs at a median of 2.7 months after starting ICI therapy. Severe neurological symptoms requiring dexamethasone may be a clinical feature typical for TP.

Purpose: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular space (PVS), following the 2021 EPTN Neuro-Oncology Atlas guidelines on T1-weighted contrast-enhanced MRI scans (T1CE). The performance of this DL model was quantitatively and qualitatively compared with an off-the-shelf model. Materials and Methods: An nnU-Net was trained for ventricle segmentation using both CT and T1CE MRI images from 78 patients. Its performance was compared to that of a publicly available pretrained segmentation model, SynthSeg. The evaluation was conducted on both internal (N = 18) and external (n = 18) test sets, with each consisting of paired CT and T1CE MRI images and expert-delineated ground truths (GTs). Segmentation accuracy was assessed using the volumetric Dice Similarity Coefficient (DSC), 95th percentile Hausdorff distance (HD95), surface DSC, and added path length (APL). Additionally, a local evaluation of ventricle segmentations quantified differences between manual and automatic segmentations across both test sets. All segmentations were scored by radiotherapy technicians for clinical acceptability using a 4-point Likert scale. Results: The nnU-Net significantly outperformed the SynthSeg model on the internal test dataset in terms of median [range] DSC, 0.93 [0.86–0.95] vs. 0.85 [0.67–0.91], HD95, 0.9 [0.7–2.5] mm vs. 2.2 [1.7–4.8] mm, surface DSC, 0.97 [0.90–0.98] vs. 0.84 [0.70–0.89], and APL, 876 [407–1298] mm vs. 2809 [2311–3622] mm, all with p < 0.001. No significant differences in these metrics were found in the external test set. However clinical ratings favored nnU-Net segmentations on the internal and external test sets. In addition, the nnU-Net had higher clinical ratings than the GT delineation on the internal and external test set. Conclusions: The nnU-Net model outperformed the SynthSeg model on the internal dataset in both segmentation metrics and clinician ratings. While segmentation metrics showed no significant differences between the models on the external set, clinician ratings favored nnU-Net, suggesting enhanced clinical acceptability. This suggests that nnU-Net could contribute to more time-efficient and streamlined radiotherapy planning workflows.

Background: Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown. Therefore, this study assesses the validity and agreement of these models. Methods: Fourteen post-treatment LGG patients and six healthy controls (HC) underwent DWI MRI on a 3T MRI scanner. DWI processing included diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), white matter tract integrity (WMTI), neurite orientation dispersion and density imaging (NODDI), and fixel-based analysis (FBA). Validity was assessed by delineating surgical cavity, peri-surgical cavity, and normal-appearing white matter (NAWM) in LGG patients, and white matter (WM) in HC. Spearman correlation assessed the agreement between DWI parameters. Results: All obtained parameters differed significantly across tissue types. Remarkably, WMTI showed that intra-axonal diffusivity was high in the surgical cavity and low in NAWM and WM. Most DWI parameters correlated well with each other, except for WMTI-derived intra-axonal diffusivity. Conclusion: This study shows that all parameters relevant for tumour monitoring and DWI-derived parameters for axonal fibre-bundle integrity (except WMTI-IAS-D_a) could be used interchangeably, enhancing inter-DWI model interpretability.