Abstract LB243: Deep learning-based molecular characterization of lung cancers from never smokers using hematoxylin and eosin-stained whole slide images

Abstract Purpose: This study employs cutting-edge deep learning techniques to comprehensively analyze hematoxylin and eosin-stained (H p. E746A750del = 0. 77) and KRAS (p. G12C = 0. 77). Performance was suboptimal for KRAS p. G12V (0. 41) and KRAS p. G12D (0. 47). Conclusions: Our deep learning network achieves high prediction scores in identifying tumors with critical driver gene alterations and actionable mutations, holding promise for potential clinical use. In the future, the model could be optimized as a screening assay to guide molecular testing and therapeutic management of patients with LCINS. Citation Format: Monjoy Saha, Tongwu Zhang, Praphulla Bhawsar, Wei Zhao, Jianxin Shi, Soo Ryum Yang, Jonas Almeida, Maria Teresa Landi. Deep learning-based molecular characterization of lung cancers from never smokers using hematoxylin and eosin-stained whole slide images abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr LB243.