  With the advanced imaging technology, digital pathology imaging of tumor
tissue slides is becoming a routine clinical procedure for cancer diagnosis.
This process produces massive imaging data that capture histological details in
high resolution. Recent developments in deep-learning methods have enabled us
to automatically detect and characterize the tumor regions in pathology images
at large scale. From each identified tumor region, we extracted 30 well-defined
descriptors that quantify its shape, geometry, and topology. We demonstrated
how those descriptor features were associated with patient survival outcome in
lung adenocarcinoma patients from the National Lung Screening Trial (n=143).
Besides, a descriptor-based prognostic model was developed and validated in an
independent patient cohort from The Cancer Genome Atlas Program program
(n=318). This study proposes new insights into the relationship between tumor
shape, geometrical, and topological features and patient prognosis. We provide
software in the form of R code on GitHub:
https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.
