{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:49:51Z","timestamp":1778600991527,"version":"3.51.4"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Lung adenocarcinoma (LUAD) is one of the main causes of cancer-related mortality worldwide. Pathological risk factors such as spreading through air spaces, high-risk pathological subtypes, occult lymph nodes, and visceral pleural invasion have significant impact on patient prognosis. In recent years, there has been significant progress in the application of artificial intelligence (AI) technology, e.g., deep learning (DL), in medical image analysis and pathological diagnosis of lung cancer, offering novel approaches for predicting the aforementioned pathological risk factors. This article reviews recent advancements in AI-based analysis and prediction of pathological risk factors in lung adenocarcinoma, with a focus on the applications and limitations of DL models, focusing on studies aimed at improving diagnostic accuracy and efficiency for specific high-risk pathological subtypes. Finally, we summarize current challenges and future directions, emphasizing the need to expand dataset diversity and scale, improve model interpretability, and enhance the clinical applicability of AI models. This article aims to provide a reference for future research on the analysis and prediction of pathological risk factors of LUAD and to promote the development and application of AI, especially DL, in this field.<\/jats:p>","DOI":"10.3389\/fonc.2025.1687360","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T13:44:11Z","timestamp":1763041451000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical 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