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P13.10.A Predicting survival of glioblastoma after radiotherapy using deep learning and neuroimaging in a multi-centre cohort
BACKGROUND Glioblastoma is an aggressive brain tumour routinely monitored with MRI. However, the potential to use imaging as a prognostic biomarker predicting survival after treatment starts is unclear. This study aims to predict survival within eight months of completing radiotherapy from MRIs using deep learning.MATERIAL AND METHODS The dataset consists of 206 prospective and retrospective consecutive patients with glioblastoma (WHO 2021) across 11 UK centres (short-term survival: N=64; 31.1%). Models were trained/validated on N=158 (76.7%) retrospective patients (3 centres). Two holdout sets were sampled: retrospective test set (N=19); and external, prospective test set (N=29; 8 separate centres). Dense convolutional neural networks were developed with separate branches for T1c-w and T2-w MR sequences; a third branch concatenates outputs from those branches to predict survival. Dense blocks used pretrained weights for an abnormality detection model. A semi-supervised approach was implemented; patients without known outcomes were added with pseudo-labels during training. Parameter tuning included the number of updated blocks, linear layer sizes, and weighting of pseudo-labels. Imaging models were compared to counterparts that apply non-imaging variables (demographics, MGMT status/%, treatment). Logistic regression, support vector classifier, and decision tree models were fit on non-imaging features alone with feature selection and mean/mode imputation. Combined imaging/non-imaging networks were developed that added a branch for non-imaging inputs.RESULTS The current best-performing imaging model used MR sequences alone (validation weighted-ROC=0.80, 95% CI=0.75-0.84; balanced accuracy=0.70, 95% CI=0.55-0.85). Among non-imaging models, the logistic regression model with feature selection performed best (validation weighted-ROC=0.59, 95% CI=0.56-0.63; balanced accuracy=0.59, 95% CI=0.56-0.63). Combined model development is ongoing.CONCLUSION This is the first known model applying deep learning to MRIs from multiple centres to distinguish patients with failure to respond to chemoradiotherapy, and those who survive the subsequent treatment window. Based on initial results, image-based models performed best at predicting survival. Such models could be used across centres to suggest closer monitoring and trial targeting of patients with expected short-term survival compared to treatment response.</p
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Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study
Background: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods: We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates. Findings: From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation: The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding: Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.</p