Keeping gender in mind: Applications of Machine Learning in Neuro-Oncology
Investigation of the sexually dimorphic expression of genes has reported that the molecular-level effects of gender influence treatment response and prognosis in various cancers. Hence, there is a need to develop “gender-specific” models that are prognostic of patient outcome to potentially assist in building comprehensive and patient-centric treatment plans. In the field of neuro-oncology, robust and consistent evidence shows that males are almost twice as likely to develop Glioblastoma (GBM), a rapidly fatal primary brain tumor, compared to females (1.5:1). Even though the visual appearance of different male and female tumor phenotypes on MRI look similar, there nonetheless might be subtle sub-visual cues reflective of the differences in the micro-architectural appearance, that are not visible to the naked human eye. Radiomics can provide a surrogate mechanism to non-invasively characterize these GBM tumor by capturing sub-visual cues of morphologic diversity (e.g. roughness, image homogeneity, regularity and edges) on routine MRI scans. It is also critical to identify the molecular associations of these radiomic features in gender controlled cohorts with underlying signalling pathways that drive different biological processes, via radiogenomic analysis. Such cross-scale associations using radiomics and radiogenomics in GBM could allow for designing gender-specific personalized treatments.
Niha Beig is currently a PhD researcher in Brain Image Computing Laboratory at Case Western Reserve University, Cleveland Ohio, USA. Her research focus is on developing assistive diagnostic tools in the field of neuro-oncology using data science and machine learning techniques. She has been working on drawing insights from quantitative medical images and genomic sequences to help distinguish various subtypes of cancers in adult and pediatric gliomas. She has over 30 peer-reviewed abstracts and publications in the field of radiomics and radiogenomics.
Niha is currently an elected member of the Sigma Xi: Scientific Research Honor Society and has also served as an area chair for Women in Machine Learning Workshop in 2018 and 2019.