South Korea-based Lunit plans to highlight scientific findings related to its Lunit Scope Suite for precision oncology at the upcoming American Society of Clinical Oncology (ASCO) 2024 annual meeting in Chicago.
The studies include comprehensive histopathomic prediction models for early breast cancer and hypothetical test-and-control group generation for treatment selection in non-small cell lung cancer (NSCLC).
In the area of breast cancer, Lunit will present findings of recent HER2 studies, including ultra-low expression in breast cancer using AI-based quantification, and subcellular quantification from HER2 immunohistochemistry (IHC) images.
For NSCLC, Lunit will present findings from its deep learning-based model that integrates chest CT and histopathology analysis for predicting immunotherapy response, as well as its analysis of tertiary lymphoid structures in hematoxylin and eosin stain (H&E) whole-slide images to identify predictive biomarkers and predict immunotherapy response.
Toward precision oncology in skin cancer, a presentation will focus on a collaborative study that examined the association of immune phenotypes with outcomes after immunotherapy in metastatic melanoma, and it will highlight findings on the heterogeneity of immune phenotypes across melanoma subtypes, according to the firm.












![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)





