# ESTRO 2023 - Abstract Book

S50

Saturday 13 May

ESTRO 2023

The results of our study on the professional quality of life of in-training radiation oncologists show medium job satisfaction. The levels of burnout and post-traumatic stress are media which requires the integration of the learning of stress management techniques to avoid the transition to extreme situations.

Proffered Papers: Biomarkers and prediction models

OC-0087 A complete bilateral model of lymphatic progression in HNSCC trained with multicentric patient data R. Ludwig 1 , J. Hoffmann 1 , B. Pouymayou 1 , L. Franceschetti 2 , L. Bauwens 3 , V. Grégoire 3 , P. Balermpas 1 , J. Unkelbach 1 1 University Hospital Zurich, Radiation Oncology, Zurich, Switzerland; 2 Univerity of Zurich, Physics, Zurich, Switzerland; 3 Centre Léon Bérard, Radiation Oncology, Lyon, France Purpose or Objective The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently defined using guidelines [1] based on the prevalence of metastasis by lymph node level (LNL). These guidelines recommend extensive irradiation of both sides of the neck for most patients, but do not consider how the patient-specific risk to harbor occult disease changes based on the individual's clinical diagnosis. We developed a statistical model to quantify the risk of occult disease in any LNL, given an individual patient’s T-category, tumor lateralization, and clinical extent of nodal metastasis. Materials and Methods We extended a previously developed hidden Markov model (HMM) of lymphatic progression in OPSCC [2] to include all relevant LNLs (I, II, III, IV, V, and VII) for both the ipsi- and the contralateral side. A tumor’s extension over the mid-sagittal plane is included as a risk factor for contralateral involvement. The state of each LNL (involved/healthy) is represented by binary random variables; lymphatic drainage is represented by directed arcs between the LNLs and are parametrized with spread probabilities. The model parameters are learned from two large OPSCC datasets from the USZ (CH) and the CLB (FR) (550 patients, see lyprox.org). Bayesian model comparison is used to determine the graph of the model that best describes the data. Results Fig. 1 compares the observed prevalence of involvement in the data (solid lines) and the model's prediction (shaded histograms) for different combinations of LNLs, T-category, and midline extension. To represent uncertainty in both the data and the model, the data is represented via Beta distribution over the observed prevalence while the model is represented via histograms obtained by sampling from the joint probability distribution over the model parameters. These comparisons show the HMM’s capability to fit to the dataset including the dependence of LNL involvement on T-category and midline extension.

Fig. 2 shows the model's predicted risks of occult metastases assuming imaging-based diagnosis of lymph node metastases with a sensitivity of 81% and a specificity of 76% [3]. The top panel in fig. 2 shows that the predicted risk for occult metastases in ipsilateral LNL IV only exceeds ~5% if the up-stream LNLs II and III harbor metastases. Similarly, the bottom panel shows that the risk in contra-lateral level III only exceeds ~5% if contralateral level II harbors metastases. The risk in contralateral level II exceeds ~5% except for lateralized tumors.

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