ESTRO 2022 - Abstract Book

S1407

Abstract book

ESTRO 2022

slices), and MINN were used for training tabular and imaging combinations (e.g. text and imaging features). Classification accuracy was used to compare the developed models against each other over the hold-out dataset.

Table 1 Classes used in datasets.

Results Classification accuracy of each of the developed models is shown in Fig. 1. The best model (a MINN) reported 99.416% classification accuracy over the hold-out samples when used to standardise all the nomenclatures in a breast radiotherapy plan into 21 different classes (Dataset 5). 19 samples belonging to different classes were misclassified with 10 being predicted as ‘exclude’ (i.e. not to use).Three types of features were used with this model: textual features, dosimetry features, and images. When compared to employing single characteristics, integrating several features resulted in greater classification accuracy. Reliable performance was observed with all the datasets when using the text feature as input to the model, which is consistent with the traditional approach, where the clinicians look at text first to standardise nomenclatures.

Fig.1 Modelling results.

Conclusion Standardisation of nomenclatures using ML is feasible on single institutional data if multiple features are included in the model. This is an ongoing project, where federated ML will be investigated for standardising radiotherapy data across different centres, guidelines, and anatomical sites.

PO-1619 Clinical evaluation of novel DWI sequence on rectal cancer patients in a low tesla MR-Linac system

M. Nardini 1 , A. Capotosti 1 , G. Chiloiro 2 , L. Boldrini 2 , D. Cusumano 1 , A. Romano 2 , M.V. Antonelli 2 , G. Turco 2 , R. Moretti 1 , L. Indovina 1 , M.A. Gambacorta 2 , V. Valentini 2 , L. Placidi 1 1 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, UOSD Fisica Medica e Radioprotezione, Rome, Italy; 2 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, UOC Radioterapia Oncologica, Rome, Italy Purpose or Objective Magnetic resonance guided radiotherapy (MRgRT) allows online adaptation based on the daily anatomy as well as on quantitative tissue variation. The latter aspect, exploit the possibility to assess treatment response during each treatment’s