ESTRO 2024 - Abstract Book


Clinical - Head & neck

ESTRO 2024

had multiples HNPGL, which were treated together. Local control (LC) was defined on MRI according RECIST criteria, while toxicity according to CTCAE v5.0 scoring system.


Median Age was 60 years (28.5-89). Median PTV of lesions treated with TT was 51.135 cm3 (8.1-444.1 cm3), median PTV of CK lesions 33.98 cm3 (8.52-70.68 cm3), and median GTV of GK lesions 7.60 cm3 (0.36-24.60 cm3). Median follow up was 68 months (2.2-180 months). Kaplan Meier estimates of local relapse-free survival was 97.2 %. One patient, treated with GK, experienced tumor progression and underwent a second GK, regaining LC. To perform a comparative toxicity analysis, volumes below 7cc have not been considered. Four pts (40%) treated with TT presented G2 acute toxicities: one mucositis, one erythema, one dysphagia and one xerostomia. Two pts (22.22%) treated with CK presented: pain G2 and dysphagia G2. One patient treated with GK experienced transient trigeminal neuralgia while another one experienced transient vertigo (11.76% of patients) (See Table 1). An improved neurological status was registered in 30 pts (60%), and 20 pts (40%) showed clinical stability. No patient experienced worsening of pre-existing neurological symptoms.


In our experience CK, GK and TT are effective and safe treatment options, with an excellent LC. Fractionated radiotherapy was prescribed for larger HNPGL volumes, and no difference was observed between treatments.

Keywords: Paragangliomas, Gammaknife, CyberKnife


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Evaluation of ART-Plan ™ autocontouring software for head and neck radiotherapy: A blinded assessment

Tom Young 1,2 , Victoria Butterworth 1 , Sarah Misson-Yates 1 , Mary Lei 1,2 , Anthony Kong 1,2 , Imran Petkar 1,2 , Miguel Reis Ferreira 1,2 , Delali Adjogatse 1,2 , Andrew King 3 , Teresa Guerrero Urbano 1,2 1 Guy's and St Thomas' NHS Foundation Trust, Radiotherapy, London, United Kingdom. 2 King's College London, School of Cancer & Pharmaceutical Sciences, London, United Kingdom. 3 King's College London, School of Biomedical Engineering Imaging Sciences, London, United Kingdom


Contouring of organs-at-risk (OARs) and target volumes is a key task within the radiotherapy (RT) workflow but is time-consuming and subject to both inter- and intra-observer variability (1). Sub-optimal contouring has been shown to affect survival and toxicity outcomes (2). Software using Artificial Intelligence (AI) to automatically delineate OARs and elective nodal regions has been developed, with several commercial solutions now available.

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