Download
document (3).pdf 370,53KB
WeightNameValue
1000 Titel
  • Virtual Predictive Autopsy: From knowledge and understanding to education, research and communication in digital tissue – based diagnosis.
1000 Autor/in
  1. Kayser, Klaus |
1000 Erscheinungsjahr 2019
1000 Publikationstyp
  1. Kapitel |
1000 Online veröffentlicht
  • 2019-07-05
1000 Erschienen in
1000 Quellenangabe
  • 5(1):274
1000 Copyrightjahr
  • 2019
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.17629/www.diagnosticpathology.eu-2019-5:274 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • BACKGROUND: The digital world is entering all compartments of tissue – based diagnosis, especially education, training and performance of surgical pathology. THEORY: Communication is a requirement of life. It is based upon knowledge, understanding, and adequate response. Understanding tries to implement and spread concordant or target related actions. Analysis of liquid biopsy, cytology, biopsy, surgical specimens and autopsy comprise the tissue – based sources. They are transferred into images and create the basis of education and training, followed by research and publication. PRESENT STAGE: Liquid biopsies require the automated application of digital tools, such as digital visualization and statistical analysis of the obtained DNA / protein figures. Manual interference does not occur.Tissue of cytology, aspirations, biopsies, and surgical specimens are still fixed and processed in conventional manner, and placed on glass slides. Digital microscopy replaces conventional light microscopy in some pathology institutes. It is usually applied close to its analogue performance. Diagnosis assistants are used for quantification of specific image features, for example to score the expression of functional cellular markers. Digital microscopy is an important compartment of the available Hospital Information System too. At present, autopsies do not contribute to tissue – based diagnosis in a notable frequency. Even big University Pathology Institutes report an autopsy frequency less than 100 cases, in comparison to approximately 100,000 biopsy specimens or even more per year. Most authors name live imaging investigations (CT, MR, Ultrasound, etc.) for reason. An additional factor might be the diminishing impact of understanding in medical diagnostics: Highly precise information of individual (small) tissue compartments is frequently considered to be sufficient for treatment. They include receptor expressions, intra-cellular pathway abnormalities, gene alterations, etc. This seems to be a contradiction to ‘organ communitive information’ obtained from autopsies. Such post mortem information can also be obtained during the patient’s life time and predict the probable trails of recovery or death by use of digital pathology. The procedure is called ‘predictive autopsy’ and described in detail herein. FUTURE ASPECTS: Digital pathology is entering the field of ‘automated diagnosis’, starting with automated recognition of ‘regions of interest’ and associated characteristics such as automated diagnosis, digital self - recognition, automated failure repair, treatment advises, etc. The field of ‘digital autopsy’ will remain reserved for education because of need for ‘real autopsies’. The proposed ‘predictive autopsies’ offer additional perspectives of digital tissue – based diagnosis, which include the digital analysis of tissue / organ dysfunctions and syntax at life time, and the impact on forecast the recover /disease progress of the patient. CONCLUSIONS: Digital pathology is on its way to enter numerous implementations of tissue – based diagnosis. We propose digital ‘predictive autopsies’ as a new tool to analyze, explain, and forecast the involvement of all organs in the individual patient’s disease development and interpret the ‘cause of death’ more in detail.
1000 Sacherschließung
lokal liquid biopsy
lokal digital pathology
lokal in vivo (predictive) autopsy
lokal life imaging
lokal tissue – based diagnosis
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-5076-3936
1000 Label
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6415239.rdf
1000 Erstellt am 2019-07-17T12:22:05.010+0200
1000 Erstellt von 218
1000 beschreibt frl:6415239
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Wed Jul 17 12:31:05 CEST 2019
1000 Objekt bearb. Wed Jul 17 12:30:30 CEST 2019
1000 Vgl. frl:6415239
1000 Oai Id
  1. oai:frl.publisso.de:frl:6415239 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source