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Cells
Special Issues
The Artificial Intelligence to the Rescue of Cancer Research
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The Artificial Intelligence to the Rescue of Cancer Research

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 725

Special Issue Editors

Dr. Asunción Espinosa-Sánchez

E-Mail Website
Guest Editor
Instituto de Biomedicina de Sevilla, CSIC, HUVR, Universidad de Sevilla, 41013 Seville, Spain
Interests: cancer; tumorigenesis; stemness; artificial intelligence; precision medicine
Dr. Amancio Carnero

E-Mail Website
Guest Editor
Instituto de Biomedicina de Sevilla, Consejo Superior de Investigaciones Científicas, Avda. Manuel Siurot s/n, 41013 Seville, Spain
Interests: cancer stem cells; immortalization; therapy resistance; tumor evolution; cellular senescence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer remains one of the main causes of mortality worldwide, despite significant advances in diagnosis and treatment. In recent years, artificial intelligence (AI) has emerged as a revolutionary tool in cancer research, offering innovative approaches to early detection, personalized therapy, and improved patient outcomes. The management of large amounts of data, including medical imaging, genomic profiles, and clinical records, allows AI to enhance our understanding of cancer biology, enable more accurate diagnoses, and optimize treatment strategies. This progress is driven by advances in machine learning and deep learning, among other techniques, which allow researchers to extract complex patterns, integrate biomedical information, and develop precise predictive models.

This Special Issue will explore the latest developments in AI applications for cancer research, including early detection and screening, predictive modeling for treatment responses, personalized medicine approaches, the integration of multi-omics data for comprehensive cancer profiling, AI-driven drug discovery, and ethical considerations regarding the use of AI in oncology.

Dr. Asunción Espinosa-Sánchez
Dr. Amancio Carnero
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 short 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. Cells 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 2700 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

  • cancer research
  • artificial intelligence
  • personalized medicine
  • multi-omics data integration
  • AI-driven drug discovery
  • precision medicine

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Published Papers (1 paper)

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Research

23 pages, 19280 KiB  
Article
Recognizing Epithelial Cells in Prostatic Glands Using Deep Learning
by Liton Devnath, Puneet Arora, Anita Carraro, Jagoda Korbelik, Mira Keyes, Gang Wang, Martial Guillaud and Calum MacAulay
Cells 2025, 14(10), 737; https://doi.org/10.3390/cells14100737 - 18 May 2025
Cited by 1 | Viewed by 482
Abstract
Artificial intelligence (AI) is becoming an integral part of pathological assessment and diagnostic procedures in modern pathology. As most prostate cancers (PCa) arise from glandular epithelial tissue, an AI-based methodology has been developed to recognize glandular epithelial nuclei in prostate biopsy tissue. An [...] Read more.
Artificial intelligence (AI) is becoming an integral part of pathological assessment and diagnostic procedures in modern pathology. As most prostate cancers (PCa) arise from glandular epithelial tissue, an AI-based methodology has been developed to recognize glandular epithelial nuclei in prostate biopsy tissue. An integrated machine-learning network, named GlandNet, was developed to correctly recognize the epithelial cells within prostate glands using cell-centric patches selected from the core biopsy specimens. Feulgen-Thionin (a DNA stoichiometric label) was used to stain biopsy sections (4–7 µm in thickness) from 82 active surveillance patients diagnosed with PCa. Images of these sections were human-annotated, and the resultant dataset consisted of 1,264,772 segmented, cell-centric nuclei patches, of which 449,879 were centered on epithelial gland nuclei from 110 needle biopsies (training set: n = 66; validation set: n = 22; and test set: n = 22). The training of GlandNet used semi-supervised machine-learning knowledge of the training and validation cohorts and integrated both human and AI predictions to enhance its performance on the test cohort. The performance was evaluated against a consensus deliberation from three observers. The GlandNet demonstrated an average accuracy, sensitivity, specificity, and F1-score of 94.1%, 95.7%, 87.8%, and 95.2%, respectively, when tested on the 20,735 glandular cells found in the three needle biopsies with the visually best consensus predictions. Conversely, the average accuracy, sensitivity, specificity, and F1-score were 90.9%, 86.4%, 94.0%, and 89.7% when assessed on 57,217 cells found in the three needle biopsies with the visually worst consensus predictions. GlandNet is a first-generation AI with an excellent ability to differentiate between epithelial and stromal nuclei in core biopsies from patients with early prostate cancer. Full article
(This article belongs to the Special Issue The Artificial Intelligence to the Rescue of Cancer Research)
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