Abstract 1147P
Background
Cancer diagnosis and treatment rely on accurate identification of morphological, molecular, and genetic biomarkers. As technology and science progress, the number of biomarkers and testing required increases. Professional guidelines structure the biomarkers testing methodology. Yet, testing capabilities, options, and interpretation vary significantly between countries and medical centers in practice. Here, we present an alternative to the traditional methodologies for biomarker detection, using neural networks to detect the different biomarkers directly from hematoxylin and eosin (H&E) stained pathology slide images.
Methods
H&E slide images were scanned in X40 resolution to generate a molecular model using advanced computer vision and neural network algorithms. Internal and publicly available databases were used to train and validate different tissue panels and specific biomarkers, including mutations, fusions, expression, and different cancer signatures.
Results
Thyroid panel validation for NTRK, RET-fusions &, BRAF mutation resulted in 100%, 100% and 91.7% sensitivity and 93.9%, 95.7% & 90.7% specificity, respectively (n=102). Similar results were obtained for the lung cancer panel of actionable alterations tested and MSI signature.
Conclusions
Our validation process reveals the potential of combining artificial intelligence methodologies with pathology and oncology domains. It allows accurate, fast, and accessible testing for all patients with optimal tissue utilization and detection of rare alterations. The concept of inferring such alterations directly from the phenotype presents a significant advantage by ignoring variants of uncertain significance (VUS) and looking only at alterations that prove their relevance by changing the cells’ morphological signature.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Imagene-AI.
Funding
Imagene-AI.
Disclosure
N. Peled: Financial Interests, Personal, Advisory Board: Imagene-AI. J. Zalach: Financial Interests, Personal, Ownership Interest: Imagene-AI; Financial Interests, Personal, Full or part-time Employment: Imagene-AI; Financial Interests, Personal, Stocks/Shares: Imagene-AI. I. Hayun: Financial Interests, Personal, Full or part-time Employment: Imagene-AI. N. Paz-Yaacov: Financial Interests, Personal, Full or part-time Employment: Imagene-AI; Financial Interests, Personal, Stocks/Shares: Imagene-AI.