Artificial intelligence for treatment delivery: image-guided radiotherapy

被引：5

作者：

Rabe, Moritz ^{[1
]}

Kurz, Christopher ^{[1
]}

Thummerer, Adrian ^{[1
]}

Landry, Guillaume ^{[1
,2
,3
]}

机构：

[1] Ludwig Maximilians Univ Munchen, LMU Univ Hosp, Dept Radiat Oncol, Marchioninistr 15, D-81377 Munich, Bavaria, Germany

[2] German Canc Consortium DKTK, Partner Site Munich, Marchioninistr 15, D-81377 Munich, Bavaria, Germany

[3] Bavarian Canc Res Ctr BZKF, Marchioninistr 15, D-81377 Munich, Bavaria, Germany

来源：

STRAHLENTHERAPIE UND ONKOLOGIE | 2025年 / 201卷 / 03期

关键词：

Synthetic computed tomography; Deep learning; Automatic segmentation; Motion management; Online adaptive radiation therapy; CONE-BEAM CT; PATHOLOGICAL COMPLETE RESPONSE; DEEP LEARNING APPROACH; GENERATE SYNTHETIC CT; NEURAL-NETWORK; RADIATION-THERAPY; ADAPTIVE RADIOTHERAPY; COMPUTED-TOMOGRAPHY; AUTO-SEGMENTATION; DELTA-RADIOMICS;

D O I：

10.1007/s00066-024-02277-9

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.

引用

页码：283 / 297

页数：15

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