Impact of AI-augmented histopathology review on next-generation sequencing (NGS) success.

Abstract

1618 Background: Insufficient nucleic acid quantity (QNS) can compromise NGS success (QNS rates can be >20%), necessitating re-biopsies and delaying therapy. While interventions like extended de-crosslinking (EXT) can rescue samples with low total nucleic acid (TNA) yield tissues, they prolong processing times, demanding a precise method to identify samples requiring such processing. We developed an AI system, Paige Predict (PP), that analyzes digitized H&E slides to predict NGS QNS and recommend tissue input quantity. This study evaluates PP in two contexts: (1) optimizing internal lab workflows by selectively routing high-risk samples to EXT, and (2) exploring its utility in an external setting, Cedars-Sinai (CS), to triage samples for comprehensive Tempus genomic profiling (CGP), requiring >50ng input, vs targeted low-input assays (>5ng) performed at CS. Methods: We conducted a validation study in the Tempus lab comparing a baseline period prior to introduction of EXT (July 2024 - Sept 2024 n=17,026) against an intervention period (Oct 2025 - Nov 2025, n=12,975) where PP automatically routed at-risk samples to EXT. To evaluate external utility, we also performed a retrospective analysis on a cohort of CS pts. We assessed PP’s ability to predict CGP QNS, and compared it against pathologist assessment for inter-institutional sample referral. Results: In the validation study, PP-guided EXT routing reduced joint DNA+RNA QNS rates by 19.6%, with a number needed to test (NNT; 1/Absolute Rate Reduction) indicating that for every 40.2 pts, one received a result that would otherwise not have. RNA QNS rates reduced by 15.9% (NNT 63.7) relative to the baseline period. This reduction was achieved while decreasing net tissue input by 16.7% and increasing TNA yields in the optimal range (100–1500ng) by 19.0% (NNT 10.1). Similar benefits were observed in the NSCLC subset. In the external CS cohort, PP could have triaged 74% of samples that failed Tempus CGP to low input sequencing at CS. Conversely, PP indicated that over 70% of samples sequenced at CS could have succeeded with Tempus CGP. Conclusions: PP significantly reduces NGS failure rates and improves tissue stewardship by deploying rescue workflows only when necessary. These analyses lay the groundwork for a future multi-institution prospective trial and establish PP as a scalable AI tool to amplify access to precision oncology. (Tempus) EXT routing validation metrics (N=12,975 all / 3,530 NSCLC) PP % change vs. baseline (p-value) / NSCLC subset NNT vs. baseline / NSCLC subset (Cedars-Sinai) retrospective analysis metrics(N=1,082) PP % flagged for QNS (50ng) DNA+RNA QNS rate -19.6% (2.0e-9) / -30.4% (8.6e-9) 40.2 / 24.4 DNA QNS 74% DNA pass & RNA QNS rate -15.9% (1.1e-4) / -3.1% (7.0 e-1) 63.7 / 384.6 DNA Pass 16.4% Optimal TNA range +19.0% (2.0e-12) / +21.0% (5.3e-11) 10.1 / 9.0 Not referred 28.6% Median N input slides -16.7% (3.0e-11) / -14.3% (6.4e-7) N/A