The aim of this doctoral thesis is to develop a tool able to automatically provide organs of interest segmentation in computed tomography images using machine learning techniques.

This tool will then be used to calculate organ doses in order to establish personalized dosimetric records in medical imaging. Doses will be calculated using informations obtained from images, radiographic technique and GPU-based Monte Carlo dose calculation algorithm (GPUMCD). Automated pipelines will be implemented to process large amounts of data.

The proposed tool provides a better evaluation of population exposure to ionizing radiation caused by medical imaging procedures.

Prostate cancer is the most common form of cancer in men in Canada.

This research project aims to establish a prognosis for a patient suffering from prostate cancer as well as predict the final pathology, by predicting the presence of lymph node metastases, from a FDG PET-CT. Radiomic characteristics are defined as the process of quantitative extraction of usable high-dimensional data from medical images. These are biomarkers that are difficult to see with the naked eye, such as texture and intensity. The database is made up of 250 prostate cancer patients. After filtration, a subset of 331 radiomic characteristics was selected. The accuracy of the model is 74.5%. This corresponds to an increase in precision of 6% compared to a model trained on all the extracted characteristics.

Ultimately, the algorithm will better predict the risk of recurrent prostate cancer and help improve methods and choice of treatment.

The clinical and economic burden of prostate cancer in Canada is substantial and is rising. It has been indicated that 1 in 7 men will develop prostate cancer during their lifetime, and another 1 in 27 will die due to the prostate cancer. However, only a part of prostate cancer cases is clinically important which make the prostate cancer case discrimination crucial to avoid over-treatment. Compared to ultrasound imaging method, advanced MRI modalities have demonstrated a better diagnostic accuracy and is becoming a clinical routine examination for patients at risk of having clinically significant prostate cancer. Even though the version two of PI-RADS has been recently published to facilitate MRI modalities application in prostate cancer, they still present limitations. For instance, variability is reported in terms of inter-reader agreement and diagnosis accuracy, mainly depend on reader experience.

This project aims to find a machine learning based approach for predication and segmentation of intraprostatic lesions to better guide radiation treatment. For accomplishing this task, the most advanced MRI modalities including DTI-MRI and DWI-MRI along with the anatomical MRI modalities will be employed. From the quantitative MRI modalities several maps that enhance specific features of the lesion will be extracted. Then after, texture information of the MRI modalities and selected maps will be extracted. In this step machine learning methods will be employed for feature selection and classification purposes. Finally, the prostate cancer extension and its type are identified.

This project consists of establishing good practices in health data management and building a software infrastructure in order to apply them.

We have developed pipelines that allow daily recovery of brachytherapy treatment data in order to calculate and store their dosimetric indices in a database dedicated to research. These indices are essential for planning radiotherapy treatments and for estimating their quality.

The aggregation of these indices allows different researchers such as bio-statisticians and radiation oncologists to carry out studies on larger data sets.

The objective of this project is to extract a set of relevant data from the files produced by medical imaging devices.

The process consists of building ETL (extract-transform-load) pipelines to make the data consumable for analysis and visualization. An example of analysis consists in observing the trend in dose administered to patients according to the establishment, protocol or device used, in order to possibly identify non-standard practices.

The data extracted could also guide practice by making it possible to assess the relevance of certain studies, and thus to optimize resources in the health network.

An exception drug is a drug that is not usually covered by the public drug insurance plan (RPAM). The measures implemented at the RAMQ for exceptional drugs allow the entire population to obtain coverage for certain drugs if they are used in compliance with the indications recognized by the Institut national d'excellence en santé et services sociaux (INESSS). Exception drugs are now a large and constantly increasing part of total spending on prescription drugs.

For the RPAM, one of the ways to control this increase is to reimburse these drugs according to pre-established rules. Currently, the system automatically processes around 20% of requests while the rest are directed to a case-by-case analysis, which generates delays.

This project is to help the business sector respond more quickly to requests for approval of exception drugs. A tool will be developed based on 15 years of data collected by the current system, and will aim to increase the number of requests processed automatically.