Inverse treatment planning using deep learning-based organs at risk in radiotherapy for head and neck cancer: a prospective planning study.

Tristan Bauer¹, Oliver Weinhold¹, Ulrich Schratzenstaller¹, Andrei Bunea^1,2, Joshua Giambattista^3,4, Jon Giambattista⁴, Alexandros Papachristofilou¹, Tobias Finazzi^5,6

¹Clinic of Radiotherapy and Radiation Oncology, University Hospital Basel, Basel, Switzerland.

²Department of Radiation Oncology, BORAD, Bottrop, Germany.

³Allan Blair Cancer Centre, Regina, Saskatchewan, Canada.

⁴Limbus AI; now Radformation, Radformation Inc., New York, USA.

⁵Clinic of Radiotherapy and Radiation Oncology, University Hospital Basel, Basel, Switzerland. tobias.finazzi@ksb.ch.

⁶Department of Radiation Oncology, Cantonal Hospital Baden, Baden, Switzerland. tobias.finazzi@ksb.ch.

PubMed DOI

Abstract

PURPOSE: Radiotherapy (RT) planning for head and neck squamous cell carcinoma (HNSCC) is known to be both challenging and time-consuming. Deep learning (DL)-based auto-segmentation of organs at risk (OARs) may streamline this procedure, although studies have mainly evaluated the geometric accuracy of DL-based contours. Here, we report on a prospective study of inverse treatment planning using DL-b…

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