Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC

Abstract

Abstract Volumetric-modulated arc therapy (VMAT) planning for locally advanced non-small cell lung cancer (NSCLC) is an iterative and planner-dependent process that often requires multiple optimization cycles to balance target coverage and organ‑at‑risk (OAR) sparing. Deep‑learning dose prediction can accelerate planning by providing patient‑specific reference dose distributions, but the impact of prescription‑dose mixing during model training remains unclear. This study evaluated whether prescription‑stratified models improve VMAT dose prediction performance. Seventy-two NSCLC VMAT cases were recalculated to 50, 54, and 60 Gy and split into training, validation, and test sets (42/10/20 cases). Four models with identical 3D U-Net architecture were developed: three single-prescription models (50/54/60 Gy) and one mixed-prescription model (50 + 60 Gy). Performance was assessed using mean absolute error (MAE) for planning target volume (PTV) and OAR dose metrics. Single-prescription models reproduced PTV coverage (D 95% and D 99% ) with MAEs < 4 Gy and hot-spot (D 2cc and D 5cc ) errors < 1 Gy, while mean dose errors for lungs and heart were ≤ 2.3 Gy. The mixed-prescription model showed larger errors: PTV hot-spot MAE rose to 11.3 Gy, and spinal cord maximum-dose errors reached 5–6 Gy, although most other OAR metrics changed modestly. Voxel‑wise difference maps revealed local deviations of a few Gy in low-dose lung regions and near steep gradients. These findings indicate that prescription‑dose stratification improves clinically relevant prediction metrics and support deep‑learning dose prediction as a planning decision‑support and optimization‑guidance tool.