Difference between revisions of "Digital Pathology"

Revision as of 01:45, 22 October 2018

Added whole slide imaging as a medical device and redirected whole slide imaging to digital pathology Digital Pathology is a broad term defined as a dynamic image-based environment that enables the acquisition, management and interpretation of pathology information generated from a glass slide. [1]. Or simply it is digitization of pathology. Whole slide imaging is commonly equated to digital pathology but digital pathology more broadly includes the capture of pathology slides by a simple camera on a microscope, or robotic microscopy. However, some may argue that photographic images of gross specimens with integration into laboratory information systems can be considered digital pathology [2].

Background

Digital Pathology is a much talked about field in pathology informatics. In the late 90’s early 00’s as digital cameras became increasingly used, microscopy photography of pathology slides became increasingly popular. It was also around this time that whole slide scanners (WSI) were first introduced [3]. Major advantages and justifications for Digital Pathology include:

• Education – Digital slides allow for greater access and sharing of complex pathology cases to pathologists around the world. Instructors can highlight regions of interest and Many slide repositories are available on the web. Prior to digital pathology complex cases would require recuts and multiple recuts may result in loss of the cells of interest and chronic exposure to light may dim the intensity and contrast of stains on glass slides.
• Research – Digital slides potentially allow automated detection of cells of interest to improve accuracy and reproducibility in the detection of abnormal cells.
• Telepathology – Expert pathologists will be able to make rapid frozen section diagnoses outside the hospital. Consultation of difficult cases may be more streamlined and have a quicker turnaround time
• Medical Record Integration – Currently much of the patient’s anatomical pathology data resides outside of the medical record. Increase integration of pathology images will allow greater transparency of pathology results to other clinicans and even patient’s themselves much like radiology images today

However, there currently the largest barriers to implementation: [4]

• Cost – WSI scanners can run upwards of at least 200 thousand dollars
• Regulatory issues – It has only been 1 year since a WSI system has been approved for use by the FDA
• Perceptions of inferiority – Many pathologists are more familiar to the workflow of signing out glass slides and adjustments to the digital pathology workflow must be made. However, studies have demonstrated it is realistically possible to achieve efficient use of digital pathology for sign-out [5] [6].
• Lack of imaging standards [7].

Challenges with Incorporation

Early slide scanners could not load many slides at a time and scanned at very slow speeds. Due to this reason, many argued in the beginning that WSI is too disruptive and costly to implement t[8]. A large Pathology laboratory may generate up to 2400 slides per day. In order to process those number of slides with one scanner, the scanning speed must take less than a minute a slide and early scanners were significantly slower. The new FDA approved Philips Intellisite Pathology Solution scans a 15 x 15 mm area at 40X in 60 seconds and hold 300 slides at a time [9].

Regulatory Issues

There also have been significant regulatory issues concerning digital pathology which has slowed incorporation into clinical workflows. Light microscopy, is registered as a class I medical device and therefore does not need FDA approval to be sold in the healthcare setting [10]. However, in 2009 the FDA held a meeting to discuss the replacement of light microscopy by WSI for primary diagnosis. They debated making WSI a class II device requiring a 510K approval or a class III device requiring premarket approval clinical trials, with the latter being the final conclusion after a few years of discussion [11]. The primary diagnosis verbiage is emphasized to distinguish it from WSI of immunohistochemical stains. Images of these types are used to quantify the expression of prognostically important molecules but do not have diagnostic implications. Given the perceived reduced risk these scanners and the image analysis algorithms associated with them classified as Class II devices. It took the FDA another 5 years to provide draft recommendations in 2014 on how to perform an assessment of WSI devices which wasn’t finalized till 2016 [12]. Because of this uncertainty, it wasn’t until 2017 a WSI scanner received regulatory approval, which was the Philips IntelliSite Pathology Solution. After this, the FDA made WSI scanners a class II medical device since a predicate device was established. Therefore, in the future it should be easier for WSI scanners to get FDA approval, thereby alleviating the barrier to implementation somewhat.

Interoperability

Interoperability of digital pathology images is difficult because no true universal format exists for both user support or for archiving. Many modern slide scanner manufactures have their own proprietary file formats with significant variance in compression techniques that affects the quality output from these scanners. Many argue for this reason whole slide images should be saved in the DICOM format. DICOM has a standard for Pathology images which was developed by working group 26 that was only approved in 2010. Slide scanner vendors have slowly been incorporating this format with their provided image software. Last October “Connectathon” shows were put on where vendors (Philips, Ventana, Leica) proved they had interoperability by scanning and importing the resulting images using the DICOM standard into an agreed upon PACS system ahead of conference [13].

Interface with LIS

Interfacing the LIS with pathology images is necessary for clinical workflows. However, full integration with the LIS is not entirely necessary for pathology images and may be difficult with the current state of laboratory information systems. A modular or integrative approach may be used. [14] In the modular approach, images are stored outside of the LIS and a middleware solution is used to connect the image repository to the LIS. This has an increased flexibility in the sharing and viewing of the images. | Vendor neutral archives (VNA) are getting increased traction in the medical field today and the modular approach may allow the incorporation into these archives easier.

Image Analysis

Digital Pathology has great potential in automating the detection of cells of interest. Quantification of positively staining cells for biomarkers in immunohistochemically (IHC) slides can be monotonous with significant inter-observer variability in pathology. Images analysis of these slides holds much promise may allowed for increased accuracy, more reproducible results, increased automation and reduce time consumption by pathologists. Image analysis of HER2 stain slides breast cancer has the longest history of utilization, and therefore has clear formalized guidelines governing its application for us. Studies have demonstrating superiority in manual assessment [15] by image analysis. Image analysis algorithms as discussed above require 510(k) FDA clearance however recently there has been a drive to create laboratory developed tests (LDTs) utilizing image analysis. These laboratory developed tests are not FDA approved or used. This is in large part due to significant number of biomarkers available for use with only a small portion of them having imaging analysis algorithms approved by the FDA. Advanced techniques involving the detection of malignant cells in H&E slides is currently not used clinically but is currently the topic of many research endeavors. Simple image analysis techniques are usually insufficient for H&E and artificial intelligence and deep learning techniques are commonly incorporated. Algorithms for detection of cancer in cervical biopsies [16]and the grading of prostate cancer has recently been reported [17].

Digital Pathology Resources

[College of American Pathologists (CAP) Website; Digital Pathology Resource Guide] [/ Digital Pathology Association]

Submitted by Thomas Schneider