Search

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 five 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 T1‐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. [ABSTRACT FROM AUTHOR]

Hodgkin lymphoma (HL) treatment increases the risk of lung cancer. Most HL survivors are not eligible for lung cancer screening (LCS) programmes developed for the general population, and the utility of these programmes has not been tested in HL survivors. We ran a LCS pilot in HL survivors to describe screening uptake, participant characteristics, impact of a decision aid and screen findings. HL survivors treated ≥5 years ago with mustine/procarbazine and/or thoracic radiation, were identified from a follow-up database and invited to participate. Participants underwent a low-dose CT (LDCT) reported using protocols validated for the general population. Two hundred and eighteen individuals were invited, 123 were eligible, 102 were screened (58% response rate): 58% female, median age 52 years, median 22 years since HL treatment. 91.4% were deemed to have made an informed decision; participation was not influenced by age, gender, years since treatment or deprivation. Only 3/35 ever-smokers met criteria for LCS through the programme aimed at the general population. Baseline LDCT results were: 90 (88.2%) negative, 10 (9.8%) indeterminate, 2 (2.0%) positive. Two 3-month surveillance scans were positive. Of 4 positive scans, 2 patients were diagnosed with small-cell lung cancer; 1 underwent curative surgery. Coronary artery calcification was detected in 36.3%, and clinically significant incidental findings in 2.9%. LDCT protocols validated in ever-smokers can detect asymptomatic early-stage lung cancers in HL survivors. This finding, together with screening uptake and low false positive rates, supports further research to implement LCS for HL survivors.