Publications by authors named "Shailesh Nayak"
Article Synopsis
- Brain tumors are challenging to diagnose and classify in oncology, and radiomics—an emerging field that analyzes quantitative features from medical images—may improve treatment planning despite concerns about study methodologies.
- A systematic review of literature identified 18 studies using radiomic features and machine learning models to classify gliomas, demonstrating their potential in distinguishing tumor subtypes and grades using various imaging techniques like MRI and PET/CT.
- The findings suggest that radiomics can achieve high classification accuracy that sometimes surpasses traditional diagnostic methods and the performance of less experienced radiologists, highlighting the need for further validation in clinical practice.
Background: Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC).
View Article and Find Full Text PDFEnvironmental contamination due to plastic waste mismanagement is a growing global concern. Plastic problem is of particular concern to the Indian Ocean nations as Asia currently contributes to the highest share of mismanaged plastic waste. Consequently, there is a worldwide interest to understand the distribution and transboundary movement of plastic from this region, which is crucial for implementing management measures.
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