Artificial Intelligence and Rectal Cancer: Beyond Images

Abstract

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.

Keywords: artificial intelligence; big data; combined models; deep learning; digital medicine; electronic health records; images; machine learning; multivariate models; personalized medicine; precision medicine; predictive models; real-world data; rectal cancer; structured data; unstructured data.

Figure 1

Schematic representation of a typical artificial neural network, with multiple hidden layers (i.e., deep learning). Circles represent artificial neurons.