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Digital Pathology in Precision Oncology: Emerging Tools for Diagnosis and...
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Digital Pathology in Precision Oncology: Emerging Tools for Diagnosis and Treatment Guidance

A special issue of Diseases (ISSN 2079-9721). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 1308

Special Issue Editors

Dr. Daniela Hartmann

E-Mail Website
Guest Editor
1. Department of Dermatology and Allergy, University Hospital, LMU Munich, 80336 Munich, Germany
2. Department of Dermatology and Allergy, Munich Municipal Hospital, 80336 Munich, Germany
Interests: dermatology; dermatosurgery; dermato-oncology; melanoma and non-melanoma skin cancer; non-invasive diagnostics in dermatology; dermatopathology
Special Issues, Collections and Topics in MDPI journals
Dr. Maximilian Deussing

E-Mail Website
Guest Editor
Department of Dermatology, University of Munich, 80336 Munich, Germany
Interests: dermatology; dermatosurgery; digital imaging; applied artificial intelligence; optical coherence tomography; confocal fluorescence microscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the forthcoming Special Issue of Cancers, entitled Digital Pathology in Precision Oncology: Emerging Tools for Diagnosis and Treatment Guidance. This Special Issue will focus on the transformative impact of artificial intelligence (AI) and advanced computational methodologies on biomedical diagnostics and treatment-related decision-making.

As Guest Editors, we are pleased to invite researchers, clinicians, and thought leaders from oncology, pathology, and related disciplines to contribute original research articles, comprehensive reviews, and case-based reports.

This Special Issue seeks to showcase pioneering work that applies AI algorithms, machine learning models, and computational pathology tools to precision oncology. We look forward to receiving your contributions, which will aid in advancing the scientific discourse in and shaping the future of precision oncology.

You may choose our Joint Special Issue in Cancers.

Dr. Daniela Hartmann
Dr. Maximilian Deussing
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 250 words) can be sent to the Editorial Office for assessment.

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. Diseases is an international peer-reviewed open access monthly 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 1800 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

  • oncology
  • digital pathology
  • ex vivo confocal microscopy
  • artificial intelligence
  • machine learning
  • virtual biopsy
  • teledermatology
  • histopathological images
  • image analysis algorithms

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Published Papers (1 paper)

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Research

18 pages, 2686 KB  
Article
MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification
by Phisit Katongtung, Kanokwatt Shiangjen, Watcharaporn Cholamjiak and Krittin Naravejsakul
Diseases 2026, 14(2), 45; https://doi.org/10.3390/diseases14020045 - 28 Jan 2026
Viewed by 817
Abstract
Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep [...] Read more.
Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance. Full article
(This article belongs to the Special Issue Digital Pathology in Precision Oncology: Emerging Tools for Diagnosis and Treatment Guidance)
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