Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

Background: Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease.

Genetic diversity and admixture patterns in Indian populations.

India is a diverse land whose population holds the history of waves of human dispersal. Recent studies suggest two major ancestral contributions to most of the Indian sub-populations. However, present day Indians are thought to contain huge genetic diversity derived consequent to multiple cultural, linguistic and geographical variations.