Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

Öner, Mustafa Ümit; Chen, Jianbin; Revkov, Egor; James, Anne; Heng, Seow Ye; Kaya, Neslihan Arife; Alvarez, Jacob J.S.; Takano, Angela; Cheng, Xin; Lim, Tony Kiat Hon; Tan, Daniel S.W.; Zhai, Weiwei; Skanderup, Anders Jacobsen; Sung, Wing‐Kin; Lee, Hwee Kuan

doi:10.1016/j.patter.2021.100399

Cited by 7 publications

(1 citation statement)

References 81 publications

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“…These systems can be combined with digital slide marking (digitally guided macrodissection), enabling integration with computer vision models for tumor enrichment 19 , 20 . Several computer vision systems have been recently developed with the goal of estimating tumor-rich dissection areas from histopathology slides to meet tumor purity input requirements for molecular testing 6 , 21 – 24 . However, no recommendation systems exist for estimating tissue quantity for minimum DNA input requirements, and thus even automated dissection systems rely on a pathologist to determine how many slides should be scraped.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

Osinski

BenTaieb

et al. 2022

Modern Pathology

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To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model ( R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per 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 100–2000 ng. The number of extraction attempts was statistically unchanged between cohorts. 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, especially those with degraded DNA, 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. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

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