Baseline total brain volume predicts changes in quality of life and overall survival after cranial radiotherapy in older patients with glioblastoma: Results from the prospective BRITER study

Background Short-course partial brain radiotherapy ± chemotherapy for older patients with GBM extends survival but there is no validated evidence for prediction of individual risk of acute radiotherapy-related side effects. Methods This prospective multicentre observational trial recruited patients with newly diagnosed GBM aged ≥65 planned for cranial radiotherapy. Baseline MRI scans were analyzed for markers of brain resilience including relative total brain volume (ratio of cerebrospinal fluid (CSF) volume to total intracranial volume (TIV)) and their relationship to change in quality of life (QoL). Results 126 patients enrolled: mean age 72 years (range 65-83). 77% had debulking surgery. 79% received radiotherapy with concurrent TMZ, and 21% received palliative radiotherapy alone. The median OS was 10.7 months. After accounting for age, sex, treatment, and baseline MoCA score, there was a relationship between baseline CSF:TIV and change in QoL score at 8 weeks post treatment. For each unit point of increase in CSF:TIV, there was a corresponding decrease in QoL score of 1.72 (95% CI −3.24 to −0.19 P = .027). 35 participants were too unwell to complete questionnaires or had died by the 8 week follow-up visit. In this subgroup, post hoc logistic regression showed baseline CSF:TIV was related to the risk of non-attendance (OR 1.35, 95% CI 1.01 to 1.80, P = .042). Cox regression models showed baseline CSF:TIV was associated with worsened OS (HR 1.41, 95% CI 1.19 to 1.66, P < .001). Conclusions This study provides evidence to support the use of an imaging biomarker to help assess the risk:benefit ratio for radiotherapy

Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium.

OBJECTIVE To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning models. METHODS MR imaging data were analysed from a random sample of 5 patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced -weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION The imaging protocol and scheduling varies across the UK, making it challenging to develop machine learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE Successful translation of deep learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules