ESTRO38 Congress Report

Physics

2. Radiotherapy-related lymphopenia affects overall survival in patients with lung cancer (E38-1852)

Azadeh Abravan 1,2 , Corinne Faivre-Finn 1,2 , Jason Kennedy 2 , Alan McWilliam 1,2 , Marcel van Herk 1,2

1 Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom. 2 Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.

Context of the study Lymphocytes, one of the subtypes of white blood cells, are one of the most radiosensitive cells in the human body. Lymphopenia, a drop in lymphocyte counts, can be observed during or early after a course of radiotherapy in cancer patients. Lymphopenia can lead to opportunistic infection, poor quality of life, and impact negatively on patients’ survival. However, a comprehensivemethodology to identify the cause of this adverse effect has not yet been established. In our study, we employed for the first-time image-based data mining approaches to identify regions of the body associated with severe lymphopenia (i.e. grade 3 or higher, according to Common Terminology Criteria for Adverse Events v4.0) in lung cancer patients treated with radiotherapy. Overview of abstract We first showed that lymphopenia during treatment is related to overall survival in lung cancer patients receiving curative-intent radiotherapy with/without chemotherapy. We then went on to identify regions in the body that are associated with lymphopenia after radiotherapy. To achieve this, wematched patients who developed lymphopenia and those who did not, based on their tumour volume, baseline lymphocyte count, prescribed radiotherapy dose (all of which are known to affect lymphocyte counts), and tumour histology. After matching, identified patients were used in image-based data mining. This methodology analysed the planned dose distribution across all patients to identify regions where dose is associated with lymphopenia. Our data mining approach found a region including heart, lung, and thoracic vertebrae where the difference in radiotherapy dose between lymphopenia yes/no groups was significant. Multivariablemodel for predicting lymphopenia, using radiotherapy dose parameters and other known factors, was proposed and further validated in a cohort of oesophageal cancer patients. What were the three main findings of your research? Firstly, lung cancer patients who develop lymphopenia during treatment have significantly worse overall survival compared to those without lymphopenia. Secondly, data mining from matched patients showed that irradiation of lung, heart, and thoracic vertebrae is associated with lymphopenia. Thirdly, after multivariable modelling, lymphopenia is found to be related to the radiotherapy dose delivered to thoracic vertebrae and heart, in combination with the baseline lymphocyte counts and radiotherapy duration. The same result is observed in the oesophageal cancer cohort. What impact could your research have? Radiotherapy-related side-effects can have a significant

negative impact on patients’ survival. In recent years, the thoracic oncology community has become increasingly aware of the impact of heart irradiation on morbidity and mortality in lung cancer patients. Emerging data has also highlighted that lymphopenia can adversely affect prognosis due to an increased risk of opportunistic infections. Identifying regions of the body associated with severe lymphopenia in lung cancer patients treated with radiotherapy will lead to personalized treatment and improved outcome. This can be overcome by reducing dose to identified regions, frequent monitoring of lymphocyte counts during therapy, and use of prophylactic antibiotics for patients at higher risk. Reducing the risk of lymphopenia may also be of importance in the era of immunotherapy and radiotherapy combinations. Is this research indicative of a bigger trend in oncology? In the field of Oncology, ‘learning fromevery patient treated’ is considered a complementary alternative to randomized clinical trials and is a fast-growing field. Retrospective data mining allows valuable information to be extracted from large volumes of routine data. It is important to develop and improve techniques to employ these massive amounts of information produced every day, intelligently. Particularly, methods of handling and maintain the rich 3-dimensional dosimetric information of every patient. Careful analysis of such data would lead us to better understand the mechanisms that affect individual patients following their care andwill allowdevelopment of new treatment strategies.

PHYSICS | Congress report

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