Eosinophilic esophagitis: Does age matter?

OBJECTIVES: Eosinophilic 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. METHODS: Single-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. RESULTS: We 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%). CONCLUSION: EoE 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.

WITHDRAWN: Neoadjuvant therapy leads to objective response in intrahepatic cholangiocarcinoma.

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.hpb.2024.04.011. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Neoadjuvant therapy leads to objective response in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (iCCA) is the second most common hepatic malignancy and has a poor prognosis. Surgical resection is the standard of care for patients with resectable disease, representing 30-40% of cases. Increasingly, neoadjuvant systemic therapy is being utilized in patients due to high-risk anatomic or biologic considerations. However, data on the clinical effect of this approach are limited. We performed a cohort study to evaluate the effect of neoadjuvant therapy in patients with oncologically high-risk iCCA. METHODS: iCCA patients (n = 181) between the years 2014-2020 were reviewed for clinical, histopathologic, treatment, and outcome-related data. Tumor regression grade was scored per CAP criteria for gastrointestinal carcinomas. RESULTS: 47 iCCA patients received neoadjuvant therapy and 72 did not. Neoadjuvant treatment led to objective response and tumor regression by CAP score. After adjustment for age, clinical stage, and tumor size, the outcomes of patients who had neoadjuvant therapy followed by surgery were not significantly different from those patients who had surgery first. DISCUSSION: In conclusion, neoadjuvant therapy in iCCA facilitated surgical care. The progression-free and overall survival for surgical patients with and without neoadjuvant therapy were not significantly different suggesting this approach needs further exploration as an effective treatment paradigm.

Digital Hepatic Iron Content: An Artificial Intelligence Model for Spatially Resolved Histologic Iron Quantitative Analysis in Liver Samples.

Currently, the precise evaluation of tissue hepatic iron content (HIC) requires laboratory testing using tissue-destructive methods based on colorimetry or spectrophotometry. To maximize the use of routine histologic stains in this context, we developed an artificial intelligence (AI) model for the recognition and spatially resolved measurement of iron in liver samples. Our AI model was developed using a cloud-based, supervised deep learning platform (Aiforia Technologies). Using digitized Pearl Prussian blue iron stain whole slide images representing the full spectrum of changes seen in hepatic iron overload, our training set consisted of 59 cases, and our validation set consisted of 19 cases. The study group consisted of 98 liver samples from 5 different laboratories, for which tissue quantitative analysis using inductively coupled plasma mass spectrometry was available, collected between 2012 and 2022. The correlation between the AI model % iron area and HIC was Rs = 0.93 for needle core biopsy samples (n = 73) and Rs = 0.86 for all samples (n = 98). The digital hepatic iron index (HII) was highly correlated with HII > 1 (area under the curve [AUC] = 0.93) and HII > 1.9 (AUC = 0.94). The percentage area of iron within hepatocytes (vs Kupffer cells and portal tract iron) identified patients with any hereditary hemochromatosis-related mutations (either homozygous or heterozygous) (AUC = 0.65, P = .01) with at least similar accuracy than HIC, HII, and any histologic iron score. The correlation between the Deugnier and Turlin score and the AI model % iron area for all patients was Rs = 0.87 for total score, Rs = 0.82 for hepatocyte iron score, and Rs = 0.84 for Kupffer cell iron score. Iron quantitative analysis using our AI model was highly correlated with both detailed histologic scoring systems and tissue quantitative analysis using inductively coupled plasma mass spectrometry and offers advantages (related to the spatial resolution of iron analysis and the nontissue-destructive nature of the test) over standard quantitative methods.

Adding Inflammatory Markers and Refining National Institute on Alcohol Abuse and Alcoholism Criteria Improve Diagnostic Accuracy for Alcohol-associated Hepatitis.

BACKGROUND & AIMS: Although histology is considered the gold standard for diagnosis of alcohol-associated hepatitis (AH), it is not required for entry into therapeutic studies if patients meet National Institute on Alcohol Abuse and Alcoholism (NIAAA) consensus criteria for probable AH. Our aim was to assess the diagnostic accuracy of NIAAA criteria against liver biopsy and to explore new criteria to enhance diagnostic accuracy for AH. METHODS: A total of 268 consecutive patients with alcohol-related liver disease with liver biopsy were prospectively included: 210 and 58 in the derivation and validation cohorts, respectively. NIAAA criteria and histological diagnosis of alcoholic steatohepatitis (ASH) were independently reviewed by clinical investigators and pathologists from Hospital Clínic and Mayo Clinic. Using biopsy-proven ASH as gold standard we determined diagnostic capability of NIAAA criteria and proposed the new improved criteria. RESULTS: In the derivation cohort, diagnostic accuracy of NIAAA for AH was modest (72%) due to low sensitivity (63%). Subjects who did not meet NIAAA with ASH at liver biopsy had lower 1-year survival compared with subjects without ASH (70% vs 90%; P < .001). NIAAAm-CRP criteria, created by adding C-reactive protein and modifying the variables of the original NIAAA, had higher sensitivity (70%), accuracy (78%), and specificity (83%). Accuracy was also higher in a sensitivity analysis in severe AH (74% vs 65%). In the validation cohort, NIAAAm-CRP and NIAAA criteria had a sensitivity of 56% vs 52% and an accuracy of 76% vs 69%, respectively. CONCLUSION: NIAAA criteria are suboptimal for the diagnosis of AH. The proposed NIAAAm-CRP criteria may improve accuracy for noninvasive diagnosis of AH in patients with alcohol-related liver disease.

Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

Artificial intelligence-based reticulin proportionate area - a novel histological outcome predictor in hepatocellular carcinoma.

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.

Metallothionein immunohistochemistry has high sensitivity and specificity for detection of Wilson disease.

Diagnosis of Wilson disease (WD) can be difficult because of its protean clinical presentations, but early diagnosis is important because effective treatment is available and can prevent disease progression. Similarly, diagnosis of WD on liver biopsy specimens is difficult due to the wide range of histologic appearances. A stain that could help identify WD patients would be of great value. The goal of this study was to use mass spectrometry-based proteomics to identify potential proteins that are differentially expressed in WD compared to controls, and could serve as potential immunohistochemical markers for screening. Several proteins were differentially expressed in WD and immunohistochemical stains for two (metallothionein (MT) and cytochrome C oxidase copper chaperone (COX17)) were tested and compared to other methods of diagnosis in WD including copper staining and quantitative copper assays. We found diffuse metallothionein immunoreactivity in all liver specimens from patients with WD (n = 20); the intensity of the staining was moderate to strong. This staining pattern was distinct from that seen in specimens from the control groups (none of which showed strong, diffuse staining), which included diseases that may be in the clinical or histologic differential of WD (steatohepatitis (n = 51), chronic viral hepatitis (n = 40), autoimmune hepatitis (n = 50), chronic biliary tract disease (n = 42), and normal liver (n = 20)). COX17 immunostain showed no significant difference in expression between the WD and control groups. MT had higher sensitivity than rhodanine for diagnosis of WD. While the quantitative liver copper assays also had high sensitivity, they require more tissue, have a higher cost, longer turnaround time, and are less widely available than an immunohistochemical stain. We conclude that MT IHC is a sensitive immunohistochemical stain for the diagnosis of WD that could be widely deployed as a screening tool for liver biopsies in which WD is in the clinical or histologic differential diagnosis.

Outcomes of idiopathic versus secondary nodular regenerative hyperplasia of the liver: A longitudinal study of 167 cases.

BACKGROUND: Nodular 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. METHODS: A 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. RESULTS: One 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. CONCLUSIONS: The 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.

Diagnostic challenges of focal nodular hyperplasia: interobserver variability, accuracy, and the utility of glutamine synthetase immunohistochemistry.

AIMS: The 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 RESULTS: The 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). CONCLUSIONS: Pathologists 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.