Current and future applications of artificial intelligence in lung cancer and mesothelioma

作者：Joshua Roche, Farzaneh Seyedshahi, Kai Rakovic, Akari Win Thu, John Le Quesne, Kevin G. Blyth · 发表于：Thorax · 年份：2025 · DOI：10.1136/thorax-2024-222054 · 被引用次数：7 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Occupational and environmental lung diseases、Lung Cancer Diagnosis and Treatment

BACKGROUND: Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provision of high-quality care difficult. In this context, artificial intelligence (AI) offers a range of assistive/automated functions that can potentially enhance clinical decision-making, while reducing inequality and pathway delay. AIMS: In this state-of-the-art narrative review, we synthesise evidence on this topic, focusing particularly on tools that ingest routine pathology and radiology images. We summarise the strengths and weaknesses of AI applied to common multidisciplinary team (MDT) functions, including histological diagnosis, therapeutic response prediction, radiological detection and quantification, and survival estimation. We also review emerging methods capable of generating novel biological insights and current barriers to implementation, including access to high-quality training data and suitable regulatory and technical infrastructure. NARRATIVE: Neural networks trained on pathology images have proven utility in histological classification, prognostication, response prediction and survival. Self-supervised models can also generate new insights into biological features responsible for adverse outcomes. Radiology applications include lung nodule tools, which offer critical pathway support for imminent lung cancer screen...