Heterogeneous data fusion and machine learning for image understanding in lung cancer

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Lung cancer remains the most common cause of cancer death worldwide. For patients with early-stage non-small cell lung cancer, where the tumour is small (less than 5 cm) and has not spread to other parts of the body, standard treatment is either surgery or high-dose radiation therapy. However, even when these cancers are diagnosed at an early-stage, up to half of patients may develop a recurrence after treatment, in which the cancer returns at the same spot or somewhere else in the body. One of the major problems with lung cancer is determining which patients will be cured of their disease following treatment. To solve this problem, this research proposes to develop a novel software tool to aid physicians in determining which patients are at a higher risk of recurrence following treatment. Prior to treatment patients receive imaging to determine the extent of their disease, including computed tomography (CT) and position emission tomography (PET). However, physicians typically only measure the diameter of the tumour on CT and look for areas where the cancer has spread on PET. We propose to develop an artificially intelligent computer system to help physicians extract more information from these medical images. A new area of artificial intelligence, known as deep learning is a type of artificial neural network, which is a software program that mimics the structure and function of biological neurons, such as those in the brain. Deep learning has shown promise in many areas of medicine, including understanding imaging data. We will develop a deep learning based artificial intelligence software system to integrate medical imaging and the non-imaging patient data to predict which patients are at a higher risk of treatment failure. A deep learning system can extract subtle features within the image, that may not be visible by the physician's eye, and combine it with other patient information. This model will integrate multi-modal and multi-scale information, including 3-dimensional medical imaging data (CT and PET), clinical parameters (e.g., age, smoking history), blood parameters, and tumour genomic information. This software system will integrate multiple sources of information about a patient and provide the physician with a prognosis for the patient, or a probability that the standard treatment will cure the patient's cancer. We will also develop, for the first time, a novel graphical user interface to visualize and display this information to the physician. Overall, the software tool developed within this research program will enable accurate computer-aided prognosis based on different types of lung imaging data and the integration of clinical, blood, and genomic information about a patient. This non-invasive and inexpensive software tool will allow for better prognostic characterization of lung cancer that can help physicians in identifying patients at higher risk of recurrence for indicating more aggressive or personalized treatment options.