Where is the digital pathology interest in dermatopathology buried?
With the increased adoption of both brightfield and whole slide fluorescence scanning, the accessibility of skin samples seems ripe for digital pathology applications. But there are good reasons for going slow in this clinic area:
1) Questions of dermatology diagnostic equivalency of glass versus image. While the whole slide imaging technology keeps improving, Jonhan Ho et al (University of Pittsburg, 2008) rightly mentions concerns with the whole slide image in clinical dermatology usage. A recent publication by Bjarne Nielsen et al (Aarhus University in Denmark, 2010), is positive on skin tumor virtual microscopy provided pathologists have completed a period of digital pathology training. The good news overall is scanning technology keeps getting better, especially in reducing poorly scanned areas that are unacceptable in a clinical practice.
2) Complexity of dermatopathology. The skin may be the only place where the surface really is the most complex. There are an estimated 1500 different rashes and skin tumors, including variants, making dermatology and dermatopathology among the most complex specialties of medicine. How exactly does one approach an equivalency study design with this much disease complexity? It would challenge even a Dr. Holger Lange to device an adequate regulatory digital pathology study design for dermatology.
3) Complexity of image analysis approaches. One needs to first be able to have the computer identify layers of the derm, prior to looking at feature-level analysis (e.g. cell and object counting, or geometry measurements in a given layer). Layer analysis requires a white-box or rules-based programming that combines geographic knowledge (“Which layer am I in?”) with textual feature recognition (“Hey computer, this layer’s texture looks like this…”). In this sense it is similar to layer-based ophthalmology image analysis. It takes substantial time to write these layer-based detection algorithms and one has to constantly verify that the assumptions made in the algorithm match the tissue being analyzed (e.g as a superficial example, an algorithm looking to identify five stratum layers may work in thick skin epidermis, but will fall apart when working in thin skin with a missing stratum lucidum and only three or four of the five layers).
4) Economics of clinical dermatopathology. Most dermatology practices do not send out many of their dermatapathology cases, and cannot afford the hundreds of thousands of dollars for digital pathology scanners and software, which would not change how they would practice their discipline (at least not yet).
Despite these limits in clinical dermatology digital pathology adoption, we are excited about the possibilities that whole slide imaging brings to dermatology research and pharmaceutical clinical trials. Quantitative data on both efficacy and toxicology is key to all stages of pharmaceutical product development, and skin is no exception. To be successful, the dermatology trained pathologist must work closely with an image analysis expert. Particularly in skin, the specific image analysis design must be discussed beforehand with a pathologist, and a pathologist needs to review and sign off on each result. This is true whether the study approach is simple, like increased collagen or dermal thickness measurements, or complex, like multiplexed IHC melanoma proliferation studies.
Interestingly enough, thanks to the efforts of some remarkable pathologist pioneers in dermatopathology, the dermatology field has historically been described in algorithmic terms. Dr. Ackerman’s book in 1978 is the classic work, and written in a rules-based approach. There have been multiple new editions, but the original 1978 textbook, if you can find one, costs thousands of dollars, and is worth far more in the contribution he provided to this field.
As a digital pathology CRO, we do a lot of work in the area of skin samples, whether the sample is for implanted device material evaluation, cosmetics studies, or dermatopathology product development. This seems an opportunity for comparative pathology approaches, and the opportunity to participate in dialogs between veterinary and MD pathologists in developing dermatology image analysis applications is truly a privilege. However, finding MD dermatopathologists who have the interest, time, and training to be involved in dermatology product development is not easy.
Of all the various organs where digital pathology will have a major impact, the complexity of dermatopathology is perhaps the most humbling to image analysis experts. We are just scratching the surface.