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

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 666

Special Issue Editors


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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

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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 dermatology, 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 and dermatology.

You may choose our Joint Special Issue in Diseases.

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 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. 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

  • dermatology 
  • 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

11 pages, 1113 KB  
Article
Rapid Assessment of Tumor Thickness in Cutaneous Squamous Cell Carcinoma Using Ex Vivo Confocal Microscopy
by Daniela Hartmann, Katharina Wex, Aimée Braun, Paulina Pabst, Alisa Swarlik, Lisa Buttgereit, Lara Stärr, Andreas Ohlmann, Elke C. Sattler and Maximilian Deußing
Cancers 2026, 18(2), 228; https://doi.org/10.3390/cancers18020228 - 12 Jan 2026
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Abstract
Objectives: Ex vivo confocal laser scanning microscopy (EVCM) is a pioneering diagnostic method that enables fresh tissue samples to be analyzed directly during surgery. For the assessment of non-melanocytic skin cancer (NMSC), including cutaneous squamous cell carcinoma (cSCC), it provides a rapid [...] Read more.
Objectives: Ex vivo confocal laser scanning microscopy (EVCM) is a pioneering diagnostic method that enables fresh tissue samples to be analyzed directly during surgery. For the assessment of non-melanocytic skin cancer (NMSC), including cutaneous squamous cell carcinoma (cSCC), it provides a rapid addition to conventional histology. While previous studies have shown that EVCM reliably identifies the morphological features of cSCCs, quantitative criteria such as tumor thickness have not yet been systematically evaluated. This study investigated whether EVCM can be used to accurately and reproducibly measure the thickness of cSCCs, an important parameter for predicting metastatic risk. Methods: Eighty-two histologically verified cSCCs from different anatomical sites were assessed by the current gold standard of histopathology and EVCM. A statistical comparison of the confocal tumor thickness (CTT) and the histopathological tumor thickness (HTT) was then performed. In addition, it was analyzed how reliable EVCM was in the assignment of cSCCs to the correct tumor thickness category. Results: There was a very high agreement between both methods, evidenced by a Spearman correlation coefficient of 0.94 and a coefficient of determination of 0.859. Overall, 95.1% of the samples were correctly classified into the appropriate tumor thickness category using EVCM. Cohen’s Kappa of 0.90 indicated almost perfect agreement between EVCM and histology. Conclusions: These findings demonstrate that EVCM is a precise and reliable method for determining tumor thickness and the corresponding category in cSCCs. It enables immediate intraoperative assessment of the metastatic risk and preliminary classification of low-risk tumors. Additional studies with larger patient cohorts are required to further validate these results and support clinical implementation. Full article
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