The SARS-CoV-2 (COVID-19) novel corona virus represents a significant health risk, particularly in older patients. Cancer is one of the leading causes of death in most rich countries, and delivering chemotherapy may be associated with increased risk in the presence of a pandemic infection. Estimating this risk is crucial in making decisions about balancing risks and benefits from administering chemotherapy. However, there are no specific data about chemotherapy risks per se. Here we develop a simple model to estimate the potential harms in patients undergoing chemotherapy during a COVID outbreak. We use age-related case fatality rates as a basis for estimating risk, and use previous data from risk of death during influenza outbreaks to estimate the additional risk associated with chemotherapy. We use data from randomised trials to estimate benefit across a range of curative and palliative settings, and address the balance of benefit against the risk of harm. We then use those data to estimate the impact on national chemotherapy delivery patterns.

Aims During the coronavirus disease 2019 (COVID-19) pandemic, organisations have produced management guidance for cancer patients and the delivery of cytotoxic chemotherapy, but none has offered estimates of risk or the potential impact across populations. Materials and methods We combined data from four countries to produce pooled age-banded case fatality rates, calculated the sex difference in survival and used data from four recent studies to convert case fatality rates into age/sex-stratified infection fatality rates (IFRs). We estimated the additional risk of death in cancer patients and in those receiving chemotherapy. We illustrate the impact of these by considering the impact on a national incident cancer cohort and analyse the risk–benefit in some clinical scenarios. Results We obtained data based on 412 985 cases and 41 854 deaths. The pooled estimate for IFR was 0.92%. IFRs for patients with cancer ranged from 0 to 29% and were higher in patients receiving chemotherapy (0.01–46%). The risk was significantly higher with age and in men compared with women. 37.5% of patients with a new diagnosis of cancer in 2018 had an IFR ≥5%. Survival benefits from adjuvant chemotherapy ranged from 5 to 10% in some common cancers, compared with the increased risk of death from COVID-19 of 0–3%. Conclusions Older male patients are at a higher risk of death with COVID-19. Patients with cancer are also at a higher risk, as are those who have recently received chemotherapy. We provide well-founded estimates to allow patients and clinicians to better balance these risks and illustrate the wider impact in a national incident cohort.

Background Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma. Methods A neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets. Results The model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218–0.988, p = 0.046; HR 0.466, 95% CI 0.235–0.925, p = 0.029, respectively). Conclusions Temporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.

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Objective. (1) Examine QoL of caregivers of patients with brain tumours compared to population norms and other cancer caregiver groups, (2) appraise the content of quantitative QoL outcome measures utilised, and (3) assess to what extent QoL measures used in research align with caregivers’ priorities. Methods. Systematic literature search of studies including caregivers of brain tumour patients using self-completed assessments of QoL. Extracted data from included studies included quantitative QoL outcome data, QoL outcome measures utilised, and the included QoL domains. The impact of brain tumour patient caregiving was assessed by summarising included data comparing brain tumour caregivers to other cancer caregivers and normative population data. QoL measures utilised by the studies and their domains were extracted, coded, and analysed by themes. The rates of investigation by theme were then compared to existing data on caregiver-own preference in relation to QoL. Results. 49 studies, including 57 outcome measures, incorporating a combined 124 QoL domains. Brain tumour caregivers reported lower QoL outcomes than population norms but similar to other cancer caregiver groups. Thematic analysis of QoL domains generated 7 themes: caregiving burden and adaptation, existential and self, family and social support, finances, information needs, physical symptoms and functioning, and psychological symptoms and wellbeing. The most investigated themes were physical and psychological symptoms, the most important for caregivers themselves were family and social support. Conclusions. Caregiving for brain tumour patients is shown to negatively affect QoL, particularly mental health, burden, and social life. Existing QoL research in caregivers of brain tumour patients predominantly utilises generic QoL measures designed for use in patients and draws a medicalised view of QoL priorities. The few studies using caregiver-specific QoL measures demonstrated closer alignment to caregiver preferences such as family and social support.

AIMS CaPaBLE tests the feasibility and acceptability of assessing quality of life (QoL) using the patient-, or caregiver-generated index (PGI/CaGI) methodology in patients with HGG and their caregivers. METHOD CaPaBLE, (https://www.isrctn.com/ISRCTN45555598), followed patients and/or their caregivers up to 6 months. Standard measures for patients were EORTC QLQ-C30/BN20, for caregivers the CarGOQOL questionnaire. The QoL topics raised through PGI/CaGI have been coded to the most relevant domain from their respective standard measure for an initial assessment of concordance. RESULTS 36 patients, 24 caregivers recruited to study; completing an average of 3 study assessment timepoints. PGI and CaGI generated 240 and 160 topics respectively. Patient concerns most frequently coded to EORTC domain of Role Functioning; Caregiver concerns mostly coded to CarGOQOL domain of Burden. Other topics frequently raised by patients such as the driving and sex life, and future planning by caregivers are not specifically raised in standard questionnaires. CONCLUSION Nearly all topics raised by patients and caregivers were mapped to the domains of their respective standard QoL measure. However, almost half of all topics raised by patients and caregivers mapped to a minority of the domains included in standard measures; whilst a notable number of topics are not specifically included in standard measures at all. This raises questions regarding the efficiency and relevance of such questionnaires to patient and caregivers’ daily lives.

Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.