GenAI models extract pathological features for lung adenocarcinoma grading and prognosis

(Press-News.org)  Lung adenocarcinoma remains one of the most challenging cancers to diagnose accurately, with pathologists spending countless hours examining tissue samples under microscopes to determine cancer grades and predict patient outcomes. A new study published in the International Journal of Surgery demonstrates how generative artificial intelligence could fundamentally change this process, offering both speed and precision that rivals human expertise.

Dr. Anqi Lin and his research team at Southern Medical University's Zhujiang Hospital put three cutting-edge GenAI models to the test: GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro. Working with 310 diagnostic slides from The Cancer Genome Atlas databases and an additional 182 slides from independent medical institutions, they discovered something remarkable—these GenAI systems could identify cancer patterns and grade tumors with striking accuracy.

"What surprised us most was not just the accuracy, but the consistency," notes Dr. Peng Luo, co-corresponding author of the study. "Claude-3.5-Sonnet achieved an average accuracy of 82.3% in distinguishing different cancer grades, and when we tested it repeatedly on the same samples, it maintained reliable performance."

The breakthrough extends beyond simple diagnosis. The research team developed a sophisticated prognostic model that combines GenAI-extracted pathological features with clinical information, successfully predicting patient outcomes across multiple validation studies. Their model identified 11 key histological features and 4 clinical variables that together provide a comprehensive risk assessment for patients.

Perhaps most significantly, the GenAI models accomplished in minutes what typically requires extensive pathologist time and expertise. The system quantifies features like tumor necrosis, cellular patterns, and inflammatory responses with precise percentages, replacing subjective descriptions with objective measurements. Beyond these efficiency gains, the implications address critical challenges in modern pathology practice. In resource-limited settings where experienced pathologists are scarce, these GenAI models could provide standardized, high-quality diagnostic support, potentially democratizing access to expert-level pathological assessment. The technology also tackles the persistent issue of inter-observer variability—while different pathologists might disagree on subtle pattern distinctions, the GenAI models provide consistent, reproducible assessments across multiple evaluations. Furthermore, the GenAI system's ability to simultaneously analyze multiple histological features creates opportunities for discovering previously unrecognized prognostic patterns. The research revealed that interstitial fibrosis, papillary patterns, and lymphocytic infiltration emerged as the most significant prognostic factors when analyzed through GenAI-assisted quantification. This level of systematic feature analysis would be nearly impossible to achieve consistently through traditional manual assessment, potentially uncovering new insights into cancer biology and patient outcomes that could reshape treatment strategies.

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