ArticleScientific reports2026
Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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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
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