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Artificial Intelligence in Computational Pathology for Cancer Diagnosis
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School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
College of Computer Science and Software Engineering, Hohai University, Nanjing, China
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Artificial Intelligence in Computational Pathology for Cancer Diagnosis

Abstract submission deadline
30 October 2026
Manuscript submission deadline
5 January 2027
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1053

Topic Information

Dear Colleagues,

Artificial intelligence is rapidly transforming computational pathology, revolutionizing cancer diagnosis through the advanced analysis of digitized histopathology images. The integration of deep learning methods, including convolutional neural networks and foundation models, has enabled unprecedented capabilities in tissue classification, tumor detection, biomarker prediction, and prognostic assessment. Recent developments in vision transformers and foundation models trained on millions of whole-slide images demonstrate remarkable performance in both common and rare cancer detection, while multimodal AI approaches are advancing precision oncology by integrating histomorphological features with genomic and clinical data.

This Topic aims to showcase cutting-edge research and comprehensive reviews on the application of artificial intelligence in computational pathology for cancer diagnosis. We welcome original contributions that address diagnostic accuracy, prognostic modeling, biomarker discovery, tumor microenvironment analysis, and clinical integration of AI-driven tools. The scope encompasses diverse cancer types and computational approaches, from traditional machine learning to state-of-the-art foundation models, emphasizing clinically validated methodologies that enhance diagnostic workflows and support precision medicine.

In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Deep learning architectures for histopathology image analysis;
  • Foundation models and self-supervised learning in digital pathology;
  • AI-based tumor detection, classification, and grading;
  • Computational biomarker discovery and prediction;
  • Prognostic and predictive modeling using histopathology images;
  • Tumor microenvironment characterization and spatial analysis;
  • The multi-modal integration of pathology, genomics, and clinical data;
  • Clinical validation and regulatory aspects of AI in pathology;
  • Explainable AI and interpretability in cancer diagnosis;
  • Quality control and standardization in computational pathology.

We look forward to receiving your contributions and hope to advance this rapidly evolving field.

Dr. Md Mamunur Rahaman
Prof. Dr. Yu-Dong Yao
Dr. Chen Li
Dr. Jinghua Zhang
Topic Editors

Keywords

  • artificial intelligence
  • computational pathology
  • cancer diagnosis
  • deep learning
  • digital pathology
  • foundation models
  • histopathology image analysis
  • biomarker prediction
  • prognostic modeling
  • precision oncology

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Cancers
cancers 		4.8 9.0 2009 17.5 Days CHF 2900 Submit
Current Oncology
curroncol 		3.6 6.1 1994 22.6 Days CHF 2200 Submit
Diagnostics
diagnostics 		3.8 6.9 2011 20.4 Days CHF 2600 Submit

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Published Papers (2 papers)

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Article
Assessing the Clinical Relevance of BRCA1 RING Domain Variants of Uncertain Significance
by Matthew D. Martin, Gabriella C. Torretto, Kaamraan Islam, Nicole E. Archer, Harriet E. Feilotter and Scott K. Davey
Curr. Oncol. 2026, 33(7), 399; https://doi.org/10.3390/curroncol33070399 - 3 Jul 2026
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Abstract
The BRCA1 protein serves an essential function in maintaining genomic integrity, to the extent that up to 80% of women carrying a pathogenic BRCA1 variant develop breast cancer (BC). Most of these carriers would benefit from prophylactic care, but genetic screens that uncover [...] Read more.
The BRCA1 protein serves an essential function in maintaining genomic integrity, to the extent that up to 80% of women carrying a pathogenic BRCA1 variant develop breast cancer (BC). Most of these carriers would benefit from prophylactic care, but genetic screens that uncover variants of uncertain significance (VUSs) do not provide insight on disease risk or clinical decision-making. In accordance with guidelines established by The American College of Molecular Genetics (ACMG) and Association for Molecular Pathology (AMP), this study produced computational and functional evidence to inform the reclassification of BRCA1 VUSs as pathogenic or benign, with a specific focus on the abundant subset of missense variants within the RING domain. A six-feature linear support vector machine (LSVM) specifically trained on BRCA1 RING variants performed well (84% accurate in predicting in vitro binding loss) and provided supporting classification evidence for 322 VUS. A mammalian cell co-immunoprecipitation (co-IP) assay that quantified the binding between variant BRCA RING constructs and endogenous BARD1 provided corroborating strong evidence for nine VUSs and correlated with a homology-directed repair (HDR) assay by Starita et al. (p = 0.04). The combined evidence warrants the reclassification of three VUSs as likely benign (N16S, A17D, and E100D) and one as likely pathogenic (H41P), and underscores the promise of domain-specific approaches for missense VUS reclassification. Full article
(This article belongs to the Topic Artificial Intelligence in Computational Pathology for Cancer Diagnosis)
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27 pages, 2607 KB  
Review
Computer Vision for Predicting the Efficacy of Neoadjuvant Therapy in Breast Cancer
by Daria Sitnikova, Alexey Fayzullin, Fedor Chistov, Peter Timashev and Nikita Savelov
Cancers 2026, 18(11), 1857; https://doi.org/10.3390/cancers18111857 - 5 Jun 2026
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Abstract
Neoadjuvant therapy (NAT) is a standard component of breast cancer treatment, yet response rates vary substantially across patients. Accurate prediction of pathological complete response remains an unmet clinical need to improve patient selection for NAT. This review summarizes current approaches of using computer [...] Read more.
Neoadjuvant therapy (NAT) is a standard component of breast cancer treatment, yet response rates vary substantially across patients. Accurate prediction of pathological complete response remains an unmet clinical need to improve patient selection for NAT. This review summarizes current approaches of using computer vision to predict breast cancer response to NAT from histopathological slides. We examined studies employing computer vision and machine learning models on hematoxylin and eosin and immunohistochemically stained whole-slide images, focusing on morphological features of tumor cells, stroma and tumor-infiltrating lymphocytes associated with pathological complete response. Key morphological predictors of therapy resistance included low tumor cell density with cord-like patterns, necrosis, predominance of collagenous and fibroblast-rich stroma and tumor vascularization, while therapy sensitivity was associated with high nuclear staining intensity, high tumor cell density and lymphocyte infiltration. We highlighted the advantages of incorporating multimodal data to enhance predictive performance. Our analysis demonstrates that computer vision models can detect subtle morphological patterns that may be difficult for pathologists to evaluate, providing valuable insights for personalized therapy planning in breast cancer. Further development of cross-modal, interpretable artificial intelligence solutions may improve prediction accuracy and deepen our understanding of tumor biology relevant to NAT response. Full article
(This article belongs to the Topic Artificial Intelligence in Computational Pathology for Cancer Diagnosis)
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