L'IRCM

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Personalized medicine is one of the driving forces in healthcare, where the treatment is individualized towards the specific needs of a patient, instead of the currently common “one size fits all” treatment modalities. This is needed due to the still disappointing outcomes of advanced tumors, for example the survival of advance stage NSCLC patients remains disappointing low. To further improve the curability and survival of cancer patients, first we need to exploit inter patient variations to select the optimal treatment for a specific patient. This variation arises, e.g. from differences in tumor shape and size, lung function and genetic factors. Here mathematical models, based on all available data of a patient, have to be used to support the physician in the decision process. For example, the genetics patterns of the tumors can select drugs that are potentially beneficial. Also, non-invasive visualization and quantification of drugs uptake using medical imaging before and during therapy could contribute significantly to the selection of potential beneficial patients and determination of the needed dosage. Secondly, improvement is achieved by taking into account intra tumor and intra organ heterogeneity derived from molecular and functional imaging. Radiation oncology is a modality that can direct its therapeutic effect in the four dimensions of space and time. Therefore, a higher radiation dose can be delivered towards the areas within the tumor that needs it the most, e.g. areas with increased radioresistance or areas with limited drug uptake. Also, the radiation must be kept away from regions within the lung that are prone to complications. It is expected that the implementation of techniques exploiting the heterogeneity of patients and tumors will improve personalized medicine, ultimately resulting in increased local control and survival.

Personalized medicine is one of the driving forces in healthcare, where the treatment is individualized towards the specific needs of a patient, instead of the currently common “one size fits all” treatment modalities. This is needed due to the still disappointing outcomes of advanced tumors, for example the survival of advance stage NSCLC patients remains disappointing low. To further improve the curability and survival of cancer patients, first we need to exploit inter patient variations to select the optimal treatment for a specific patient. This variation arises, e.g. from differences in tumor shape and size, lung function and genetic factors. Here mathematical models, based on all available data of a patient, have to be used to support the physician in the decision process. For example, the genetics patterns of the tumors can select drugs that are potentially beneficial. Also, non-invasive visualization and quantification of drugs uptake using medical imaging before and during therapy could contribute significantly to the selection of potential beneficial patients and determination of the needed dosage. Secondly, improvement is achieved by taking into account intra tumor and intra organ heterogeneity derived from molecular and functional imaging. Radiation oncology is a modality that can direct its therapeutic effect in the four dimensions of space and time. Therefore, a higher radiation dose can be delivered towards the areas within the tumor that needs it the most, e.g. areas with increased radioresistance or areas with limited drug uptake. Also, the radiation must be kept away from regions within the lung that are prone to complications. It is expected that the implementation of techniques exploiting the heterogeneity of patients and tumors will improve personalized medicine, ultimately resulting in increased local control and survival.

Conference in French with English visual support