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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Veröffentlicht in:	Medical physics (Lancaster) Jg. 52; H. 11; S. e70141 - n/a
Hauptverfasser:	Lei, Yang, Zhang, Jiahan, Liu, Tian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.11.2025
Schlagworte:	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
Online-Zugang:	Volltext
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