MR-linac: role of artificial intelligence and automation
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinic...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
22 January 2025
|
| In: |
Strahlentherapie und Onkologie
Year: 2025, Jahrgang: 201, Heft: 3, Pages: 298-305 |
| ISSN: | 1439-099X |
| DOI: | 10.1007/s00066-024-02358-9 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00066-024-02358-9 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00066-024-02358-9 
|
| Verfasserangaben: | Serena Psoroulas, Alina Paunoiu, Stefanie Corradini, Juliane Hörner-Rieber, Stephanie Tanadini-Lang |
| Zusammenfassung: | The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs. Despite these improvements, MR-guided radiotherapy (MRgRT) remains labor intensive and time consuming, highlighting the need for AI to streamline workflows and support rapid decision-making. Synthetic CTs from MR images and automated contouring and treatment planning will reduce manual processes, thus optimizing treatment times and expanding access to MR-linac technology. AI-driven quality assurance will ensure patient safety by predicting machine errors and validating treatment delivery. Advances in intrafractional motion management will increase the accuracy of treatment, and the integration of imaging biomarkers for outcome prediction and early toxicity assessment will enable more precise and effective treatment strategies. |
|---|---|
| Beschreibung: | Gesehen am 18.08.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1439-099X |
| DOI: | 10.1007/s00066-024-02358-9 |