  The standard diagnostic procedures for targeted therapies in lung cancer
treatment involve histological subtyping and subsequent detection of key driver
mutations, such as EGFR. Even though molecular profiling can uncover the driver
mutation, the process is often expensive and time-consuming. Deep
learning-oriented image analysis offers a more economical alternative for
discovering driver mutations directly from whole slide images (WSIs). In this
work, we used customized deep learning pipelines with weak supervision to
identify the morphological correlates of EGFR mutation from hematoxylin and
eosin-stained WSIs, in addition to detecting tumor and histologically subtyping
it. We demonstrate the effectiveness of our pipeline by conducting rigorous
experiments and ablation studies on two lung cancer datasets - TCGA and a
private dataset from India. With our pipeline, we achieved an average area
under the curve (AUC) of 0.964 for tumor detection, and 0.942 for histological
subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA
dataset. For EGFR detection, we achieved an average AUC of 0.864 on the TCGA
dataset and 0.783 on the dataset from India. Our key learning points include
the following. Firstly, there is no particular advantage of using a feature
extractor layers trained on histology, if one is going to fine-tune the feature
extractor on the target dataset. Secondly, selecting patches with high
cellularity, presumably capturing tumor regions, is not always helpful, as the
sign of a disease class may be present in the tumor-adjacent stroma.
