Extraction and analysis of biomarkers derived from PET and MRI for improved treatment planning in radiotherapy

Beyond the conventional techniques of diagnosis and follow-up of cancer, radiomics analysis could allow to personalize radiotherapy treatments, by proposing a non-invasive characterization of tumor heterogeneity. Based on the extraction of advanced quantitative parameters (histograms of intensities, texture, shape) from multimodal imaging, this technique has notably proved its interest in determining predictive signatures of treatment response. During this thesis, signatures of cervical cancer recurrence have been developed, based on radiomics analysis alone or in combination with conventional biomarkers, providing major perspectives in the stratification of patients that can lead to dosimetric treatment plan adaptation. However, various methodological barriers were raised, notably related to the great variability of the protocols and technologies of image acquisition, which leads to major biases in multi-centric radiomics studies. These biases were assessed using phantom acquisitions and multicenter patient images for PET imaging, and two methods enabling a correction of the stratification effect were proposed. In MRI, a method of standardization of images by harmonization of histograms has been evaluated in brain tumors. To go further in the characterization of intra-tumor heterogeneity and to allow the implementation of a personalized radiotherapy, a method for local texture analysis has been developed. Specifically adapted to brain MRI, its ability to differentiate sub-regions of radio-necrosis or tumor recurrence was evaluated. For this purpose, parametric heterogeneity maps have been proposed to experts as additional MRI sequences. In the future, validation of the predictive models in external centers, as well as the establishment of clinical trials integrating these methods to personalize radiotherapy treatments, will be mandatory steps for the integration of radiomics in the clinical routine. (author)