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: closed (31 January 2024) | Viewed by 6967

Special Issue Editor


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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

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Keywords

  • digital pathology
  • artificial intelligence
  • digital image analysis
  • deep learning algorithms
  • biomarkers quantification
  • convolutional neural networks
  • detection
  • prognosis
  • prediction
  • academic industry partnerships

Published Papers (4 papers)

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Research

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16 pages, 2601 KiB  
Article
Department Wide Validation in Digital Pathology—Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists’ Guidance
by Mai Kelleher, Richard Colling, Lisa Browning, Derek Roskell, Sharon Roberts-Gant, Ketan A. Shah, Helen Hemsworth, Kieron White, Gabrielle Rees, Monica Dolton, Maria Fernanda Soares and Clare Verrill
Diagnostics 2023, 13(13), 2144; https://doi.org/10.3390/diagnostics13132144 - 22 Jun 2023
Cited by 1 | Viewed by 965
Abstract
Aim: we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) “Best Practice Recommendations for Implementing Digital Pathology (DP),” at a large academic teaching hospital that scans 100% of its surgical workload. [...] Read more.
Aim: we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) “Best Practice Recommendations for Implementing Digital Pathology (DP),” at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off. Methods and results: twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting. Conclusion: we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides. Full article
(This article belongs to the Special Issue Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases)
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13 pages, 31743 KiB  
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
Cited by 5 | Viewed by 1763
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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8 pages, 895 KiB  
Brief Report
An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates
by Yogavijayan Kandasamy and Stephanie Baker
Diagnostics 2023, 13(18), 2865; https://doi.org/10.3390/diagnostics13182865 - 05 Sep 2023
Viewed by 1238
Abstract
A preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 15 million babies are born preterm annually worldwide, indicating a global preterm birth rate of about 11%. Up to 50% of premature neonates in the gestational age [...] Read more.
A preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 15 million babies are born preterm annually worldwide, indicating a global preterm birth rate of about 11%. Up to 50% of premature neonates in the gestational age (GA) group of <29 weeks’ gestation will develop acute kidney injury (AKI) in the neonatal period; this is associated with high mortality and morbidity. There are currently no proven treatments for established AKI, and no effective predictive tool exists. We propose that the development of advanced artificial intelligence algorithms with neural networks can assist clinicians in accurately predicting AKI. Clinicians can use pathology investigations in combination with the non-invasive monitoring of renal tissue oxygenation (rSO2) and renal fractional tissue oxygenation extraction (rFTOE) using near-infrared spectroscopy (NIRS) and the renal resistive index (RRI) to develop an effective prediction algorithm. This algorithm would potentially create a therapeutic window during which the treating clinicians can identify modifiable risk factors and implement the necessary steps to prevent the onset and reduce the duration of AKI. Full article
(This article belongs to the Special Issue Digital Pathology: Diagnosis, Prognosis, and Prediction of Diseases)
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16 pages, 2712 KiB  
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
Cited by 5 | Viewed by 2122
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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