While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

中文翻译：

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人工智能可以告诉我们什么关于疾病的分子机制？


虽然正电子发射断层扫描或单光子发射计算机断层扫描的分子成像已经在宏观尺度上报告了肿瘤分子机制，但越来越多的证据表明医学图像中存在多种附加特征，可以进一步改善肿瘤特征、治疗预测和预后。早期报告已经揭示了放射组学在个性化和改善患者管理和结果方面的力量。目前尚不清楚的是这些额外的指标如何与疾病的潜在分子机制相关。此外，以快速、可重复和透明的方式处理来自医学图像等的越来越多的数据的能力对于未来的临床实践至关重要。在这里，人工智能（AI）可能会产生影响。人工智能涵盖了计算机执行的广泛“智能”功能，包括语言处理、知识表示、问题解决和规划。虽然基于规则的算法（例如计算机辅助诊断）自 20 世纪 90 年代以来一直用于医学成像，但人们对人工智能重新燃起的兴趣与计算能力的提高和机器学习 (ML) 的进步有关。在这篇综述中，我们考虑了为什么分子和细胞过程令人感兴趣，以及哪些过程已经接触到文献中报道的人工智能和机器学习方法。非小细胞肺癌作为最常见的肿瘤类型，被用作本次综述的范例和重点，其中人工智能和机器学习方法已经过测试，并说明了一些概念。