Radiotherapy 3D printing needs standardized quality assurance across institutions
Inter-centre material variability supports shared QA datasets and standardized acceptance criteria for clinical radiotherapy 3D printing.
Inter-centre material variability supports shared QA datasets and standardized acceptance criteria for clinical radiotherapy 3D printing.
A 1 mm symmetric MLC closing reduced minimum GTV dose by 25%, while translations caused cumulative target undercoverage.
Strict exit-dose gamma thresholds correlated with anatomical change and identified all clinically replanned patients in a retrospective validation cohort.
Low-cost diagnostic and treatment phantoms improved students’ self-reported understanding of radiation medicine, although formal learning outcomes were not assessed.
Combining plan complexity with three-dimensional dose radiomics improved tomotherapy PSQA prediction, although performance deteriorated substantially during cross-institution testing.
Millimeter-scale localization uncertainty shifted assigned biopsy doses by several Gy and made nominal DVH threshold classification frequently unreliable.
Spectral hardening altered multimeter calibration by up to 11% for kerma, 37% for HVL and 42% for voltage.
A model trained at one centre closely predicted doses in 560 head and neck plans from six external institutions.
IPEM, AAPM and NCS recommendations largely aligned, with complementary guidance for MRI simulation, MR-only workflows and MR-Linac quality assurance.
A retrieval-augmented GPT-4.1 chatbot rapidly surfaced previous TrueBeam faults, but procedural, role-definition and safety errors remained.
An autoencoder identified anomalous IMRT plans with an area under the curve of 0.98 and explained which parameters drove each alert.
A national end-to-end audit found acceptable spine stereotactic radiotherapy accuracy but systematic differences between dose-reporting conventions.
VMAT-based lattice SFRT achieved cross-platform deliverability with gamma pass rates above 90% and preserved peak-to-valley dose ratios.
Deep learning auto-segmentation reduced editing, but both minimal and excessive manual edits were associated with lower clinical acceptability.
Photon-counting computed tomography generated effective atomic number and physical density images directly from virtual monoenergetic images.