“Digital pathology”的意思、由来-开放百科全书

Digital pathology is an image-based information environment which is enabled by computer technology that allows for the management of information generated from a digital slide. Digital pathology is enabled in part by virtual microscopy, which is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor. With the advent of Whole-Slide Imaging, the field of digital pathology has exploded and is currently regarded as one of the most promising avenues of diagnostic medicine in order to achieve even better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases.

Potential

In pathology, trained pathologists look at tissue slides under a microscope. The tissue on those slides may be subjected to staining to highlight structures. When those slides are digitized, they then have the potential to be shared (tele-pathology) and numerically analyzed using computer algorithms. Algorithms can be used to automate the manual counting of structures, or for classifying the condition of tissue such as is used in grading tumors. This could reduce human error and improve accuracy of diagnoses. Digital slides are also, by nature, easier to share than physical slides. This increases potential for using data for education and consultations between two or more experts.

Challenges

Digital pathology has been approved by the FDA for primary diagnosis.^[1] The approval was based on a multi-center study of 1,992 cases in which whole-slide imaging (WSI) was shown to be non-inferior to microscopy across a wide range of surgical pathology specimens, sample types and stains.^[2] While there are advantages to WSI when creating digital data from glass slides, when it comes to real-time telepathology applications, WSI is not a strong choice for discussion and collaboration between multiple remote pathologists.^[3] Furthermore, unlike digital radiology where the elimination of film made return on investment (ROI) clear, the ROI on digital pathology equipment is less obvious. The strongest ROI justification includes improved quality of healthcare, increased efficiency for pathologists, and reduced costs in handling glass slides.^[4]

History

The roots of digital pathology go back to the 1960s, when first telepathology experiments took place. Later in the 1990s the principle of virtual microscopy^[5] appeared in several life science research areas. At the turn of the century the scientific community more and more agreed on the term ”digital pathology” to denote digitization efforts in pathology. However in 2000 the technical requirements (scanner, storage, network) were still a limited factor for a broad dissemination of digital pathology concepts. Over the last 5 years this changed as new powerful and affordable scanner technology as well as mass / cloud storage technologies appeared on the market. The field of Radiology has undergone the digital transformation almost 15 years ago, not because radiology is more advanced, but there are fundamental differences between digital images in radiology and digital pathology: The image source in radiology is the (alive) patient, and today in most cases the image is even primarily captured in digital format. In pathology the scanning is done from preserved and processed specimens, for retrospective studies even from slides stored in a biobank. Besides this difference in pre-analytics and metadata content, the required storage in digital pathology is two to three orders of magnitude higher than in radiology. However, the advantages anticipated through digital pathology are similar to those in radiology:

Digital pathology is today widely used for educational purposes^[6] in telepathology and teleconsultation as well as in research projects. Digital pathology allows to share and annotate slides in a much easier way and to download annotated lecture sets generates new opportunities for e-learning and knowledge sharing in pathology. Digital pathology in diagnostics is an emerging and upcoming field.

Environment

Scan

Digital slides are created from glass slides using a scanning device. Digital pathology requires high quality scans free of dust, scratches, and other obstructions.^[7]

View

Digital slides are accessible for viewing via a computer monitor and viewing software either locally or remotely via the Internet.

Example: digital pathology tissue slide stained with Her2/neu biomarker used for diagnosis of breast cancer.

Manage

Digital slides are maintained in an information management system that allows for archival and intelligent retrieval.

Network

Digital slides are often stored and delivered over the Internet or private networks, for viewing and consultation.

Analyze

Image analysis tools are used to derive objective quantification measures from digital slides. Image segmentation and classification algorithms are used to identify medically significant regions and objects on digital slides. Recent developments in machine learning using deep learning methods are very promising and allow to make information hidden in integrated pathological data (images, patient history and *omics data) in arbitrarily high-dimensional spaces accessible and quantifiable, thereby generating a novel source of information which is not yet available to the expert and not exploited in current Digital Pathology settings. ^[8]

Integrate

Digital pathology workflow is integrated into the institution's overall operational environment.

Sharing

Digital pathology also allows internet information sharing for education, diagnostics, publication and research.

See also

References

1. ^{{cite journal | url=https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm552742.htm | title=FDA allows marketing of first whole slide imaging system for digital pathology |accessdate=May 24, 2017}}
2. ^{{cite journal | doi = 10.1097/PAS.0000000000000948 | title = Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter randomized blinded noninferiority study of 1992 cases (pivotal study). | journal = American Journal of Surgical Pathology | volume = Epub ahead of print | issue = 1 | year = 2017 | last1 = Mukhopadhyay | first1 = Sanjay | last2 = Feldman | first2 = Michael | last3 = Abels | first3 = Esther|pmid=28961557 | pmc=5737464 | pages=39–52}}
3. ^{{cite journal |last1=Siegel |first1=Gabriel |last2=Regelman |first2=Dan |last3=Maronpot |first3=Robert |last4=Rosenstock |first4=Moti |last5=Hayashi |first5=Shim-mo |last6=Nyska |first6=Abraham |title=Utilizing novel telepathology system in preclinical studies and peer review |journal=Journal of Toxicologic Pathology |date=Oct 2018 |volume=31 |issue=4 |pages=315–319 |doi=10.1293/tox.2018-0032 |pmid=30393436 |pmc=6206289 }}
4. ^{{cite web |url=http://www.sectra.com/medical/pathology/resources/articles/How_to_build_digital_pathology_business_case.html|publisher=Sectra Medical Systems| title = How to Build a Business Case to Justify the Investment in Digital Pathology |accessdate=April 26, 2015}}
5. ^{{cite journal |year=1997 |last1=Ferreira |first1=R |last2=Moon |first2=J |last3=Humphries |first3=J |last4=Sussman |first4=A | first5 = J | last5 = Saltz | first6 = R | last6 = Miller | first7 = A | last7 = Demarzo | title=The virtual microscope |volume=45 |pages=449–453 | journal=Romanian Journal of Morphology and Embryology | pmc=2233368 | pmid=9357666}}
6. ^{{cite journal | doi=10.1111/j.1600-0463.2011.02869.x |year=2012 |last1=Hamilton |first1=Peter W. |last2=Wang |first2=Yinhai |last3=McCullough |first3=Stephen J. | last4=Sussman | title=Virtual microscopy and digital pathology in training and education|volume=45 |issue=4 |pages=305–315 | journal=Apmis, 120(4) | pmid=22429213}}
7. ^{{cite web|last=Flagship Biosciences|title=How to Improve Whole Slide Scanning in Digital Pathology|url=http://flagshipbio.com/therapeutics/neurology/it-all-starts-with-a-quality-scan/|publisher=Flagship Biosciences LLC|accessdate=25 September 2013}}
8. ^{{cite book | doi=10.1007/978-3-319-69775-8_2 | title = Machine Learning and Knowledge Extraction in Digital Pathology needs an integrative approach. Towards Integrative Machine Learning and Knowledge Extraction | journal = Springer Lecture Notes in Artificial Intelligence | volume = LNAI 10344 | year = 2017 | pages=13–50 | first1 = Andreas | last1 = Holzinger | first2 = Bernd | last2 = Malle | first3 = Peter | last3 = Kieseberg | first4 = Peter M. | last4 = Roth | first5 = Heimo | last5 = Müller | first6 = Robert | last6 = Reihs | first7 = Kurt | last7 = Zatloukal | series = Lecture Notes in Computer Science | isbn = 978-3-319-69774-1 }}