In the field of digital pathology, a frequent approach for the creation of image-based biomarkers involves extracting features from scanned pathology slides. These features, which are often related to the morphology or spatial distribution of various tissue or cell types, provide valuable insights into the underlying biology of diseases. In cancer research, it is particularly important to examine how these features correlate with clinical outcomes such as overall survival (OS), progression-free survival (PFS), or other binary outcomes (e.g., response to a specific treatment).
Here we release python code that can be executed in a notebook to facilitate this process. It accepts a pandas DataFrame and generates a one-page summary PDF file, facilitating the analysis of individual features and their potential correlation with clinical outcomes.Continue reading Data Exploration Of Features For Outcome Association In Digital Pathology