Deep learning synthetic computed tomography supported head-and-neck adaptive radiotherapy dose recalculation
Synthetic computed tomography from cone-beam computed tomography achieved a 98.7% gamma pass rate for head-and-neck dose recalculation.
Synthetic computed tomography from cone-beam computed tomography achieved a 98.7% gamma pass rate for head-and-neck dose recalculation.
In 180 thoracic proton therapy patients, larger clinical target volume and heart volume independently predicted the need for adaptive replanning.
Weekly monitoring identified week 2 triggers for nodal targets and contralateral parotids, with later week 4 triggers for primary targets.
Mean treated-breast displacement was 1.6 mm, with larger patient-specific changes near treatment completion and during follow-up.
Tumour volume reduction above 40% during radiotherapy was independently associated with improved overall survival in patients with oropharyngeal cancer.
A reusable phantom-based test verified simulation-omitted adaptive workflows on cone-beam computed tomography-guided and magnetic resonance-guided treatment systems.
Patient-specific surface imaging reconstructed breast radiotherapy dose with a 93.8% gamma passing rate and 42-millisecond latency without additional imaging radiation.
Dynamic collimation improved target-region contrast and signal-to-noise ratio while preserving full-field information without increasing the total photon budget.