  To achieve minimum DNA input and tumor purity requirements for
next-generation sequencing (NGS), pathologists visually estimate
macrodissection and slide count decisions. Misestimation may cause tissue waste
and increased laboratory costs. We developed an AI-augmented smart pathology
review system (SmartPath) to empower pathologists with quantitative metrics for
determining tissue extraction parameters. Using digitized H&E-stained FFPE
slides as inputs, SmartPath segments tumors, extracts cell-based features, and
suggests macrodissection areas. To predict DNA yield per slide, the extracted
features are correlated with known DNA yields. Then, a pathologist-defined
target yield divided by the predicted DNA yield/slide gives the number of
slides to scrape. Following model development, an internal validation trial was
conducted within the Tempus Labs molecular sequencing laboratory. We evaluated
our system on 501 clinical colorectal cancer slides, where half received
SmartPath-augmented review and half traditional pathologist review. The
SmartPath cohort had 25% more DNA yields within a desired target range of
100-2000ng. The SmartPath system recommended fewer slides to scrape for large
tissue sections, saving tissue in these cases. Conversely, SmartPath
recommended more slides to scrape for samples with scant tissue sections,
helping prevent costly re-extraction due to insufficient extraction yield. A
statistical analysis was performed to measure the impact of covariates on the
results, offering insights on how to improve future applications of SmartPath.
Overall, the study demonstrated that AI-augmented histopathologic review using
SmartPath could decrease tissue waste, sequencing time, and laboratory costs by
optimizing DNA yields and tumor purity.
