Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).
John H LockhartHayley D AckermanMyunggyo LeeMahmoud AbdalahAndrew John DavisNicole HackelTheresa A BoyleJames SallerAysenur KeskeKay HänggiBrian H RuffellOlya StringfieldWilliam Douglas CressAik Choon TanElsa R FloresPublished in: NPJ precision oncology (2023)
Preclinical genetically engineered mouse models (GEMMs) of lung adenocarcinoma are invaluable for investigating molecular drivers of tumor formation, progression, and therapeutic resistance. However, histological analysis of these GEMMs requires significant time and training to ensure accuracy and consistency. To achieve a more objective and standardized analysis, we used machine learning to create GLASS-AI, a histological image analysis tool that the broader cancer research community can utilize to grade, segment, and analyze tumors in preclinical models of lung adenocarcinoma. GLASS-AI demonstrates strong agreement with expert human raters while uncovering a significant degree of unreported intratumor heterogeneity. Integrating immunohistochemical staining with high-resolution grade analysis by GLASS-AI identified dysregulation of Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced tumor regions. Our work demonstrates the benefit of employing GLASS-AI in preclinical lung adenocarcinoma models and the power of integrating machine learning and molecular biology techniques for studying the molecular pathways that underlie cancer progression.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- big data
- squamous cell
- high grade
- high resolution
- papillary thyroid
- signaling pathway
- locally advanced
- squamous cell carcinoma
- mouse model
- cell therapy
- endothelial cells
- cell proliferation
- rectal cancer
- neoadjuvant chemotherapy
- single cell
- oxidative stress
- convolutional neural network
- stem cells
- mass spectrometry
- radiation therapy
- low grade
- young adults
- study protocol
- lymph node
- flow cytometry
- pluripotent stem cells