Introduction:The variability of cancers and medical big data can be addressed using artificial intelligence techniques. Artificial intelligence models can accept different input types, including images as well as other formats such as numerical data, predefined categories, and free text. Non-image sources are as important as images in clinical practice and the literature; nevertheless, the secondary literature tends to focus exclusively on image-based inputs. This article reviews such models, using non-image components as a use case in the context of rectal cancer.Methods:A literature search was conducted using PubMed and Scopus, without temporal limits and in English; for the secondary literature, appropriate filters were employed.Results and Discussion:We classified artificial intelligence models into three categories: image (image-based input), non-image (non-image input), and combined (hybrid input) models. Non-image models performed significantly well, supporting our hypothesis that disproportionate attention has been given to image-based models. Combined models frequently outperform their unimodal counterparts, in agreement with the literature. However, multicenter and externally validated studies assessing both non-image and combined models remain under-represented.Conclusions:To the best of our knowledge, no previous reviews have focused on non-image inputs, either alone or in combination with images. Non-image components require substantial attention in both research and clinical practice. The importance of multimodality—extending beyond images—is particularly relevant in the context of rectal cancer and potentially other pathologies.
引言：癌症的多样性和医学大数据问题可通过人工智能技术加以应对。人工智能模型能够处理多种输入类型，包括图像及其他格式数据（如数值数据、预定义分类和自由文本）。在临床实践和文献中，非图像数据源与图像数据具有同等重要性，然而现有综述文献往往仅聚焦于图像输入。本文以直肠癌为研究背景，通过非图像数据应用案例系统评述此类模型。 方法：通过PubMed和Scopus数据库进行文献检索，不设时间限制且限定英文文献；针对综述类文献采用相应筛选条件。 结果与讨论：我们将人工智能模型分为三类：图像模型（基于图像输入）、非图像模型（基于非图像输入）以及融合模型（混合输入）。非图像模型表现出卓越性能，印证了我们关于当前研究过度聚焦图像模型的假设。融合模型常优于单模态模型，这与现有文献结论一致。然而，针对非图像模型与融合模型的多中心外部验证研究仍显不足。 结论：据我们所知，目前尚无专门针对非图像输入（单独或与图像结合）的综述研究。非图像数据在科研与临床实践中均需获得充分重视。多模态方法的重要性——其范畴远超越图像数据——在直肠癌及其他潜在病理学研究中具有特殊意义。
肿瘤(癌症)患者之家