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Diagnostics
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Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases
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Special Issue "Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 July 2023 | Viewed by 1490

Special Issue Editor

Dr. Solène-Florence Kammerer-Jacquet

E-Mail Website
Guest Editor
1. Department of Pathology, Rennes University Hospital, Rennes, France
2. CHU Rennes, Inserm, Université de Rennes, LTSI – UMR 1099, F-35000 Rennes, France
Interests: digital pathology; artificial intelligence; deep learning algorithms; uropathology; dermatopathology; molecular pathology; academic industry partnerships; educational; innovation

Special Issue Information

Dear Colleagues,

Digital pathology is revolutionizing the field by converting glass slides into digital slides. These slides can be easily viewed, analyzed on a computer monitor, managed with annotations and tag selections, and shared in research, teaching, or expertise networks. Digital pathology has made it possible to change the diagnosis workflow, generate big data, and develop and apply artificial intelligence (AI) models. Image analysis is able to extract quantitative and complex data from digitized whole-slide images. AI algorithms generated by machine learning are mainly used for detection, the quantification of biomarkers, or the prediction of prognosis, responses to therapy, or molecular alterations. This Special Issue is dedicated to the applications of digital pathology and AI in the diagnosis, prognosis, and prediction of disease. Indeed, digital pathology can improve the accuracy of a diagnosis by improving analysis and reducing errors. In addition, AI is also crucial to enhancing productivity by improving workflows and reducing time analysis thanks to automatic screening and the quantification of biomarkers. AI algorithms also help predict the prognosis, the therapeutic response, or the presence of molecular alterations from pathological images beyond human visual perception. Original papers, implementation experiences, and reviews are particularly welcome. Papers from academic–industry partnerships are also encouraged. Special attention is required concerning the accessibility of publication content for novice machine-learning pathologists, the selection of adequate tests and validation sets, and data reproducibility.

Dr. Solène-Florence Kammerer-Jacquet
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. Diagnostics 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 2000 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
  • artificial intelligence
  • digital image analysis
  • deep learning algorithms
  • biomarkers quantification
  • convolutional neural networks
  • detection
  • prognosis
  • prediction
  • academic industry partnerships

Published Papers (2 papers)

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Article
Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning
by
Kai Sun
,
Yushi Chen
,
Bingqian Bai
,
Yanhua Gao
,
Jiaying Xiao
and
Gang Yu
Diagnostics 2023, 13(7), 1277; https://doi.org/10.3390/diagnostics13071277 - 28 Mar 2023
Viewed by 658
Abstract
Background: Current artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks [...] Read more.
Background: Current artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks and establish models with performance comparable to supervised learning (SL). Methods: An accurate source domain model was trained using 28,634 colorectal patches. Additionally, 1000 sentinel lymph node patches and 1008 breast patches were used to train two target domain models. The feature distribution difference between sentinel lymph node metastasis or breast cancer and CRC was reduced by heterogeneous domain adaptation, and the maximum mean difference between subdomains was used for knowledge transfer to achieve accurate classification across multiple cancers. Result: HTL on 1000 sentinel lymph node patches (L-HTL-1000) outperforms SL on 1000 sentinel lymph node patches (L-SL-1-1000) (average area under the curve (AUC) and standard deviation of L-HTL-1000 vs. L-SL-1-1000: 0.949 ± 0.004 vs. 0.931 ± 0.008, p value = 0.008). There is no significant difference between L-HTL-1000 and SL on 7104 patches (L-SL-2-7104) (0.949 ± 0.004 vs. 0.948 ± 0.008, p value = 0.742). Similar results are observed for breast cancer. B-HTL-1008 vs. B-SL-1-1008: 0.962 ± 0.017 vs. 0.943 ± 0.018, p value = 0.008; B-HTL-1008 vs. B-SL-2-5232: 0.962 ± 0.017 vs. 0.951 ± 0.023, p value = 0.148. Conclusions: HTL is capable of building accurate AI models for similar cancers using a small amount of data based on a large dataset for a certain type of cancer. HTL holds great promise for accelerating the development of AI in histopathology. Full article
(This article belongs to the Special Issue Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases)
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Systematic Review
Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review
by
Pierre Allaume
,
Noémie Rabilloud
,
Bruno Turlin
,
Edouard Bardou-Jacquet
,
Olivier Loréal
,
Julien Calderaro
,
Zine-Eddine Khene
,
Oscar Acosta
,
Renaud De Crevoisier
,
Nathalie Rioux-Leclercq
,
Thierry Pecot
and
Solène-Florence Kammerer-Jacquet
Diagnostics 2023, 13(10), 1799; https://doi.org/10.3390/diagnostics13101799 - 19 May 2023
Viewed by 464
Abstract
Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims [...] Read more.
Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool. Full article
(This article belongs to the Special Issue Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases)
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