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Cancers
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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: 31 October 2025 | Viewed by 3455

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 policies can be found here.

Published Papers (4 papers)

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Research

18 pages, 2808 KiB  
Article
Application of Telepathology for Rapid On-Site Evaluation of Touch Imprint Cytology in CT-Guided Percutaneous Transthoracic Core Needle Biopsy of Pulmonary Nodules: The Experience of Our Multidisciplinary Thoracic Tumor Board
by Stefano Lucà, Riccardo Monti, Carminia Maria Della Corte, Antonia Cantisani, Immacolata Cozzolino, Eduardo Clery, Martina Amato, Laura Marone, Francesca Capasso, Gaetano Di Guida, Beatrice Leonardi, Floriana Morgillo, Alfonso Fiorelli, Renato Franco, Marco Montella and Giovanni Vicidomini
Cancers 2025, 17(11), 1738; https://doi.org/10.3390/cancers17111738 - 22 May 2025
Viewed by 314
Abstract
Background: Lung cancer remains the leading cause of cancer-related mortality, often diagnosed at advanced stages, where minimally invasive tissue sampling is essential for diagnosis and molecular profiling. Rapid On-Site Evaluation (ROSE) enhances the diagnostic yield of small biopsies, but is frequently limited by [...] Read more.
Background: Lung cancer remains the leading cause of cancer-related mortality, often diagnosed at advanced stages, where minimally invasive tissue sampling is essential for diagnosis and molecular profiling. Rapid On-Site Evaluation (ROSE) enhances the diagnostic yield of small biopsies, but is frequently limited by a shortage of pathologists and logistical constraints. Telepathology offers a potential solution by enabling remote real-time assessment. This study evaluates the feasibility, diagnostic accuracy, and efficiency of telecytology-assisted ROSE (TC-ROSE) using touch imprint cytology (TIC) during CT-guided transthoracic core needle biopsy (CNB) of pulmonary nodules. Methods: 50 patients underwent CNB. TIC samples were assessed and evaluated on-site or remotely via a fully remote-controlled microscope system (OCUS®). TIC slide preparation was performed by pathologists (30 cases), radiologists (10), and trained assistants (10). The study analyzed diagnostic concordance between remote and on-site assessments, time efficiency, and the feasibility of involving non-pathologists in TIC preparation. Results: Diagnostic samples were obtained in 86% of TIC samples, with full concordance (100%) between TC-ROSE and traditional ROSE. The slides required approximately 140 s for scanning, and the overall evaluation time was around 3 min per case. Overall, 100% of TICs were adequately assessed by both pathologists and non-pathologists. No increased number of complications was recorded among patients with TCROSE, compared to those ROSE evaluated. The remote setup allowed pathologists to maintain routine workflows, improving time efficiency. Conclusions: The findings confirm that telecytology is a viable, accurate, and efficient approach to ROSE, offering a practical solution for overcoming workforce and logistical barriers, particularly in settings with limited pathology resources. Full article
(This article belongs to the Special Issue Digital Pathology Systems Enabling the Quality of Cancer Patient Care)
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15 pages, 18522 KiB  
Article
Multi-Observer Study on Diagnostic Accuracy of Pediatric Renal Tumors Imaged with Higher-Harmonic-Generation Microscopy
by Sylvia Spies, Elina Nazarian, Srinivas Annavarapu, Paola Collini, Aurore Coulomb L’Hermine, Ellen D’Hooghe, Jozef Kobos, Guillaume Morcrette, Mariana A. Morini, Sergey D. Popov, Rajeev Shukla, Isabela Werneck da Cunha, Cornelis P. van de Ven, Marry M. van den Heuvel-Eibrink, Ronald R. de Krijger and Marie Louise Groot
Cancers 2025, 17(10), 1693; https://doi.org/10.3390/cancers17101693 - 18 May 2025
Viewed by 459
Abstract
Background/Objectives: Wilms tumors, the most common pediatric renal tumors, are heterogeneous and consist of varying amounts of three components: blastema, epithelium, and stroma. Postoperative chemotherapy is tailored based on risk group classification and stage. Due to this heterogeneity, pathologists perform extensive tumor sampling [...] Read more.
Background/Objectives: Wilms tumors, the most common pediatric renal tumors, are heterogeneous and consist of varying amounts of three components: blastema, epithelium, and stroma. Postoperative chemotherapy is tailored based on risk group classification and stage. Due to this heterogeneity, pathologists perform extensive tumor sampling to ensure accurate classification. Higher-harmonic-generation microscopy (HHGM) is an innovative imaging technique that enables rapid visualization of fresh tissue without preparation or staining. This makes it particularly valuable for sample selection, as the tissue can be reused for further analysis. This study aims to evaluate the accuracy of pathologists in distinguishing normal renal tissue, abnormal renal tissue, and three types of pediatric renal tumors, Wilms tumor, renal cell carcinoma, and congenital mesoblastic nephroma, in HHGM images. Methods: Twenty-nine samples from eighteen patients with a pediatric renal tumor were imaged using an HHG microscope and subsequently processed for histological analysis. Overview images of the samples were acquired at a rate of 10 s per mm2, while high-quality images took 1 min per mm2. A multi-observer study involving ten international expert pathologists of the SIOP-RTSG was conducted. Results: Pathologists were able to differentiate between normal and abnormal tissue with 100% (29/29) accuracy and correctly identified tumor versus non-tumor tissue with 97% (28/29) accuracy. Conclusions: These results show that HHGM is a highly promising technique for the rapid assessment of pediatric renal tumor samples, particularly for evaluating sample representativeness. Full article
(This article belongs to the Special Issue Digital Pathology Systems Enabling the Quality of Cancer Patient Care)
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16 pages, 3750 KiB  
Article
Multi-Observer Study on the Assessment of Pediatric Gonadal Tumors Using Higher Harmonic Generation Microscopy as Compared to Conventional Histology
by Sylvia Spies, Elina Nazarian, Felix Bremmer, Ivan A. Gonzalez, João Lobo, Miguel Reyes-Múgica, Eduardo Zambrano, Caroline C. C. Hulsker, Annelies M. C. Mavinkurve-Groothuis, Ronald R. de Krijger and Marie Louise Groot
Cancers 2025, 17(10), 1636; https://doi.org/10.3390/cancers17101636 - 12 May 2025
Viewed by 393
Abstract
Background/Objectives: Pediatric gonadal tumors are rare tumors, and germ cell tumors (GCTs) are the most common subgroup. GCTs are heterogeneous tumors and have different subtypes that can be either benign or malignant. Therefore, extensive sampling of the resected tumor is required to obtain [...] Read more.
Background/Objectives: Pediatric gonadal tumors are rare tumors, and germ cell tumors (GCTs) are the most common subgroup. GCTs are heterogeneous tumors and have different subtypes that can be either benign or malignant. Therefore, extensive sampling of the resected tumor is required to obtain an accurate diagnosis. Higher harmonic generation microscopy (HHGM) is an innovative imaging technique that enables rapid visualization of fresh tissue without the need for preparation or staining. This makes it particularly valuable for sample selection, as the tissue can be reused for further analysis. This study aims to evaluate the accuracy of pathologists detecting normal gonadal tissue, germ cell tumors, and other pediatric gonadal tumors in HHGM images. Methods: Twenty-eight samples of twenty-two patients with a germ cell tumor or other gonadal tumor were imaged with the HHG microscope and subsequently processed for histology. Overview images of the samples were made in 10 s per mm2, and high-quality images in 1 min per mm2. A multi-observer study was performed with five expert pathologists. Results: Pathologists were able to differentiate between tumor and non-tumor tissue with an accuracy of 75% (21/28) on the HHGM images versus an accuracy of 89% (25/28) on the corresponding histology. Discrepancies mainly concerned teratoma cases for HHGM as well as H&E, indicating that sampling errors of these heterogeneous tumors affected the outcomes of this study adversely. Conclusions: Although the sample size was limited by the rarity of these tumors, our data show that HHGM is a promising technique for the rapid assessment of pediatric gonadal tumor samples, particularly in evaluating their representativeness. Full article
(This article belongs to the Special Issue Digital Pathology Systems Enabling the Quality of Cancer Patient Care)
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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 1744
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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