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Full-Text Articles in Oncology
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
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
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 …