Artificial Intelligence and Rectal Cancer: Beyond Images
by
, , , , and
Tommaso Novellino
1,*
,
Carlotta Masciocchi
2,
Andrada Mihaela Tudor
1,
Calogero Casà
1,3,
Giuditta Chiloiro
2,
Angela Romano
1,2,
Andrea Damiani
2,
Giovanni Arcuri
2,
Maria Antonietta Gambacorta
1,2
and
Vincenzo Valentini
3 1
Department of Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
2
Centro di Medicina dell’Invecchiamento, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy
3
Ospedale Isola Tiberina—Gemelli Isola, 00186 Rome, Italy
*
Author to whom correspondence should be addressed.
Cancers 2025, 17(13), 2235; https://doi.org/10.3390/cancers17132235
Submission received: 21 May 2025
/
Revised: 25 June 2025
/
Accepted: 27 June 2025
/
Published: 3 July 2025
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
Simple Summary
The cancer burden, particularly in rectal cases, can be alleviated through the use of artificial intelligence models, provided they are properly designed, implemented, and validated. Artificial intelligence encompasses machine learning, which in turn includes deep learning. Artificially intelligent models can be developed based on various types of data, including images, numerical values, and texts. We believe there is currently considerable hype around image-based models, and that more intensive exploration of other data types—such as electronic health records and omics—could greatly enhance both research and clinical practice. By analyzing the literature, we confirm this idea and offer some recommendations that we ultimately consider beneficial for patients, especially by promoting multimodal approaches beyond simply imaging.
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.
