This is a repository copy of A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Background Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. MethodsIn this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. Findings The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0•818 (95% CI 0•817-0•820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6•31 cm³ (5•60 to 7•14), thereby underestimating the true tumour volume by a median of -0•48 cm³ (-0•37 to -0•76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true postcontrast T1-weighted sequences (0•782, 0•751-0•807, p<0•0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4•2 months (95% CI 4•1-5•2) with synthetic post-contrast T1-weighted and 4•3 months (4•1-5•5) with true post-contrast T1-weighted sequences (p=0•33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1•749, 95% CI 1•282-2•387, p=0•0004) and model C-index (0•6...

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

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Background In patients with recurrent glioblastoma corticosteroids are frequently used to mitigate intracranial pressure and to improve patient neurological functioning. To date, in these patients no systematic studies have been performed to assess neurocognitive functioning (NCF) in relation to corticosteroid treatment. Methods Using baseline data (i.e., prior to randomization) of EORTC trial 26101 we performed regression analysis to assess the predictive value of corticosteroids intake on performance of the EORTC brain tumor clinical trial NCF test battery. The battery is comprised of the Hopkins Verbal Learning Test-Revised (HVLT-R), Controlled Oral Word Association test (COWA) and Trail Making Test (A and B). Results Out of 321 patients 148 (46.1%) were not using corticosteroids, and 173 were using dexamethasone (34.3%), methylprednisolone (9.7%) or other corticosteroids (9.9%). Patients on corticosteroids had worse performance on all neurocognitive tests. Regression analyses demonstrated a negative association between corticosteroids use and the HVLT-R Free Recall score (R² change = 0.034, F change (1,272) = 13.392, p&lt;.001) and HVLT-R Delayed Recall score (R² change = 0.028, F change (1,270) = 10.623 p=.002). No statistically significant association was found for HVLT-R Delayed recognition, COWA, TMT part A and TMT part B (p&gt;0.05). Conclusions Glioblastoma patients prescribed with corticosteroids show poorer memory functions, expressive language, visual-motor scanning speed and executive functioning than patients not using corticosteroids. Furthermore, we found a negative association between corticosteroid intake and memory functions. The possibility of deleterious effects of corticosteroids on NCF should be considered during clinical decision-making.

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