Machine Learning Can Reliably Distinguish Histological Patterns of Micropapillary and Solid Lung Adenocarcinomas

The World Health Organization recommends subclassification of lung cancer according to the percentages of histologic subtypes within a tumor. The manual quantification of lung tumor composition is very time consuming, but it can potentially be aided by a machine learning application. We have updated our previously developed methodology to segment and distinguish solid and micropapillary lung tumor subtypes. Binary tumor masks delineated by machine learning were defined by the mean area of binary objects and by the number of objects found in an image frame. These two features distinguished solid (\(n=31\)) and micropapillary (\(n=61\)) histologic subtypes with excellent performance (\(p<4.04{\textsc {e}}{\text {-}}19\)) for three different frame sizes. Our method to quantify tumor growth patterns applied to histological images of lung adenocarcinoma, demonstrates for the first time that it is feasible to quantify the composition of histological subtypes in individual lung cancers.