Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens

Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium

Background

The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency.

Methods

We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition.

Results

Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists' assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women's samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses.

Conclusion

The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia

BACKGROUND

We aimed to quantify ductular reaction (DR) in biliary atresia using a neural network in relation to underlying pathophysiology and prognosis.

METHODS

Image-processing neural network model was applied to 259 cytokeratin-7-stained native liver biopsies of patients with biliary atresia and 43 controls. The model quantified total proportional DR (DR%) composed of portal biliary epithelium (BE%) and parenchymal intermediate hepatocytes (PIH%). The results were related to clinical data, Sirius Red-quantified liver fibrosis, serum biomarkers, and bile acids.

RESULTS

In total, 2 biliary atresia biopsies were obtained preoperatively, 116 at Kasai portoenterostomy (KPE) and 141 during post-KPE follow-up. DR% (8.3% vs. 5.9%, p=0.045) and PIH% (1.3% vs. 0.6%, p=0.004) were increased at KPE in patients remaining cholestatic postoperatively. After KPE, patients with subsequent liver transplantation or death showed an increase in DR% (7.9%-9.9%, p = 0.04) and PIH% (1.6%-2.4%, p = 0.009), whereas patients with native liver survival (NLS) showed decreasing BE% (5.5%-3.0%, p = 0.03) and persistently low PIH% (0.9% vs. 1.3%, p = 0.11). In Cox regression, high DR predicted inferior NLS both at KPE [DR% (HR = 1.05, p = 0.01), BE% (HR = 1.05, p = 0.03), and PIH% (HR = 1.13, p = 0.005)] and during follow-up [DR% (HR = 1.08, p<0.0001), BE% (HR = 1.58, p = 0.001), and PIH% (HR = 1.04, p = 0.008)]. DR% correlated with Sirius red-quantified liver fibrosis at KPE (R = 0.47, p<0.0001) and follow-up (R = 0.27, p = 0.004). A close association between DR% and serum bile acids was observed at follow-up (R = 0.61, p<0.001). Liver fibrosis was not prognostic for NLS at KPE (HR = 1.00, p = 0.96) or follow-up (HR = 1.01, p = 0.29).

CONCLUSIONS

DR predicted NLS in different disease stages before transplantation while associating with serum bile acids after KPE.

Deep learning based image analysis of liver steatosis in mouse models

The incidence of non-alcoholic fatty liver disease (NAFLD) is a continuously growing health problem worldwide, along with obesity. Therefore, both novel methods to efficiently study the manifestation of NAFLD and to analyze drug efficacy in pre-clinical models are needed. In the present study, we developed a deep neural network -based model to quantify micro- and macrovesicular steatosis in the liver on hematoxylin-eosin stained whole slide images (WSIs), using the cloud-based platform, Aiforia Create (Aiforia Technologies, Helsinki, Finland). The training data included a total of 101 WSIs from dietary interventions of wild-type mice and from two genetically modified (GM) mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro- and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists, and correlated well with the liver fat content measured by EcoMRI ex vivo, and the correlation with total liver triglycerides were notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections, and thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review

BACKGROUND

Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field.

OBJECTIVE

The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields.

RESULTS

42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias.

CONCLUSIONS

DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Histological evaluation in biliary diseases

PURPOSE OF REVIEW

This review focuses on recent developments of histopathology in the most common biliary disorders affecting adults. The reader is referred to other sources for the specialized topics on paediatric populations and post liver transplantation.

RECENT FINDINGS

Fibrosis stage at diagnosis is an independent predictor of liver transplant-free survival in patients with primary biliary cholangitis. Immunohistochemistry might have an important role in predicting response to treatment. New histological scoring systems with excellent correlation with long-term clinical outcomes are being developed in primary sclerosing cholangitis (PSC). Quantification of fibrosis with collagen proportionate area can improve risk stratification and could be particularly useful to assess treatment response in PSC.Gene sequencing on cytology and intrabiliary biopsy may improve risk stratification for cholangiocarcinoma. Genetic variants of ATP8B1, ABCB11 and ABCB4 are relatively common in adults with cholestatic liver disease. New causes of cholestatic liver injury have recently been described.

SUMMARY

Histology is often not necessary for the diagnosis of biliary disease, but can provide important information that may assist the clinician in patients' management. Histopathology remains crucial to confirm a diagnosis of cholangiocarcinoma, and to identify the pattern of biliary injury in immune-mediated cholangiopathies and rarer pathological entities.

Update on Hepatobiliary Plasticity

The liver field has been debating for decades the contribution of the plasticity of the two epithelial compartments in the liver, hepatocytes and biliary epithelial cells (BECs), to derive each other as a repair mechanism. The hepatobiliary plasticity has been first observed in diseased human livers by the presence of biphenotypic cells expressing hepatocyte and BEC markers within bile ducts and regenerative nodules or budding from strings of proliferative BECs in septa. These observations are not surprising as hepatocytes and BECs derive from a common fetal progenitor, the hepatoblast, and, as such, they are expected to compensate for each other's loss in adults. To investigate the cell origin of regenerated cell compartments and associated molecular mechanisms, numerous murine and zebrafish models with ability to trace cell fates have been extensively developed. This short review summarizes the clinical and preclinical studies illustrating the hepatobiliary plasticity and its potential therapeutic application.

Prominent Pseudoacini in Focal Nodular Hyperplasia: A Potential Diagnostic Pitfall

Pseudoacini are generally a morphologic feature of hepatocellular carcinoma (HCC), being absent or rare in benign hepatocytic tumors, such as hepatocellular adenoma. However, rarely these can be seen in focal nodular hyperplasia (FNH) and may pose diagnostic challenges, especially when prominent. The study was aimed to evaluate the occurrence of pseudoacini in FNH and their clinicopathologic correlations. A total of 95 FNH cases diagnosed from 2005 to 2020 were included in the study. A pseudoacinus was defined as a circular arrangement of hepatocytes around a central dilated lumen present within the lobular parenchyma of the lesion with or without inspissated bile. Among the 95 FNH cases, 28 (29.5%) showed pseudoacini, which were prominent in 12 (12.6%) cases. Of these 3 occurred in patients above 50 years old. The pseudoacini were numerous in 3 cases, leading to an initial consideration of HCC in the differential diagnosis, and 1 case was diagnosed as well-differentiated hepatocellular neoplasm on initial biopsy. All 12 cases showed map-like staining pattern for glutamine synthetase. The hepatocytes forming the pseudoacini were positive for CK7 and HepPar1, while the inner lumina were highlighted by CD10 and bile salt export pump immunostains similar to adjacent canaliculi. The presence of prominent pseudoacini was not significantly associated with any clinical or pathologic features. The findings suggest that pseudoacini are likely manifestation of hepatocyte biliary transdifferentiation associated with chronic cholestasis in the lesion. This feature may pose a potential diagnostic pitfall especially on needle biopsies and awareness is needed to avoid misdiagnosing this as HCC.

Novel histological scoring for predicting disease outcome in primary sclerosing cholangitis

Background

Primary sclerosing cholangitis (PSC) is a progressive cholestatic liver disease that may lead to liver cirrhosis or cholangiocarcinoma. Liver histology and fibrosis stage are predictive markers of disease progression, and histological cirrhosis is defined as a significant endpoint. PSC‐specific histological scoring methods are lacking at present. We aimed to develop a tailored classification system for PSC, the PSC histoscore, based on histological features associated with disease progression.

Methods

In total, 300 PSC patients diagnosed between 1988 and 2018 were enrolled; their data were collected from the PSC registry (Helsinki University Hospital), and liver specimens were obtained from the Biobank of Helsinki. Five histological features included in the adapted Nakanuma scoring system and three additional parameters typical for PSC histology were evaluated and compared with the clinical and laboratory data. A compound endpoint consisting of liver transplantation, development of cholangiocarcinoma, or death was used as outcome measurement.

Results

Stage (fibrosis, bile duct loss, ductular reaction, and chronic cholestasis) and grade (portal inflammation, portal edema, hepatitis activity, and cholangitis activity) parameters were found to be independent predictive risk factors for the compound endpoint (P < 0.001). High disease grade (2–6) and stage (2–4) better correlated with clinical endpoints when evaluated with the PSC histoscore system compared to the adapted Nakanuma classification. The risk for disease progression in sequential endoscopic retrograde cholangiography (ERC) examinations was increased with elevated total PSC histoscores.

Conclusion

The PSC histoscore is a novel histological classification system for PSC. Our findings support the applicability of liver histology as a marker for disease progression.