Publications by authors named "Serafettin Zenkin"
Article Synopsis
- The study assessed how radiomics—a method of extracting and analyzing features from medical images—can predict the tumor microenvironment (TME) and response to anti-PD-1 treatment in patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC).
- Using advanced techniques like CT scans and machine learning algorithms, researchers built models to evaluate disease control rates, progression-free survival, and overall survival, alongside assessing tumor characteristics like hypoxia and immune cell presence.
- Findings indicated that radiomics could accurately predict treatment outcomes and TME features, suggesting its potential as a valuable tool, although more extensive research is needed to confirm these results.
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 PDFBackground: Preoperative symptom severity in cervical spondylotic myelopathy (CSM) can be variable. Radiomic signatures could provide an imaging biomarker for symptom severity in CSM. This study utilizes radiomic signatures of T1-weighted and T2-weighted magnetic resonance imaging images to correlate with preoperative symptom severity based on modified Japanese Orthopaedic Association (mJOA) scores for patients with CSM.
View Article and Find Full Text PDFPurpose: Although glioblastoma (GBM) is the most common primary brain malignancy, few tools exist to pre-operatively risk-stratify patients by overall survival (OS) or common genetic alterations. We developed an MRI-based radiomics model to identify patients with EGFR amplification, MGMT methylation, GBM subtype, and OS greater than 12 months.
Methods: We retrospectively identified 235 patients with pathologically confirmed GBMs from the Cancer Genome Atlas (88; TCGA) and MD Anderson Cancer Center (147; MDACC).
Accurate 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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