A cholestatic condition is characterised by a decrease in bile flow as substances normally excreted into bile are retained. Often this is due to impaired hepatocytes or obstruction of bile ducts caused by primary damage to the biliary epithelium.
The majority of adult patients with chronic cholestasis have primary sclerosing cholangitis (PSC), a long-term progressive liver disease that affects both small and large bile ducts. PSC is one of the leading causes of liver transplantation in the Nordic countries. It damages the bile ducts through inflammation and ultimately causes cirrhosis leading to loss of liver function as the disease progresses. There are currently no effective treatments as the disease can even reoccur after liver transplantation, some patients having to undergo several transplantations.
Cytokeratin 7 (K7) is an important marker for chronic cholestasis prognosis. In the normal liver K7 expression occurs in the biliary epithelium, whereas hepatocytes remain negative. However, in chronic cholestasis, periportal hepatocytes and intermediate hepatobiliary cells stain positive for K7. Hence, the expression of K7 in the liver is commonly known as an indicator of bile duct injury.
However, traditional methods for K7 detection and analysis are subjective and can lead to significant result variability. Pathologist and Aiforia user Dr. Nelli Sjöblom’s new publication aims to solve this using AI models to locate and quantify K7-positive hepatocytes for accurate and reliable results. We interviewed Nelli to learn more about her process and experience with Aiforia Create. Nelli has developed multiple projects regarding PSC with Aiforia; view a short video showcasing her work below.
Interview with Nelli Sjöblom, MD and pathologist, Helsinki University Hospital
Tell us a bit about the project or research work you used Aiforia’s software for.
Nelli: We aimed to develop an automated image analysis tool to assess the amount of K7-positive hepatocytes in any liver biopsy specimen. The quantity of K7-positive cells in a liver biopsy specimen reflects the amount of chronic cholestasis and is also a predictive marker in some liver diseases. Cholestasis can be caused by multiple different liver diseases and liver failure. The distinction between K7-positive biliary epithelium and the cholestatic hepatocytes was not an easy task to be automatized in the liver with some of the conventional machine learning tools because both of them are stained very similarly in the K7 staining.
What do you think is most unique about the project behind this publication?
To our knowledge, the amount of chronic cholestasis and its predictive value has not been studied before in a cohort of patients with primary sclerosing cholangitis (PSC) that was chosen as a ‘validation’ cohort for our AI model. Our team has access to a unique PSC registry (Helsinki University Hospital) including all the patient’s clinical data and their histological liver specimens that gave us the opportunity to investigate and utilize their samples on the development of this AI model. More prognostic markers are needed for this rare disease for which the liver transplantation surgery is the only effective treatment option. In addition, digital pathology is taking over our traditional diagnostic methods (e.g. the microscope) so we wanted to see if developing tools for diagnostic assistance would also work in analyzing these specifically stained liver specimens.
How was your experience learning to use the Aiforia software? Have you worked with AI previously?
I had not worked with AI or Aiforia software before, but I had read about automated image analysis and methods developed to assist pathologists working in fully digitalized pathology laboratories. Thus, I immediately became interested when my supervisors Prof. Johanna Arola and Prof. Martti Färkkilä offered me this project as part of my thesis.
The experience was great - the platform was rather intuitive and I find it very important that the people (the pathologists) using these tools will be involved in the development of such tools as well.
Why do you think AI model image analysis tools have not previously been used in assessment of chronic cholestasis? Do you find it is worth investing time into?
Not many commercial AI models have been developed for other purposes than to aid in diagnostics of the most common malignancies such as breast cancer or prostate cancer. But I am sure when they become more accessible and affordable, more pathologists will be able to build AI models specifically designed for their needs.
Objective and rapid tools are needed in different fields of pathology, especially when it comes to research and drug development (treatment response assessment). I believe that is where the AI models truly prove their worth.
What are the benefits you see of Aiforia to your work?
The automation makes the image analysis and histological interpretation fast and objective. The aim was to show that the methodology described in the article is non-inferior to a human pathologist and we can rely on the results produced by the AI model. This is valuable information regarding the future use of such tools.
What are your next steps after this publication; do you plan to use AI for further research or other projects?
The next phase is to apply this tool on the previously described PSC cohort and see whether the results correlate with disease progression and whether chronic cholestasis will indicate poorer prognosis. Development of other models for different staining methods of the liver are ongoing.
Video showcasing multiple projects over the course of Nelli’s work on PSC
Video: Deep neural networks in diagnosis and disease progression of Primary Sclerosing Cholangitis (PSC)
- 0:09 Implementing machine learning and supervised learning in labeling the training set of images
- 1:03 New classification of histology based on an algorithm in PSC and its impact on prognosis
- 2:00 Indicators of chronic cholestasis (CK-7) in assessment of PSC based on deep learning networks
Read more about the beginning of Nelli’s work here.