Oncologic Emergencies: Immune-Based Cancer Therapies and Complications

Cancer therapies have undergone several recent advancements. Current cancer treatments include immune-based therapies comprised of checkpoint inhibitors, and adoptive immunotherapy; each treatment has the potential for complications that differ from chemotherapy and radiation. This review evaluates immune-based therapies and their complications for emergency clinicians. Therapy complications include immune-related adverse events (irAE), cytokine release syndrome (CRS), autoimmune toxicity, and chimeric antigen receptor (CAR) T-cell-related encephalopathy syndrome (CRES). Immune-related adverse events are most commonly encountered with checkpoint inhibitors and include dermatologic complications, pneumonitis, colitis/diarrhea, hepatitis, and endocrinopathies. Less common irAEs include nephritis, myocardial injury, neurologic toxicity, ocular diseases, and musculoskeletal complications. CRS and CRES are more commonly associated with CAR T-cell therapy. CRS commonly presents with flu-like illness and symptoms resembling sepsis, but severe myocardial and pulmonary disease may occur. Critically ill patients require resuscitation, broad-spectrum antibiotics, and hematology/oncology consultation

Low Dose CT Image Reconstruction With Learned Sparsifying Transform

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt an alternating algorithm to optimize the PWLS-ST cost function that alternates between a CT image update step and a sparse coding step. We adopt a relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed OS-LALM) to accelerate the CT image update step by reducing the number of forward and backward projections. Numerical experiments on the XCAT phantom show that for low dose levels, the proposed PWLS-ST method dramatically improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop paper DOI: 10.1109/IVMSPW.2016.752821

Game theoretic and machine learning techniques for balancing games

Game balance is the problem of determining the fairness of actions or sets of actions in competitive, multiplayer games. This problem primarily arises in the context of designing board and video games. Traditionally, balance has been achieved through large amounts of play-testing and trial-and-error on the part of the designers. In this thesis, it is our intent to lay down the beginnings of a framework for a formal and analytical solution to this problem, combining techniques from game theory and machine learning. We first develop a set of game-theoretic definitions for different forms of balance, and then introduce the concept of a strategic abstraction. We show how machine classification techniques can be used to identify high-level player strategy in games, using the two principal methods of sequence alignment and Naive Bayes classification. Bioinformatics sequence alignment, when combined with a 3-nearest neighbor classification approach, can, with only 3 exemplars of each strategy, correctly identify the strategy used in 55\% of cases using all data, and 77\% of cases on data that experts indicated actually had a strategic class. Naive Bayes classification achieves similar results, with 65\% accuracy on all data and 75\% accuracy on data rated to have an actual class. We then show how these game theoretic and machine learning techniques can be combined to automatically build matrices that can be used to analyze game balance properties