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Cancers
Special Issues
Digital Pathology Systems Enabling the Quality of Cancer Patient Care
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Digital Pathology Systems Enabling the Quality of Cancer Patient Care

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1494

Special Issue Editor

Prof. Dr. Peter J. Schüffler

E-Mail Website
Guest Editor
Institute of Pathology, School of Medicine and Health, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany
Interests: digital and computational pathology

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your article for this Special Issue of Cancers titled “Digital Pathology Systems Enabling the Quality of Cancer Patient Care”.

This Special Issue aims to highlight current advances in quality-related issues in digital pathology. This includes both digital data generation and data analysis/interpretation. During data generation, digital pathology heavily relies on new measures for quality control and quality assurance. For example, image artifacts occur at every stage of tissue processing, but methods of artificial intelligence might be able to identify and mitigate these issues. Moreover, the effect of artifacts on machine learning methods is not completely known. Data analysis, however, requires its own set of quality measures. AI-assisted pathology data analysis processes will help improve the success rate of a pathology lab and interobserver agreement and deepen our understanding of cancer development and treatment.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: quality control and quality assurance in digital pathology, quality control and assurance in diagnostic procedure using algorithmic approaches, and automated support for clinical pathology and image analysis. The systems can be built from standalone expert models, over larger self-supervised domain models towards extremely large multi-modal foundation models that help in clinical data interpretation.

We look forward to receiving your contributions.

Prof. Dr. Peter J. Schüffler
Guest Editor

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

  • digital pathology
  • computational pathology
  • artificial intelligence
  • multimodal AI
  • foundation models
  • quality control
  • quality assurance
  • medical image analysis
  • computer-aided diagnosis
  • integrated digital pathology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

16 pages, 4734 KiB  
Article
Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors
by Yen-Chang Chen, Shinn-Zong Lin, Jia-Ru Wu, Wei-Hsiang Yu, Horng-Jyh Harn, Wen-Chiuan Tsai, Ching-Ann Liu, Ken-Leiang Kuo, Chao-Yuan Yeh and Sheng-Tzung Tsai
Cancers 2024, 16(13), 2449; https://doi.org/10.3390/cancers16132449 - 3 Jul 2024
Viewed by 1174
Abstract
Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at [...] Read more.
Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors. Full article
(This article belongs to the Special Issue Digital Pathology Systems Enabling the Quality of Cancer Patient Care)
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