ESTRO 2021 Abstract Book
Conclusion We found three wavelet image features that added to the predictive performance of clinical variables and can be used for improved risk-stratification and potential addition of immunotherapy for high-risk patients. The features were strongly associated with the risk of DM, and could be a vital component of treatment adaptation as 21/114 patients had DM. External or prospective validation of these results is warranted.
PH-0107 A dynamic model for lymphatic progression of cancer through the head & neck R. Ludwig 1 , B. Pouymayou 1 , P. Balermpas 1 , J. Unkelbach 1 1 University Hospital Zurich, Radiation Oncology, Zurich, Switzerland
Purpose or Objective Currently, elective CTV definition in head & neck cancer is mostly based on the empiric prevalence of lymph node involvement for a given primary tumor location. However, an individual patient’s risk of harboring microscopic metastases in each lymph node level (LNL) varies depending on their T-stage and findings of macroscopic metastases in LNLs through imaging. We propose a probabilistic model for lymphatic metastatic spread that can quantify this risk of microscopic involvement based on the individual patient's state of cancer progression, which may allow for personalized CTV-N definition. Materials and Methods A patient’s state of disease is modelled via one hidden binary random variable for each LNL, which indicates if the LNL harbors tumor including occult metastases. This hidden state of a LNL is connected via sensitivity and specificity to an observed binary random variable that indicates if metastases are seen on imaging. Over one- time step, tumor cells may spread from the primary tumor to an LNL, or between LNLs, with some probability rate. Formally, this is described by a hidden Markov model (HMM). The directed arcs of the HMM's graph reflect the direction of lymph flow and spread probability rates are learned from a dataset of patients in whom involvement of each LNL was reported (Figure 1). T-stage can be incorporated into the model by assuming that on average late T-stage tumors had more time to progress than early T-stage tumors. We demonstrate the HMM model for ipsilateral spread of oropharyngeal head & neck squamous cell carcinoma, trained via MCMC sampling using a dataset reconstructed from , and under the assumption that the portion of N0 patients is 30% for early T-stage patients and 20% for late T-stage. Figure 1:
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