Publications by authors named "Nursima Ak"
Article Synopsis
- The study investigates the differences in treatment responses among melanoma patients based on tumor characteristics, utilizing radiomic analysis of medical images to identify non-invasive biomarkers.
- This research involved 291 patients treated with either immune checkpoint inhibitors or BRAF targeted therapy, and 667 tumor lesions were analyzed for treatment outcomes.
- The findings show significant organ-level differences in treatment response and variability, with specific machine-learning models accurately predicting disease control or progression based on radiomic features, highlighting the potential for personalized treatment strategies.
Background: Immune-checkpoint inhibitors (ICIs) have showed unprecedent efficacy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). However, not all patients manifest clinical benefit due to the lack of reliable predictive biomarkers. We showed preliminary data on the predictive role of the combination of radiomics and plasma extracellular vesicle (EV) PD-L1 to predict durable response to ICIs.
View Article and Find Full Text PDFAccurate skull stripping facilitates following neuro-image analysis. For computer-aided methods, the presence of brain skull in structural magnetic resonance imaging (MRI) impacts brain tissue identification, which could result in serious misjudgments, specifically for patients with brain tumors. Though there are several existing works on skull stripping in literature, most of them either focus on healthy brain MRIs or only apply for a single image modality.
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