Oncology Commons^™

Predicting Survival Of Nsclc Patients Treated With Immune Checkpoint Inhibitors: Impact And Timing Of Immune-Related Adverse Events And Prior Tyrosine Kinase Inhibitor Therapy, Michael R. Sayer, Isa Mambetsariev, Kun-Han Lu, Chi Wah Wong, Ashley Duche, Richard Beuttler, Jeremy Fricke, Rebecca Pharaon, Leonidas Arvanitis, Zahra Eftekhari, Arya Amini, Marianna Koczywas, Erminia Massarelli, Moom Rahman Roosan, Ravi Salgia

Pharmacy Faculty Articles and Research

Introduction: Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined.

Objective: To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs.

Methods: A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virigina B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …

Conference Proceedings: Aurora Scientific Day 2020

Journal of Patient-Centered Research and Reviews

Abstracts published in this supplement were among those presented at the 46th annual Aurora Scientific Day research symposium, held virtually on May 20, 2020. The symposium provides a forum for describing research studies conducted by faculty, fellows, residents, and allied health professionals affiliated with Wisconsin-based Aurora Health Care, a part of the Advocate Aurora Health health system, which publishes the Journal of Patient-Centered Research and Reviews.

Motion-Induced Artifact Mitigation And Image Enhancement Strategies For Four-Dimensional Fan-Beam And Cone-Beam Computed Tomography, Matthew J. Riblett

Theses and Dissertations

Four dimensional imaging has become part of the standard of care for diagnosing and treating non-small cell lung cancer. In radiotherapy applications 4D fan-beam computed tomography (4D-CT) and 4D cone-beam computed tomography (4D-CBCT) are two advanced imaging modalities that afford clinical practitioners knowledge of the underlying kinematics and structural dynamics of diseased tissues and provide insight into the effects of regular organ motion and the nature of tissue deformation over time. While these imaging techniques can facilitate the use of more targeted radiotherapies, issues surrounding image quality and accuracy currently limit the utility of these images clinically.

The purpose of …