Non‐convex IMRT treatment planning using deep inverse optimization

Background Intensity‐modulated radiation therapy (IMRT) utilizes inverse optimization to calculate beamlet intensities, achieving clinical dose objectives for tumors while minimizing dose to organs‐at‐risk (OARs). However, incorporating non‐convex dose‐volume constraints into IMRT planning is challe...

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Vydané v:	Medical physics (Lancaster) Ročník 52; číslo 11; s. e70141 - n/a
Hlavní autori:	Lei, Yang, Zhang, Jiahan, Liu, Tian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.11.2025
Predmet:	Carcinoma, Non-Small-Cell Lung - radiotherapy deep inverse optimization Deep Learning Humans IMRT auto‐planning Lung Neoplasms - radiotherapy non‐convex programming Organs at Risk - radiation effects Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted - methods Radiotherapy, Intensity-Modulated - methods IMRT auto‐planning non‐convex programming deep inverse optimization
ISSN:	0094-2405, 2473-4209, 2473-4209
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