Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

癌症是全世界死亡的主要原因。不幸的是，复杂的异质肿瘤微环境 (TME) 混淆了了解这种疾病的努力。更好地了解 TME 可能会带来新的诊断、预后和治疗发现。实现这一目标的一种方法涉及体外肿瘤模型，该模型概括了体内TME 组成和空间排列。在这里，我们回顾了利用体外的潜力肿瘤模型和人工智能来描绘 TME。这包括 (i) 识别新特征，(ii) 研究高阶关系，以及 (iii) 以 (iv) 整体、客观、可重复和有效的方式分析和解释多组学数据，这超越了以前的 TME 方法分析。我们还讨论了这种方法的局限性，即数据集不足、生物相关性不确定、伦理问题和后勤限制；最后，我们推测未来可以克服这些限制的研究途径，最终转化为改善的临床结果。