CDS PhD Seminar Series with Lingyi Xu

Abstract: Molecular and cellular alterations to the normal pseudostratified columnar bronchial epithelium results in the development of bronchial premalignant lesions through a histologic progression from normal to hyperplasia, metaplasia, dysplasia, carcinoma in situ and invasive carcinoma. Endobronchial biopsies obtained via various bronchoscopy techniques are formalin fixed paraffin embedded, and hematoxylin and eosin stained (H&E) to access the pathologic features and histologic grade of the tissue. The broad and continuous spectrum of histologic and molecular changes makes reproducible stratification of lesions across multiple studies challenging. Here we proposed a transformer-based framework that flexibly utilizes transcriptomic and histologic patterns to distinguish lesions with bronchial dysplasia or worse from normal, hyperplasia, and metaplasia. We leveraged H&E whole slide images of endobronchial biopsies and bulk gene expression data from previously published studies as well as new data obtained from high-risk patients. Our framework maximizes the use of training data by allowing sample inputs with one or both data modalities. The flexibility of our framework to make predictions when a data modality is missing and its ability to integrate data from different modalities and studies is important for advancing our stratification of bronchial premalignant lesions. Bio: Lingyi is a PhD student in Computing & Data Sciences at Boston University. Her research investigates the potential of multimodal medical data in enhancing disease diagnosis and assessment. Her current work involves applying graph models and machine learning algorithms in digital pathology and cancer genomics to advance the understanding of lung precancerous conditions.