Melanoma

Melanoma is one of the fastest growing cancers in the world, and can affect patients earlier in life than most other cancers. Therefore, it is imperative to be able to identify patients at high risk for melanoma and enroll them in screening programs to detect the cancer early. Electronic health records collect an enormous amount of data about real-world patient encounters, treatments, and outcomes. This data can be mined to increase our understanding of melanoma as well as build personalized models to predict risk of developing the cancer. Cancer risk models built from structured clinical data are limited in current research, with most studies involving just a few variables from institutional databases or registries. This dissertation presents data processing and machine learning approaches to build melanoma risk models from a large database of de-identified electronic health records. The database contains consistently captured structured data, enabling the extraction of hundreds of thousands of data points each from millions of patient records. Several experiments are performed to build effective models, particularly to predict sentinel lymph node metastasis in known melanoma patients and to predict individual risk of developing melanoma. Data for these models suffer from high dimensionality and class imbalance. Thus, classifiers such as logistic regression, support vector machines, random forest, and XGBoost are combined with advanced modeling techniques such as feature selection and data sampling. Risk factors are evaluated using regression model weights and decision trees, while personalized predictions are provided through random forest decomposition and Shapley additive explanations. Random undersampling on the melanoma risk dataset shows that many majority samples can be removed without a decrease in model performance. To determine how much data is truly needed, we explore learning curve approximation methods on the melanoma data and three publicly-available large-scale biomedical datasets. We apply an inverse power law model as well as introduce a novel semi-supervised curve creation method that utilizes a small amount of labeled data.

Melanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed
or left undiagnosed. Advances in artificial intelligence, particularly deep learning,
have enabled the design and implementation of intelligent solutions to skin lesion
detection and classification from visible light images, which are capable of performing
early and accurate diagnosis of melanoma and other types of skin diseases. This work
presents solutions to the problems of skin lesion segmentation and classification. The
proposed classification approach leverages convolutional neural networks and transfer
learning. Additionally, the impact of segmentation (i.e., isolating the lesion from the
rest of the image) on the performance of the classifier is investigated, leading to the
conclusion that there is an optimal region between “dermatologist segmented” and
“not segmented” that produces best results, suggesting that the context around a
lesion is helpful as the model is trained and built. Generative adversarial networks,
in the context of extending limited datasets by creating synthetic samples of skin
lesions, are also explored. The robustness and security of skin lesion classifiers using
convolutional neural networks are examined and stress-tested by implementing
adversarial examples.

The metastatic process involves tumor cell adhesion to basement membrane components, such as type IV collagen. A specific mitogen activated protein kinase cascade is activated by cell adhesion to type IV collagen. This activation causes the expression of proteolytic enzymes. These enzymes will then participate in compromising extracellular matrix components and enhance cell movement through them. To better understand tumor invasion of type IV collagen, we have constructed triple-helical peptide (THP) ligands for melanoma cell receptors, and used these ligands to determine if receptors such as CD44/CSPG and the alpha2beta1 integrin have unique matrix metalloproteinase (MMP) signaling pathways affected by the tyrosine kinase inhibitor genistein. MMP protein expression profiles were evaluated using the alpha2beta1 integrin ligand, and CD44/CSPG ligand. Results were indicative of specific activation sequences that tumor cells undergo upon binding to select regions of type IV collagen.