Lung Adenocarcinoma Evolution H&E Pathomic Feature Analysis Dataset
Published: 31 July 2023| Version 1 | DOI: 10.17632/7zc56ttd96.1
Contributors:
Pingjun Chen, , , , , , , Description
We curated three distinct cohorts (Japan, China, and the USA) covering 98 patients, 162 slides, and 669 regions of interest (ROI), including 143 Normal, 129 AAH, 94 AIS, 98 MIA, and 205 ADC. We employed state-of-the-art artificial intelligence (AI) techniques to robustly segment and recognize cells on routinely used H&E histopathology images and extracted nine biology-relevant pathomic features to decode lung preneoplasia evolution.
Files
Steps to reproduce
Refer to the code repository https://doi.org/10.5281/zenodo.8188290.
Institutions
University of Texas MD Anderson Cancer Center
Categories
Evolutionary Biology, Biomedical Engineering, Lung Cancer, Medical Image Processing, Digital Pathology, Adenocarcinoma
Funding
University of Texas MD Anderson Cancer Center
P30 CA01667
National Institutes of Health
R00CA218667
National Institutes of Health
R01CA234629