Rudi A. J. O. Dierckx

Coronary artery calcification (CAC) entails arterial wall hardening and lipid-rich plaques accumulation and may contribute to early cognitive decline. Its association with cognition, brain structure and function, Alzheimer’s disease, neurodegeneration, and inflammatory and lipid biomarkers remains unclear. We conducted a cross-sectional study of 278 cognitively unimpaired adults aged ≥ 50 years with absent (score = 0; n = 151) or high CAC (score ≥ 300; n = 127). Participants underwent cognitive testing, serum assays (Aβ42/40, pTau181, NfL, GFAP, inflammatory/lipid markers), and multimodal MRI (structural, resting-state and vessel encoded arterial spin labeling). High CAC was associated with lower general cognition, increased white matter hyperintensities, altered hippocampal and sensorimotor connectivity, and reduced perfusion in the inferior frontal gyrus pars opercularis. No group differences emerged in the serum biomarkers. These results reveal that high CAC is associated with cognitive and cerebrovascular changes independent of Alzheimer’s disease biomarker increase, suggesting vascular pathways in early brain aging.

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.

Purpose: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias. Methods: We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET scans. The center difference is learned by applying IRMA on PCA-based feature vectors of healthy controls (HC), resulting in a subspace V, representing information not comparable between centers, and the remaining subspace U, where no center differences are present. In this proof-of-concept study, we demonstrate the properties of the method using data from four centers. After center-harmonization, a Generalized Matrix Learning Vector Quantization (GMLVQ) model was trained to discriminate between Parkinson’s disease, Alzheimer’s disease and Dementia with Lewy Bodies. Results: At the initial IRMA iteration, the system was able to determine the center origin of the four HC cohorts almost perfectly. The method required six iterations, corresponding to a six-dimensional subspace V, to determine the entire center difference. An uncorrected disease classification model was highly biased to center-specific effects, creating a falsely inflated performance when applying internal (cross-) validation. The cross-validation performance of the center-harmonized model remained high, while it generalized significantly better to unseen test cohorts. Furthermore, the framework is highly transparent, providing analytic reconstructions of the correction and visualizations of the data in voxel space. Conclusion: IRMA can be used to learn and disregard center-specific information in features extracted from brain ¹⁸F-FDG PET scans, while retaining disease-specific information.

The latest technical development in the field of positron emission tomography/computed tomography (PET/CT) imaging has been the extension of the PET axial field-of-view. As a result of the increased number of detectors, the long axial field-of-view (LAFOV) PET systems are not only characterized by a larger anatomical coverage but also by a substantially improved sensitivity, compared with conventional short axial field-of-view PET systems. In clinical practice, this innovation has led to the following optimization: (1) improved overall image quality, (2) decreased duration of PET examinations, (3) decreased amount of radioactivity administered to the patient, or (4) a combination of any of the above. In this review, novel applications of LAFOV PET in oncology are highlighted and future directions are discussed.

MRI is the gold standard for treatment response assessments for glioblastoma. However, there is no consensus regarding the optimal interval for MRI follow-up during standard treatment. Moreover, a reliable assessment of treatment response is hindered by the occurrence of pseudoprogression. It is unknown if a radiological follow-up strategy at 2–3 month intervals actually benefits patients and how it influences clinical decision making about the continuation or discontinuation of treatment. This study assessed the consequences of scheduled follow-up scans post-chemoradiotherapy (post-CCRT), after three cycles of adjuvant chemotherapy [TMZ3/6], and after the completion of treatment [TMZ6/6]), and of unscheduled scans on treatment decisions during standard concomitant and adjuvant treatment in glioblastoma patients. Additionally, we evaluated how often follow-up scans resulted in diagnostic uncertainty (tumor progression versus pseudoprogression), and whether perfusion MRI improved clinical decision making. Scheduled follow-up scans during standard treatment in glioblastoma patients rarely resulted in an early termination of treatment (2.3% post-CCRT, 3.2% TMZ3/6, and 7.8% TMZ6/6), but introduced diagnostic uncertainty in 27.7% of cases. Unscheduled scans resulted in more major treatment consequences (30%; p < 0.001). Perfusion MRI caused less diagnostic uncertainty (p = 0.021) but did not influence treatment consequences (p = 0.871). This study does not support the current pragmatic follow-up strategy and suggests a more tailored follow-up approach.

The molecular imaging of biomarkers plays an increasing role in medical diagnostics. In particular, the imaging of enzyme activity is a promising approach, as it enables the use of its inherent catalytic activity for the amplification of an imaging signal. The increased activity of a sulfatase enzyme has been observed in several types of cancers. We describe the development and in vitro evaluation of molecular imaging agents that allow for the detection of sulfatase activity using the whole-body, non-invasive MRI and CEST imaging methods. This approach relies on a responsive ligand that features a sulfate ester moiety, which upon sulfatase-catalyzed hydrolysis undergoes an elimination process that changes the functional group, coordinating with the metal ion. When Gd³⁺ is used as the metal, the complex can be used for MRI, showing a 25% decrease at 0.23T and a 42% decrease at 4.7T in magnetic relaxivity after enzymatic conversion, thus providing a “switch-off” contrast agent. Conversely, the use of Yb³⁺ as the metal leads to a “switch-on” effect in the CEST imaging of sulfatase activity. Altogether, the results presented here provide a molecular basis and a proof-of-principle for the magnetic imaging of the activity of a key cancer biomarker.

MRI is the gold standard for treatment response assessment in glioblastoma. However, there is no consensus regarding the optimal interval for MRI follow-up during standard treatment. Moreover, reliable assessment of treatment response is hindered by the occurrence of pseudoprogression. It is unknown if a radiological follow-up strategy with 2-3 months intervals actually benefits patients and how it influences clinical decision making about continuation or discontinuation of treatment. This study assessed the consequences of scheduled follow-up scans (post-chemoradiotherapy [post-CCRT], after 3 cycles of adjuvant chemotherapy [TMZ3/6], after completion of treatment [TMZ6/6]) and unscheduled scans on treatment decisions during standard concomitant and adjuvant treatment in glioblastoma patients. Additionally, we evaluated how often follow-up scans resulted in diagnostic uncertainty (tumor progression versus pseudoprogression), and whether perfusion MRI improved clinical decision making. Scheduled follow-up scans during standard treatment in glioblastoma patients rarely resulted in early termination of treatment (2.3% post-CCRT, 3.2% TMZ3/6, and 7.8% TMZ6/6), but introduced diagnostic uncertainty in 27.7% of cases. Unscheduled scans resulted in more major treatment consequences (30%; p<0.001). Perfusion MRI caused less diagnostic uncertainty (p=0.021) but did not influence treatment consequences (p=0.871). This study does not support the current pragmatic follow-up strategy and suggests a more tailored follow-up approach.

Isocitrate dehydrogenase (IDH) mutation status is an important biomarker in the glioma-defining subtype and corresponding prognosis. This study proposes a straightforward method for 2-hydroxyglutarate (2-HG) quantification by MR spectroscopy for IDH mutation status detection and directly compares in vivo 2-HG MR spectroscopy with ex vivo 2-HG concentration measured in resected tumor tissue. Eleven patients with suspected lower-grade glioma (ten IDH1; one IDHwt) were prospectively included. Preoperatively, 3T point-resolved spectroscopy (PRESS) was acquired; 2-HG was measured as the percentage elevation of Glx3 (the sum of 2-HG and Glx) compared to Glx4. IDH mutation status was assessed by immunochemistry or direct sequencing. The ex vivo 2-HG concentration was determined in surgically obtained tissue specimens using gas chromatography–mass spectrometry. Pearson correlation was used for assessing the correlation between in vivo MR spectroscopy and ex vivo 2-HG concentration. MR spectroscopy was positive for 2-HG in eight patients, all of whom had IDH1 tumors. A strong correlation (r = 0.80, p = 0.003) between 2-HG MR spectroscopy and the ex vivo 2-HG concentration was found. This study shows in vivo 2-HG MR spectroscopy can non-invasively determine IDH status in glioma and demonstrates a strong correlation with ex vivo 2-HG concentration in patients with lower-grade glioma.

The aim of this study was to evaluate whether radiologically defined sarcopenia, or a low skeletal muscle index (SMI), could be used as a practical biomarker for frailty and postoperative complications (POC) in patients with head and neck skin cancer (HNSC). This was a retrospective study on prospectively collected data. The L3 SMI (cm²/m²) was calculated with use of baseline CT or MRI neck scans and low SMIs were defined using sex-specific cut-off values. A geriatric assessment with a broad range of validated tools was performed at baseline. POC was graded with the Clavien–Dindo Classification (with a grade of > II as the cut-off). Univariate and multivariable regression analyses were performed with low SMIs and POC as the endpoints. The patients’ (n = 57) mean age was 77.0 ± 9 years, 68.4% were male, and 50.9% had stage III–IV cancer. Frailty was determined according to Geriatric 8 (G8) score (OR 7.68, 95% CI 1.19–49.66, p = 0.032) and the risk of malnutrition was determined according to the Malnutrition Universal Screening Tool (OR 9.55, 95% CI 1.19–76.94, p = 0.034), and these were independently related to low SMIs. Frailty based on G8 score (OR 5.42, 95% CI 1.25–23.49, p = 0.024) was the only variable related to POC. However, POC was more prevalent in patients with low SMIs (∆ 19%, OR 1.8, 95% CI 0.5–6.0, p = 0.356).To conclude, a low SMI is a practical biomarker for frailty and malnutrition in HNSC. Future research should be focused on interventions based on low SMI scores and assess the effect of the intervention on SMI, frailty, malnutrition, and POC.