Machine Learning Analysis of H&E Lung Adenocarcinoma Tumor Microenvironment Shows Association of Human Interpretable Histopathological Features with KEAP1 Mutations

Here we present machine-learning (ML) algorithms that can predict
KEAP1^MUT directly from routinely available hematoxylin and eosin (H&E)
stained tissue samples. ML-based genomic assessment of the H&E samples
that are routinely collected from each patient has potential to decrease the
cost and turnaround time of genotyping because it requires no additional
tissue collection or processing for genomic sequencing. This could increase
the efficiency of clinical development program and may allow patients to
receive the most effective therapies sooner.