ESTRO 37 Abstract book

S1092

ESTRO 37

stage. We present a statistical model to estimate the probability of microscopic involvement of LN levels based on the individual patient's state of lymphatic tumor progression. Material and Methods To estimate the probability of microscopic involvement of LN levels, patient specific characteristics (site of the PT and macroscopic LN metastases) have to be combined with population-based statistics on the lymphatic progression patterns. Bayesian networks (BN) are well suited to quantitatively describe this problem. A BN consists of nodes (which represent random variables) and edges (which represent probabilistic relationships). We developed a BN model in which each LN level is associated with two binary nodes (Figure 1). One corresponds to the detected macroscopic state (e.g. visible LN metastases on imaging) while the second corresponds to the microscopic involvement and cannot be observed. The edge linking the two nodes models the detection probabilities and is used to infer the microscopic state. PT sites are modeled by additional binary nodes. The edges connecting the LN levels and the PT nodes represent the probability for tumor cells to spread from one site to another. The graph structure and the model parameters are chosen based on current guidelines [2] and to match the risks of microscopic involvement reported in [1].

Conclusion Bayesian networks provide a framework for combining patient characteristics with population based patterns of lymphatic progression, and thereby provide a method to individualize CTV definition. References: [1] G. Sanguineti et al, IJROBP, V. 74(5), p.1356-64, 2009 [2] V. Grégoire et al, Radiother. Oncol., V. 110(1), p.172- 81, 2014 EP-2002 Ethnicity Information in Toxicity-Related Radiogenomic Studies: A Systematic Review N. Yahya 1 , S. Zuber 1 , H. Manan 2 1 The National University of Malaysia, Faculty of Health Sciences, Kuala Lumpur, Malaysia 2 The National University of Malaysia, Department of Radiology, Kuala Lumpur, Malaysia Purpose or Objective Associations of genes and radiotherapy-related toxicity obtained from one ethnicity or racial group are not necessarily applicable to individuals whose ancestry is primarily from a different ethnic or racial group. This study aims to map the statement of ethnicity in radiogenomic studies involving radiotherapy-related toxicity and determine the level of inclusion of this information in the analysis. Material and Methods Studies were identified by searching Scopus, PubMed and Medline until November 2016. References from the retrieved articles were also searched for additional publications. Studies satisfying the following criteria were eligible for inclusion: related to the effects of radiation dose from radiotherapy to normal tissues and involving human subjects. Studies on animals, cell cultures, case reports, meta- analysis and systematic review articles were excluded. The completeness of the ethnicity information was determined by mining the statement in the method/results. The consideration of ethnicity as a covariate and its significance was based on the statement mentioned in the statistical analysis/results of the study. Results 177 studies were found to satisfy the inclusion criteria. 75/177 stated the ethnicity of which 21/75 specifically stated the numbers for each ethnic and another 54/75 either reported a homogenous cohort or mentioned “mixed” without further specification. Of studies with ethnicity details, 10/21 has substantial mixed (second most common ethnicity > 20% of the most common) and 14/21 considered ethnicity as a covariate. From 14 studies, 2 found significant associations between ethnicity and toxicity. Conclusion Large proportion of radiogenomic studies are from homogenous cohorts which can make comparisons across studies with differing ancestry difficult. Studies combining cohorts from different ethnicity need to be encouraged.

Results Once the BN is defined, it can be applied to personalize CTV definition for a newly diagnosed patient. To that end, an inference algorithm retrieves the probabilities of the microscopic state, given the observed macroscopic state. In Figure 1, we illustrate this for an oropharynx tumor patient in whom levels II and III harbor LN metastases. The BN model predicts that level IV is microscopically involved with a probability of 11%. An advantage of the BN model is that it describes how the risk of microscopic involvement depends on the macroscopic progression (Table 1). For example, when level III has no observed metastases, the involvement probability of level IV drops to 5%.