Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is
indispensable for its diagnosis. However, human evaluation of pathology slides cannot
accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and
eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous
cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images
from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image …
indispensable for its diagnosis. However, human evaluation of pathology slides cannot
accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and
eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous
cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images
from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image …
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