Citations

Abstract In epilepsy patients lacking magnetic resonance imaging‐visible lesions such as focal cortical dysplasia (FCD), surgical resections target regions with abnormal electroencephalographic (EEG) activity. However, histopathological identification of subtle cortical architectural abnormalities in these specimens remains challenging. We investigated whether artificial intelligence (AI)‐based morphometric and spatial analysis of NeuN‐stained cortical sections could detect neuronal architectural disorganization in epilepsy resections, including regions without definitive histologic dysplasia. Whole slide images were generated from 83 FCD regions and 19 neurologically normal autopsy controls. Regions of interest were annotated in QuPath as FCD, FCD‐adjacent, FCD‐distant, apparently normal (abnormal EEG without histologic dysplasia), and true normal (autopsy controls). NeuN‐positive neurons were detected using QuPath (90% sensitivity, 12% false positive rate). Spatial and morphometric features—including neuronal clustering, distribution inhomogeneity, and nuclear morphology—were extracted and used to train a multinomial, Least Absolute Shrinkage and Selection Operator‐regularized regression classifier. The classifier achieved 70.6% accuracy in subregion classification and 88.2% accuracy in overall specimen diagnosis. Notably, regions with abnormal EEG but lacking histologic dysplasia exhibited quantifiable architectural disorganization similar to those seen in areas adjacent to FCD and distinct from control tissue. AI‐driven analysis of neuronal morphology and spatial distribution reveals subtle cortical disorganization in epilepsy resections, including in histologically ambiguous regions. Further investigation is warranted to determine if this methodology can enhance the diagnostic yield of neuropathological evaluation and support more precise surgical targeting in epilepsy.

Metabolic dysfunction-associated steatohepatitis (MASH) is a global health care burden. Appropriate large animal models mimicking the main MASH characteristics of steatosis, inflammation, and hepatocyte damage alongside progression of fibrosis are imperative for robust preclinical studies, especially in the field of hepatobiliary surgery. The present study aimed to characterize a novel model of steatohepatitis in fumarylacetoacetate hydroxylase-deficient ( FAH -/- ) pigs. FAH -/- pigs were generated using a CRISPR/Cas9 system. The animals received a choline-deficient, L-amino acid-defined high-fat diet and subtherapeutic doses of nitisinone and were followed for 3 months. Liver biopsies were obtained at baseline and every month during treatment. Steatosis, inflammation, and fibrosis were assessed by conventional histological scoring systems and by an artificial intelligence (AI) model. Serum liver parameters were analyzed every 2 weeks. Steatosis increased significantly throughout the study, with severe steatosis observed as early as 2 months into treatment. The inflammation score was increased in all animals after 3 months of treatment, whereas the AI-based CD45+ cell count showed region-specific trends in the portal and lobular areas. Collagen content and the corresponding fibrosis stage showed an increase over the 3-month period; however, the difference was not significant. Serum liver parameters did not show any relevant elevations during the study. In summary, we successfully developed and characterized a novel model of steatohepatitis in the FAH -/- pig within 3 months. Further studies with prolonged observation time and/or cycling of nitisinone administration are needed to evaluate whether progressive fibrosis and cirrhosis could be achieved with this model.

AimThe aim of this study was to compare the histopathologic features of MMF‐induced colitis and colonic GVHD and develop a digital tool using deep learning convolutional neural networks (CNNs) to semi‐automate the quantification of eosinophils.MethodsMMF and GVHD colitis cases were retrieved. A cohort of transplant patients who were at risk for developing GVHD and were on MMF at the time of biopsy was also identified. Cases were reviewed for histologic features and a deep learning CNN was trained to quantify lamina propria eosinophils. Clinical history was collected.ResultsNinety‐five patients were included in the study group: GVHD (n = 37), MMF (n = 25) and GVHD vs. MMF (n = 33). Within the GVHD vs. MMF group, one patient was thought to have definitive MMF colitis and one possible MMF colitis. The GVHD group had a significantly higher apoptotic count compared to the MMF group (median: 7 vs. 2, P = 0.03). Neuroendocrine aggregates were present in the GVHD (N = 12) and GVHD vs. MMF (N = 4) groups while absent in the MMF group. The MMF group had a higher semi‐quantitative eosinophil score and a higher automated eosinophil count as detected by the deep learning CNN (median eos/HPF: MMF 19.2, GVHD vs. MMF 3.6, GVHD 3.4, P < 0.001).ConclusionsLower eosinophil counts, higher apoptotic counts and neuroendocrine cell aggregates favour the diagnosis of GVHD. AI models for eosinophil quantification may serve as an ancillary tool for assessing this differential diagnosis. Given the rarity of MMF‐induced colitis in SCT patients, pathologists should have a high threshold for making this diagnosis in this population.

The nonalcoholic steatohepatitis clinical research network (NASH-CRN) system is commonly used for histologic assessment of disease activity and fibrosis in NASH. Despite this, the system does not fully capture the range of disease activity and fibrosis. As such, an expanded NAS (E-NAS) grading and staging system with a calculated E-NAS index was developed by our group. In this follow up study, we aim to revalidate the E-NAS system and compare its reliability to existing systems. Hematoxylin and eosin and trichrome stained sections from 40 liver biopsies were reviewed digitally by four hepatopathologists and assessed using the NASH-CRN and E-NAS systems as well as a modified Ishak fibrosis stage. The pathologist's gestalt impression of disease activity and fibrosis was scored on a Visual Analogue Scale (VAS), which ranged from 0 (no activity/fibrosis) to 100 (the worst activity/fibrosis ever seen). Inter-rater reliability was assessed, and Spearman correlation coefficients were calculated. The E-NAS index had higher inter-rater agreement versus the NAS score (ICC 0.70 vs 0.61). The inter-rater agreement for ballooning in the E-NAS system was also higher at 0.67 compared to the NAS (ICC 0.60). ICCs for fibrosis were comparable between all the systems assessed (0.78 to 0.88). Finally, the calculated E-NAS index was higher with increasing stage of fibrosis compared to the NAS suggesting that it associates better with fibrosis. In summary, the E-NAS system demonstrates substantial inter-rater reliability as well as improved correlation with disease activity VAS and fibrosis compared to the NAS score.

AbstractObjectivesEosinophilic esophagitis (EoE) is often diagnosed in school‐age children between 6‐ and 9‐year‐old. There is less known about those who are diagnosed with EoE that are younger than 6 years old. The objective of this study is to compare clinical presentation, comorbidities, and outcomes based on age at diagnosis of EoE.MethodsSingle‐center retrospective chart review of children (<18 years) diagnosed with EoE between 2005 and 2020. We recorded demographics, clinical presentation, family history, past medical history, treatment, and endoscopic findings. Children in this cohort were classified based on age into three age groups: <2 years, 2−<6 years, and 6−<18 years.ResultsWe identified 256 children with EoE, the mean age (SD) at the time of diagnosis was 9 (5.2) years and 184 (72%) were male. We had 164 (64%) patients with available follow‐up esophagogastroduodenoscopies (EGDs) data (495 EGDs in total) of those 99/164 (60%) reached mucosal remission. In the very young children (<2 years) vomiting was the most common presentation, while poor weight gain was seen more in the 2−<6‐year group in comparison to the >6‐years. Food impaction and abdominal pain were most likely to present in older children 6−18 years. Combination therapy, as opposed to a single therapy, induced remission at a higher frequency in the <6‐year group in comparison to the 6−<18‐year group (85% vs. 66%).ConclusionEoE should be considered in younger children presenting with feeding difficulty and poor weight gain. Combination therapy seems to be more effective in younger children with EoE, but further studies with bigger sample size are needed to study the efficacy of the different combination therapies.

Aims Reticulin stain is used routinely in the histological evaluation of hepatocellular carcinoma (HCC). The goal of this study was to assess whether the histological reticulin proportionate area (RPA) in HCCs predicts tumour‐related outcomes. Methods and results We developed and validated a supervised artificial intelligence (AI) model that utilises a cloud‐based, deep‐learning AI platform (Aiforia Technologies, Helsinki, Finland) to specifically recognise and quantify the reticulin framework in normal livers and HCCs using routine reticulin staining. We applied this reticulin AI model to a cohort of consecutive HCC cases from patients undergoing curative resection between 2005 and 2015. A total of 101 HCC resections were included (median age = 68 years, 64 males, median follow‐up time = 49.9 months). AI model RPA reduction of > 50% (compared to normal liver tissue) was predictive of metastasis [hazard ratio (HR) = 3.76, P = 0.004, disease‐free survival (DFS, HR = 2.48, P < 0.001) and overall survival (OS), HR = 2.80, P = 0.001]. In a Cox regression model, which included clinical and pathological variables, RPA decrease was an independent predictor of DFS and OS and the only independent predictor of metastasis. Similar results were found in the moderately differentiated HCC subgroup (WHO grade 2), in which reticulin quantitative analysis was an independent predictor of metastasis, DFS and OS. Conclusion Our data indicate that decreased RPA is a strong predictor of various HCC‐related outcomes, including within the moderately differentiated subgroup. Reticulin, therefore, may represent a novel and important prognostic HCC marker, to be further explored and validated.

Abstract Objectives Cirrhosis-like hepatocellular carcinoma (CL-HCC) is a rare hepatocellular malignancy characterized by multiple tumor nodules that clinically, radiologically, macroscopically, and microscopically mimic cirrhosis. We aimed to elucidate the molecular biology of CL-HCC and determine tumor nodule clonality. Methods We performed RNA sequencing on formalin-fixed, paraffin-embedded tissue from confirmed CL-HCC cases (n = 6), along with corresponding nonneoplastic hepatic tissue (n = 4) when available. Transcriptomes from our previous work on steatohepatitic hepatocellular carcinoma and The Cancer Genome Atlas (TCGA) were used for comparison purposes. Results Histologically, CL-HCC displayed innumerable nodules and extensive vascular invasion. Intratumoral nodule comparison indicated that the multiple nodules were all clonally related, not independent primaries. The unique histomorphologic appearance corresponded with a distinct transcriptome compared with other HCCs, including fibrolamellar HCC (n = 6), steatohepatitic HCC (n = 8), and conventional HCC in TCGA (n = 424). Tumor-normal gene expression analysis revealed consistent overexpression of several genes involved in degradation of tissue matrix. No recurrent translocations or point mutations were identified. CL-HCC showed a gene expression profile indicative of zone 2 hepatocytes. Conclusions CL-HCC’s distinctive clinicopathologic features correspond to a unique gene expression profile, increased expression of invasive markers, features of zone 2 hepatocytes, and features suggestive of intratumoral nodule monoclonality.

AbstractBackgroundNodular regenerative hyperplasia (NRH) is a rare condition characterized clinically by the development of non‐cirrhotic portal hypertension. NRH is the histopathological result in the liver of various systemic disease processes including autoimmune disorders, haematological malignancies and medications. However, natural history of this condition has been limited to small case series while patient outcomes pertaining to different aetiologies of NRH are largely unknown.MethodsA retrospective cohort of consecutive patients diagnosed with pathology‐confirmed NRH at Mayo Clinic between 2002 and 2017 was identified. The histological diagnosis of NRH was determined by expert liver pathologists. Patients with metastatic liver disease, history of liver transplantation or younger than 18 were excluded. Potential aetiologies of NRH were classified as haematological, rheumatological, drug‐associated, miscellaneous or idiopathic. Long‐term mortality was analysed using Kaplan‐Meier estimation and Cox regression models.ResultsOne hundred and sixty‐seven consecutive patients with pathology‐confirmed NRH were analysed over a 15‐year period and followed for a median time of 50 months (1–306 months). The mean age at diagnosis was 53 years. No aetiology or risk factor for NRH was identified in the majority of patients (94, 56.3%), whereas an associated, possibly causal, condition was found in 73 patients (secondary NRH). The most common presenting feature was elevated liver tests (80%), but no significant differences in laboratory tests were seen based on aetiology of NRH. Compared to idiopathic NRH, those with an identified cause had a higher rate of splenomegaly at presentation (54% vs. 27%, p = 0.002). Portal hypertension‐related complications at diagnosis were common, with ascites present in one‐third of patients. Overall transplant‐free survival was 63% at 5 years. Median survival in idiopathic NRH was 9.4 years compared to 7.3 years in secondary NRH. Age, renal function and volume status at presentation were significantly associated with survival; however, MELD score was not.ConclusionsThe rates of liver‐related complications and mortality in NRH are low, and only a small number of patients ultimately require liver transplantation. Most patients do not have an identified risk factor or aetiology for NRH, and liver‐related outcomes do not appear to differ based on associated, possibly causal, conditions.

AimsThe diagnosis of focal nodular hyperplasia (FNH) and the interpretation of glutamine synthetase (GS) staining can be challenging on biopsies. We aimed to evaluate the reproducibility of needle biopsy diagnosis of FNH, the effect of GS immunohistochemistry on FNH diagnosis, and which histological features are most useful for the diagnosis of FNH.Methods and resultsThe study included virtual needle biopsies generated from 75 resection specimens (30 FNHs, 15 hepatocellular adenomas, 15 hepatocellular carcinomas, and 15 non‐lesional liver specimens). Pathologists were reasonably accurate (83.1%) in the diagnosis of FNH with haematoxylin and eosin alone. Ductular reaction and nodularity had the highest sensitivity for a diagnosis of FNH (88.1% and 82.2%, respectively), whereas central scar was the most specific feature (90.6%). The presence of two or more of the classic histological features had 89.6% sensitivity and 86.2% specificity for a diagnosis of FNH. Diagnostic accuracy was significantly higher with the addition of a GS stain. A map‐like GS staining pattern was highly specific (99.3%) for FNH. However, GS staining was interpreted as non‐map‐like in 14.4% of reviews of true FNH cases, and overall interobserver agreement for interpretation of the GS staining pattern was only moderate (kappa = 0.42).ConclusionsPathologists are reasonably accurate in the diagnosis of FNH on virtual biopsies, and GS staining improves accuracy. However, a subset of FNH cases remain challenging. Steatosis and a pseudo‐map‐like GS staining pattern were associated with increased difficulty. Therefore, although a map‐like GS staining pattern is useful for confirmation of a diagnosis, the lack of a map‐like GS staining pattern on needle biopsy does not necessarily exclude a diagnosis of FNH.

Abstract The precise characterization of the lobular architecture of the liver has been subject of investigation since the earliest historical publications, but an accurate model to describe the hepatic lobular microanatomy is yet to be proposed. Our aim was to evaluate whether Voronoi diagrams can be used to describe the classic liver lobular architecture. We examined the histology of normal porcine and human livers and analyzed the geometric relationships of various microanatomic structures utilizing digital tools. The Voronoi diagram model described the organization of the hepatic classic lobules with overall accuracy nearly 90% based on known histologic landmarks. We have also designed a Voronoi-based algorithm of hepatic zonation, which also showed an overall zonal accuracy of nearly 90%. Therefore, we have presented evidence that Voronoi diagrams represent the basis of the two-dimensional organization of the normal liver and that this concept may have wide applicability in liver pathology and research.