AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides

TL;DR

This study introduces SmartPath, an AI-powered system that assists pathologists in optimizing tissue extraction from FFPE slides, improving DNA yield and tumor purity for next-generation sequencing, thereby reducing waste and costs.

Contribution

The paper presents a novel AI-augmented review system that quantitatively guides tissue dissection, improving DNA yield predictions and optimizing slide selection compared to traditional methods.

Findings

SmartPath increased DNA yields within target range by 25%.

The system recommended fewer slides for large tissue sections, saving tissue.

SmartPath helped prevent re-extraction by suggesting more slides for scant tissue samples.

Abstract

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.