ArticleNPJ precision oncology2023
Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- p53 Drives Lung Cancer Regression through a TSC2/TFEB-dependent Senescence Program.Cancer discovery · 2026Article
- Article
- The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM.Pathologica · 2025Article
- Aging directs the differential evolution of KRAS-driven lung adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- Interleukin-1α release during necrotic-like cell death generates myeloid-driven immunosuppression that restricts anti-tumor immunity.Cancer cell · 2024Article
- Tenascin-C in the early lung cancer tumor microenvironment promotes progression through integrin αvβ1 and FAK.bioRxiv : the preprint server for biology · 2024Article
- A Fast-Tracking Sample Preparation Protocol for Proteomics of Formalin-Fixed Paraffin-Embedded Tumor Tissues.Methods in molecular biology (Clifton, N.J.) · 2024Article
Corrections and comments
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.