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Cancers
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Advances in Artificial Intelligence Methodologies and Applications in Cancer
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Advances in Artificial Intelligence Methodologies and Applications in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 15 March 2026 | Viewed by 580

Special Issue Editors

Dr. Jennifer G. Le-Rademacher

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
Interests: cancer; clinical trial design; survival analysis; patient-reported outcomes; complex data; symptom management; adverse event
Dr. Nicholas Larson

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
Interests: statistical genetics; bioinformatics and computational biology; machine learning; pharmacogenomics; cancer; cardiovascular disease

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is not new, having been introduced in the 1950s. Recent advances in computational power, algorithms, and data storage have significantly enhanced its application in various fields, including medicine and health. AI's ability to process and analyze vast amounts of data is transforming healthcare, particularly cancer research and patient care. AI-powered diagnostic tools can detect the early signs of cancer from medical imaging with remarkable precision, reducing human error and expediting diagnosis to achieve timely intervention. AI tools can identify mutations and biomarkers to enhance diagnosis and predict treatment responses.

However, it is essential to understand AI's limitations and address ethical considerations to ensure that these technologies benefit all patients. Understanding AI's impact and ensuring that it is used responsibly is crucial for advancing cancer research and patient care.

This Special Issue aims to highlight advances in the application of AI, providing insights into the transformative potential of AI in cancer research and patient care. The scope of this Special Issue includes methodological and technical advances, innovative applications, case studies of AI in action, ethical considerations, challenges, and solutions regarding the utilization of AI technology in cancer.

Dr. Jennifer G. Le-Rademacher
Dr. Nicholas Larson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • AI tools
  • cancer research
  • diagnostic tools
  • medical imaging
  • biomarkers
  • ethical considerations
  • patient care

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Published Papers (1 paper)

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Review

16 pages, 430 KiB  
Review
Artificial Intelligence and Rectal Cancer: Beyond Images
by Tommaso Novellino, Carlotta Masciocchi, Andrada Mihaela Tudor, Calogero Casà, Giuditta Chiloiro, Angela Romano, Andrea Damiani, Giovanni Arcuri, Maria Antonietta Gambacorta and Vincenzo Valentini
Cancers 2025, 17(13), 2235; https://doi.org/10.3390/cancers17132235 - 3 Jul 2025
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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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Methodologies and Applications in Cancer)
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