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Erdur, A.C.* ; Rusche, D.* ; Scholz, D.* ; Kiechle, J.* ; Fischer, S.* ; Llorián-Salvador, O.* ; Buchner, J.A.* ; Nguyen, M.Q.* ; Etzel, L. ; Weidner, J.* ; Metz, M.C.* ; Wiestler, B.* ; Schnabel, J.A. ; Rueckert, D.* ; Combs, S.E. ; Peeken, J.C.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

Strahlenther. Onkol., DOI: 10.1007/s00066-024-02262-2 (2024)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
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Publication type Article: Journal article
Document type Review
Keywords Automatic Segmentation ; Deep Learning ; Radiation Oncology ; Radiotherapy Planning; Clinical Target Volume; Medical Image Segmentation; Radiation-therapy; Auto-segmentation; Interobserver Variability; Automatic Segmentation; Statistical Shape; Neural-network; Cancer; Delineation
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0179-7158
e-ISSN 1439-099X
Journal Strahlentherapie und Onkologie : Journal of Radiation Oncology, Biology, Physics
Publisher Urban & Vogel
Publishing Place Tiergartenstrasse 17, D-69121 Heidelberg, Germany
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)
Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30203 - Molecular Targets and Therapies
30205 - Bioengineering and Digital Health
Research field(s) Radiation Sciences
Enabling and Novel Technologies
PSP Element(s) G-501300-001
G-507100-001
Grants Else-Kroener-Fresenius-Stiftung - Wilhelm Sander-Stiftung
Deutsche Forschungsgemeinschaft (DFG, German Research foundation)
DOI 10.1007/s00066-024-02262-2
WOS ID 001285681900001
Scopus ID 85201052621
PubMed ID 39105745
Erfassungsdatum 2024-09-17
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