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Biomolecules
Special Issues
Digital Pathology
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Digital Pathology

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 25761

Special Issue Editor

Dr. William Cho

E-Mail Website
Guest Editor
Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
Interests: cancer biomarker; evidence-based medicine; extracellular vesicles; genomics; microRNA; molecular diagnostics; non-coding RNAs; nasopharyngeal carcinoma; next-generation sequencing; non-small cell lung cancer; proteomics; drug repurposing and bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Invention of the microscope was a milestone for modern medicine and for mankind. Nowadays, disease diagnosis heavily relies on definitive decisions from histopathological analysis of the specimen. In the current era, digital pathology is a dynamic, image-based environment that incorporates the acquisition, management, and interpretation of pathology information generated from a digitized glass slide. Digital slides are created when glass slides are captured by a scanning device, and they provide a high-resolution digital image that can be viewed on a computer screen or mobile device.

Digital pathology can improve the quality of diagnosis in meaningful ways, including reduced errors, improved analysis, and better views. Thus, digital pathology enhances productivity because of the improved workflow, reduced turnaround times, and more innovative design. However, it is also challenging current conventional settings, and the integration of digital pathology should be well planned out.

This Special Issue serves as a platform to propel the field of digital pathology.

The scope of this Special Issue includes, but is not limit to, the following:

  • virtual multiplex immunohistochemistry;
  • automated classification of whole-slide images based on deep learning;
  • super-resolution recurrent convolutional neural networks;
  • challenges in analysis of digital tissue biopsies;
  • translational artificial intelligence and deep learning in diagnostic pathology;
  • efficient algorithms for digital image analysis;
  • computational pathology, best practices and recommendations;
  • sensitivity analysis in digital pathology;
  • artificial intelligence algorithms in digital pathology; and
  • automated tumor recognition and scoring for biomarkers.

Dr. William Cho
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 short 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. Biomolecules 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 2700 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

  • deep learning in diagnostic pathology
  • digital pathology
  • digital tissue biopsies
  • tumor recognition and scoring
  • virtual multiplex immunohistochemistry

Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 190 KiB  
Editorial
Digital Pathology: New Initiative in Pathology
by William C. Cho
Biomolecules 2022, 12(9), 1314; https://doi.org/10.3390/biom12091314 - 17 Sep 2022
Cited by 2 | Viewed by 1469
Abstract
Digital pathology (DP) is an emerging field of pathology that manages information generated from digitized specimen slides [...] Full article
(This article belongs to the Special Issue Digital Pathology)

Research

Jump to: Editorial

14 pages, 2171 KiB  
Article
Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry
by János Bencze, Máté Szarka, Balázs Kóti, Woosung Seo, Tibor G. Hortobágyi, Viktor Bencs, László V. Módis and Tibor Hortobágyi
Biomolecules 2022, 12(1), 19; https://doi.org/10.3390/biom12010019 - 23 Dec 2021
Cited by 12 | Viewed by 4542
Abstract
Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim [...] Read more.
Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring. Full article
(This article belongs to the Special Issue Digital Pathology)
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16 pages, 4605 KiB  
Article
Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease
by David Marti-Aguado, Matías Fernández-Patón, Clara Alfaro-Cervello, Claudia Mestre-Alagarda, Mónica Bauza, Ana Gallen-Peris, Víctor Merino, Salvador Benlloch, Judith Pérez-Rojas, Antonio Ferrández, Víctor Puglia, Marta Gimeno-Torres, Victoria Aguilera, Cristina Monton, Desamparados Escudero-García, Ángel Alberich-Bayarri, Miguel A. Serra and Luis Marti-Bonmati
Biomolecules 2021, 11(12), 1808; https://doi.org/10.3390/biom11121808 - 02 Dec 2021
Cited by 7 | Viewed by 2590
Abstract
Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic [...] Read more.
Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-score: proportionate area of staining intensities clusters) measurements were independently performed. Our data show that I-scores and C-scores increase with inflammation grades (p < 0.001). High correlation was seen for CH (ρ = 0.85–0.88), but only moderate for NAFLD (ρ = 0.5–0.53). I-score (p = 0.008) and C-score (p = 0.002) were higher for CH than NAFLD. Our MATLAB algorithm performed better than QuPath software for the diagnosis of low-moderate inflammation (p < 0.05). C-score AUC for classifying NASH was 0.75 (95%CI, 0.65–0.84) and for moderate/severe CH was 0.99 (95%CI, 0.97–1.00). Digital pathology measurements increased with fibrosis stages (p < 0.001). In conclusion, quantitative and morphometric metrics of inflammatory burden obtained by digital pathology correlate well with pathologists’ scores, showing a higher accuracy for the evaluation of CH than NAFLD. Full article
(This article belongs to the Special Issue Digital Pathology)
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19 pages, 4352 KiB  
Article
xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer
by Aurelia Bustos, Artemio Payá, Andrés Torrubia, Rodrigo Jover, Xavier Llor, Xavier Bessa, Antoni Castells, Ángel Carracedo and Cristina Alenda
Biomolecules 2021, 11(12), 1786; https://doi.org/10.3390/biom11121786 - 29 Nov 2021
Cited by 6 | Viewed by 2875
Abstract
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead [...] Read more.
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient’s spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task. Full article
(This article belongs to the Special Issue Digital Pathology)
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10 pages, 2123 KiB  
Article
Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group
by Ceren Boyaci, Wenwen Sun, Stephanie Robertson, Balazs Acs and Johan Hartman
Biomolecules 2021, 11(11), 1612; https://doi.org/10.3390/biom11111612 - 30 Oct 2021
Cited by 11 | Viewed by 2153
Abstract
Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast [...] Read more.
Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920–0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593–4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions. Full article
(This article belongs to the Special Issue Digital Pathology)
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24 pages, 4126 KiB  
Article
Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis
by Julia Moran-Sanchez, Antonio Santisteban-Espejo, Miguel Angel Martin-Piedra, Jose Perez-Requena and Marcial Garcia-Rojo
Biomolecules 2021, 11(6), 793; https://doi.org/10.3390/biom11060793 - 25 May 2021
Cited by 1 | Viewed by 2249
Abstract
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, [...] Read more.
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People’s Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation. Full article
(This article belongs to the Special Issue Digital Pathology)
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23 pages, 6194 KiB  
Article
Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization
by Guillaume E. Courtoy, Isabelle Leclercq, Antoine Froidure, Guglielmo Schiano, Johann Morelle, Olivier Devuyst, François Huaux and Caroline Bouzin
Biomolecules 2020, 10(11), 1585; https://doi.org/10.3390/biom10111585 - 22 Nov 2020
Cited by 33 | Viewed by 7954
Abstract
Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods [...] Read more.
Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics. Full article
(This article belongs to the Special Issue Digital Pathology)
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