The differentiation between major histological types of lung cancer, such as 
adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer 
(SCLC) is of crucial importance for determining optimum cancer treatment. 
Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy 
(TBLB) are one of the primary sources for making a diagnosis; however, a subset 
of cases present a challenge for pathologists to diagnose from H&E-stained 
slides alone, and these either require further immunohistochemistry or are 
deferred to surgical resection for definitive diagnosis. We trained a deep 
learning model to classify H&E-stained Whole Slide Images of TBLB specimens into 
ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained 
model was capable of classifying an independent test set of 83 challenging 
indeterminate cases with a receiver operator curve area under the curve (AUC) of 
0.99. We further evaluated the model on four independent test sets-one TBLB and 
three surgical, with combined total of 2407 WSIs-demonstrating highly promising 
results with AUCs ranging from 0.94 to 0.99.