Computer-Assisted Digital Pathology in Diagnostics

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 July 2023) | Viewed by 2011

Special Issue Editor

Center for Biomedical Informatics, Wake Forest School of Medicine, Winston Salem, NC, USA
Interests: tumor biomarkers; machine learning

Special Issue Information

Dear Colleagues, 

Diagnostic pathology is a medical specialty focusing on the examination of disease via microscopic examination of abnormal body tissues (usually with histopathology) and diagnosis based on the morphological characteristics of said tissues. Within diagnostic pathology, digital pathology has played an increasingly crucial role. It involves the digitization, transfer, and storage of histologically stained tissue section slides at high resolution. With integration of digital slides into the pathology workflow, advanced algorithms and computer-aided diagnostic techniques have extended the frontiers of the pathologist's view beyond a microscopic slide and have enabled true utilization and integration of knowledge beyond human limits and boundaries. In particular, these technologies serve as an enabling platform for the application of computer-assisted technologies such as artificial intelligence (AI), deep learning, and machine learning in digital pathology. AI already enables pathologists to identify unique imaging markers associated with disease processes with the goal of improving early detection, determining prognosis, and selecting treatments most likely to be effective. This allows pathologists to serve more patients while maintaining diagnostic and prognostic accuracy.

Computer-assisted diagnostic pathology is slated to immensely impact clinical practice, but especially for oncology and precision medicine. Much like the evolution of the efficiency and effectiveness of radiology, the pressure on pathologists to reduce the turnaround time and develop more efficient workflows is trending towards digitalization. This digital innovation has the potential to change the way diagnosis is carried out—in particular, the added benefits of shared images and data, increased efficiency and integrated diagnostics, modernization of pathology workflows to improve patient care and safety, increased collaboration through multidisciplinary and disease-specific patient-care conferences, improved accountability (on behalf of the physician, who makes the final clinical decision), and cost savings by optimizing staff performance. Overall, computer-aided diagnostic pathology can automate and standardize many of the tasks that are manual and subjective. This Special Issue covers (but is not limited to) topics on machine learning and deep learning methods with their applications in:

  • grading and classification of pathology images;
  • multi-stain and multiplexed image analysis;
  • image analysis of anatomical structures/functions and lesions;
  • architectural feature extraction and quantification;
  • stain normalization/standardization;
  • radiology-pathology registration and fusion;
  • segmentation of cellular and tissue structures;
  • multi-modality fusion for analysis diagnosis and intervention;
  • content-based image retrieval;
  • computer-aided diagnosis prognosis and predictive analysis;
  • metrics variability and standardization issues unique to digital pathology;
  • whole-slide image analysis;
  • detection or identification of predictive and prognostic tissue biomarkers;
  • observer performance human factors reading strategies and diagnostic interpretation issues;
  • immunohistochemistry scoring;
  • automated quantification of tissue biomarkers;
  • registration of multiple stained tissue microscopy images;
  • comparison of quantitative results with qualitative results.

Dr. Muhammad Khalid Khan Niazi
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 2600 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

  • diagnostic pathology
  • digital pathology
  • digital slides
  • histologically stained tissue
  • computer-aided diagnostic techniques
  • artificial intelligence (AI)
  • deep learning
  • machine learning
  • unique imaging markers
  • prognostic
  • oncology
  • precision medicine

Published Papers (1 paper)

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Review

21 pages, 366 KiB  
Review
Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology—Current Affairs and Perspectives
by Mihaela Moscalu, Roxana Moscalu, Cristina Gena Dascălu, Viorel Țarcă, Elena Cojocaru, Ioana Mădălina Costin, Elena Țarcă and Ionela Lăcrămioara Șerban
Diagnostics 2023, 13(14), 2379; https://doi.org/10.3390/diagnostics13142379 - 14 Jul 2023
Cited by 5 | Viewed by 1558
Abstract
In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods [...] Read more.
In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist’s vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist’s evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level. Full article
(This article belongs to the Special Issue Computer-Assisted Digital Pathology in Diagnostics)
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