High-confidence AI-based biomarker profiling for H&E slides to optimize pathology workflow in lung cancer.

Abstract

e21207 Background: In recent years AI methodologies have been increasingly adopted in the oncology space as part of the clinical decision process. Our previous research has shown accurate detection of actionable biomarkers based on AI algorithms applied to Hematoxylin & Eosin (H&E) scanned slides. While investigating a wide actionable biomarker panel, this profiling process can also flag patients with no actionable biomarkers. In Non-Small Cell Lung Cancer (NSCLC), EGFR, ALK, and ROS1 are the most commonly tested actionable alterations and the primary exclusion criteria for immunotherapy. Here we present a novel AI-based solution that can accurately and almost instantaneously detects patients without the main actionable biomarkers from the H&E image alone, thus helping prioritize the pathology lab workflow and navigate patients to a faster and more optimal diagnosis route. Methods: A retrospective cohort of 435 NSCLC patients was collected from the Sheba Medical Center pathology department. All cases (from primary and metastatic sites) with documented results for EGFR mutations, ALK, and ROS1-fusions were included. Molecular profiling was based on Oncomine Comprehensive, Dx target, or Solid tumor DNA assays, with 4 cases also supported by the Idylla EGFR mutation test. Deidentified digital whole slide images (WSIs) were run using Imagene’s AI classifier and compared to the officially reported results. The classifier was generated using self-supervised algorithm, combined with multiple instance learning algorithms applied using untagged and tagged FFPE H&E scan (40x or 20x magnification) from Imagene’s internal database (including TCGA research network slides). A categorical prediction (positive/negative) inferred on the validation set by the combined AI classifier model. Results: The combined AI model for detecting EGFR, ALK and ROS1 actionable alteration resulted in 127 patients (29.4% of the cohort) reported with no EGFR actionable mutation, ALK, or ROS1 fusions. Only two patients (1.57% of the negatively detected patients) were found to be false negative ( EGFR: p.G719D and p.L858R), demonstrating 98.43% accuracy. Conclusions: Image-based molecular detection can serve as an accurate and fast detection method to be used in the clinical setting for monitoring and prioritizing current pathology workflow. Specifically, it can detect a major fraction of the non-targetable patients and optimize their diagnostic flow.