Aida Niñerola-Baizán

Meningioma is the most common benign primary intracranial tumor. Although its typical appearance on contrast-enhanced T1-weighted images is characteristic, tumor conspicuity and boundary definition vary substantially across MRI sequences, which complicates automated detection and delineation. Automatic meningioma detection and segmentation provide the volumetric delineation on which tumor volume estimation, radiotherapy target definition, and radiomic analyses depend and may reduce the time and inter-observer variability associated with manual delineation. We investigated deep learning models performance in automated detection and segmentation by using different combinations of routine MRI sequences. We retrospectively analyzed 149 patients with histologically diagnosed meningioma. The imaging protocol included T1-weighted (T1), contrast-enhanced T1-weighted (T1-CE), T2-weighted (T2), T2 gradient echo-weighted (T2-GE), fluid-attenuated inversion recovery (FLAIR) sequences, and apparent diffusion coefficient (ADC). A maximum of 63 combinations were used to feed an Attention U-Net, and models’ outputs were compared with manual segmentation by an experienced neuroradiologist to assess the detection rate (DR) and segmentation performance. Among single-sequence models, T1-CE (DR: 0.79 ± 0.11, DICE: 0.67 ± 0.10) showed the best performance. Models trained on multiple MRI sequences (up to 3) consistently improved those trained on single sequences. T1-CE + T1 + FLAIR model achieved best lesion detection (DR: 0.98 ± 0.03) and segmentation (DICE: 0.82 ± 0.04). By combining T1-CE with up to two additional sequences, the algorithm developed provides high efficiency and accuracy in automatic meningioma detection and segmentation. This work suggests that, when optimized, multi-sequence models could improve the performance of single-sequence models, which may facilitate future development of automated workflows for volumetric meningioma assessment.

Alterations in sleep–wake patterns are well-recognized features of overt alpha-synucleinopathies. However, their presence in prodromal stages, characterized by isolated Rapid-Eye-Movement (REM) sleep behavior disorder, remains controversial, and their association with clinical symptoms and imaging risk factors has been underexplored. In this study, we used seven-day ambulatory circadian monitoring to characterize circadian and sleep–wake disturbances in a well-characterized sample of forty-two patients with video-polysomnography-confirmed isolated REM sleep behavior disorder and 23 healthy controls. We investigated the associations between circadian disturbances and non-motor clinical symptoms, assessed in a comprehensive neuropsychological protocol, alongside imaging proxies of glymphatic system functioning, namely the Diffusion Tensor Imaging Along the Perivascular Spaces index, and the volume of perivascular spaces and choroid plexuses. Moreover, we assessed the associations between circadian alterations and striatal dopaminergic uptake in the most affected putamen, a well-established risk factor for conversion to alpha-synucleinopathies, as assessed with presynaptic dopaminergic imaging. Patients with isolated REM sleep behavior disorder exhibited reduced diurnal physical activity, along with reduced amplitude and increased fragmentation of the sleep probability and motor activity rhythms. Circadian alterations correlated with greater neuropsychiatric symptoms and reduced psychomotor and mental speed. Patients with isolated REM sleep behavior disorder exhibited a decreased Diffusion Tensor Imaging Along the Perivascular Spaces and larger volume of perivascular spaces, but only the former was associated with sleep impairment. Decreased putaminal dopaminergic uptake was associated with rest-activity rhythm fragmentation. These results show that circadian alterations are observed in early stages of neurodegeneration and are associated with clinical and imaging risk markers of conversion to alpha-synucleinopathies, posing them as possible targets for intervention in prodromal disease stages.

Background: Assessment of the peritoneal cancer burden is crucial for determining the optimal treatment in advanced ovarian cancer (AOC). Effective non-invasive methods to predict tumour load remain limited. This study aimed to assess the applicability of 2-[¹⁸F]FDG PET/CT volumetric parameters, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) for predicting the surgical peritoneal cancer index (PCI) in AOC before primary treatment. Methods: Patients with high-grade serous or undifferentiated AOC who underwent surgical PCI evaluation and 2-[¹⁸F]FDG PET/CT between 01/2013 and 12/2018 were included. MTV and TLG were calculated using thresholds of 40% and 50% (MTV40, MTV50, TLG40, and TLG50). Correlations between the peritoneal carcinomatosis MTV (car_MTV) and TLG (car_TLG) were analysed. The capacity of volumetric parameters to estimate PCIs above or below 14 and 20 was assessed for the whole abdominal cavity and in per-quadrant analysis, specifically for upper-abdomen areas 1, 2, and 3 (MTV40_1, 2, 3 and TLG40_1, 2, 3). Results: MTV40, MTV50, TLG40, and TLG50 significantly correlated with the PCI in the final study population (n = 45). MTV40 showed a Pearson coefficient of 0.41 (p = 0.003). MTV3_40 (AUC 0.79) and TLG3_40 (AUC 0.81) presented the highest AUCs for predicting a PCI above or below 14. The volumetric parameters allowed the prediction of a PCI greater or less than 20, with an AUC of 0.77 for MTV40_1 and 0.78 for TLG40_1. Conclusions: 2-[¹⁸F]FDG PET/CT MTV and TLG correlate significantly with the surgical PCI when assessing peritoneal carcinomatosis or quadrant-specific disease. This approach offers a reliable non-invasive method for evaluating tumour burden in AOC.

Purpose: To investigate the impact of reduced injected doses on the quantitative and qualitative assessment of the amyloid PET tracers [¹⁸F]flutemetamol and [¹⁸F]florbetaben. Methods: Cognitively impaired and unimpaired individuals (N = 250, 36% Aβ-positive) were included and injected with [¹⁸F]flutemetamol (N = 175) or [¹⁸F]florbetaben (N = 75). PET scans were acquired in list-mode (90–110 min post-injection) and reduced-dose images were simulated to generate images of 75, 50, 25, 12.5 and 5% of the original injected dose. Images were reconstructed using vendor-provided reconstruction tools and visually assessed for Aβ-pathology. SUVRs were calculated for a global cortical and three smaller regions using a cerebellar cortex reference tissue, and Centiloid was computed. Absolute and percentage differences in SUVR and CL were calculated between dose levels, and the ability to discriminate between Aβ- and Aβ + scans was evaluated using ROC analyses. Finally, intra-reader agreement between the reduced dose and 100% images was evaluated. Results: At 5% injected dose, change in SUVR was 3.72% and 3.12%, with absolute change in Centiloid 3.35CL and 4.62CL, for [¹⁸F]flutemetamol and [¹⁸F]florbetaben, respectively. At 12.5% injected dose, percentage change in SUVR and absolute change in Centiloid were < 1.5%. AUCs for discriminating Aβ- from Aβ + scans were high (AUC ≥ 0.94) across dose levels, and visual assessment showed intra-reader agreement of > 80% for both tracers. Conclusion: This proof-of-concept study showed that for both [¹⁸F]flutemetamol and [¹⁸F]florbetaben, adequate quantitative and qualitative assessments can be obtained at 12.5% of the original injected dose. However, decisions to reduce the injected dose should be made considering the specific clinical or research circumstances.

Objectives Accurate assessment of disease extent is required to select the best primary treatment for advanced epithelial ovarian cancer patients. Estimation of tumour burden is challenging and it is usually performed by means of a surgical procedure. Imaging techniques and tumour markers can help to estimate tumour burden non-invasively. 2-[¹⁸F]FDG PET/CT allows the evaluation of the whole-body disease. This study aimed to correlate HE4 and CA125 serum concentrations with tumour burden evaluated by volumetric 2-[¹⁸F]FDG PET/CT parameters in advanced high-grade epithelial ovarian cancer. Methods We included 66 patients who underwent 2-[¹⁸F]FDG PET/CT and serum tumour markers determination before primary treatment. Volumes of interest were delimited in every pathological uptake. Whole-body metabolic tumour volume (wb_MTV) and total lesion glycolysis (wb_TLG) were calculated summing up every VOI’s MTV value. SUVmax thresholds were set at 40% (MTV40 and TLG40) and 50% (MTV50 and TLG50). In addition, four VOI subgroups were defined: peritoneal carcinomatosis, retroperitoneal nodes, supradiaphragmatic nodes, and distant metastases. MTV and TLG were calculated for each group by adding up the corresponding MTV values. TLG was calculated likewise. Results wb_MTV and wb_TLG were found to be significantly correlated with serum CA125 and HE4 concentrations. The strongest correlation was observed between HE4 and wb_MTV40 (r = 0.62, p < 0.001). Pearson’s correlation coefficients between peritoneal carcinomatosis MTV40 and tumour markers were 0.61 (p < 0.0001) and 0.29 (p = 0.02) for HE4 and CA125 respectively. None of these tumour markers showed a positive correlation with tumour load outside the abdominal cavity assessed by volumetric parameters. Conclusion HE4 performs better than CA125 to predict metabolic tumour burden in high-grade epithelial ovarian cancer before primary treatment. 2-[18F]FDG PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution. These results support the usefulness of HE4 and PET/CT to improve the stratification of these patients in clinical practice. Key Points • In patients with high-grade advanced ovarian epithelial carcinoma, both CA125 and HE4 correlate to whole-body tumour burden assessed by PET/CT before primary treatment. • HE4 estimates peritoneal disease much better than CA125. • PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution.

Background: Subjective Cognitive Decline (SCD) is a risk factor for cognitive decline and increases the likelihood of abnormal Alzheimer’s disease (AD) biomarkers. A set of SCD features (SCDplus) has been proposed to be more strongly related with AD pathology, like onset within 5 years. Limited evidence suggest that there is an association between recent SCD onset and amyloid‐β (Aβ) levels but longitudinal studies exploring this association are lacking. Our objective was to explore the association between incidence and stability of SCD and biomarkers of AD pathology, synaptic dysfunction and neurodegeneration.Method: We analyzed clinical, neuroimaging and cerebrospinal fluid (CSF)‐biomarkers data from 261 cognitively unimpaired individuals from the ALFA+ study [mean age 57(5) at baseline]. SCD status was recorded at baseline and follow‐up [mean interval 48.5(8.6) months, 80% before 60 months] using the SCD‐Q question: Do you perceive memory or cognitive difficulties? Individuals were classified as Stable‐SCD [SCD at both visits (n=28)], Incident‐SCD [SCD only at follow‐up (n=44)], Unstable‐SCD [SCD only at baseline (n=21)], and non‐SCD [no SCD at any visit (n=168)]. All remained cognitively unimpaired at follow‐up. We defined amyloid status by visual reads (Aβ+/Aβ‐) from [¹⁸F]flutemetamol PET scans. Aβ40, Aβ42, p‐tau, t‐tau, neurogranin and Neurofilament Light (NfL) were measured with Roche NeuroToolKit and Elecsys® immunoassays (Roche Diagnostics). Logistic regression models were used to predict Aβ status and ANCOVA models to explore differences in biomarkers, adjusting for age, sex and interval between visits.Result: The proportion of Aβ+ individuals was higher in Incident‐SCD as compared to non‐SCD (OR=2.5, p=0.04, Table 1). Further, Incident‐SCD group displayed lower Aβ42/Aβ40 than non‐SCD (p=0.02, partial Eta²=0.02). While Stable‐SCD did not show differences in amyloid measures as compared to controls, NfL was significantly increased (p2=0.08) and t‐tau approached significance (p=0.08).Conclusion: Recent SCD onset was associated with amyloid‐β pathology, while stable SCD, even without clinical progression, was associated to biomarkers of neurodegeneration but not to amyloid pathology. These results support the usefulness of the onset in the last 5 years as SCDplus feature to detect amyloid pathology, while also highlights the role of neurodegeneration in SCD maintenance.