Research presented today suggests an artificial intelligence tool called DeepGEM may provide an advancement in genomic testing that offers an accurate, cost-effective, and timely method for gene mutation prediction from histopathology slides. The research was presented today at the IASLC 2024 World Conference on Lung Cancer by Professor Wenhua Liang, from the China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, China. Accurate detection of driver gene mutations is essential for effective treatment planning and prognosis prediction in lung cancer .
Traditional genomic testing , which relies on high-quality tissue samples, is often time-consuming and resource-intensive, limiting accessibility, particularly in low-resource settings . Addressing this gap, Prof. Liang and his team used DeepGEM, which uses routinely acquired histology slides to predict gene mutations, significantly enhancing accessibility and efficiency in mutation screening.
Prof. Liang and his colleagues analyzed datasets from 16 centers and 3,658 patients. This dataset, which includes paired pathological images and gene mutation data, was complemented by publicly available datasets from The Cancer Genome Atlas.
DeepGEM was initially trained and evaluated on an internal dataset of 1,717 patients and subsequently tested on an external dataset from 15 additional centers with 1,719 patients and .