Hartley, Christopher P.

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.

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.

AbstractBackgroundObtaining a diagnosis and treating pulmonary malignancies during the same anesthesia requires either an on‐site pathologist or a system for remotely evaluating microscopic images. Cytology specimens are challenging to remotely assess given the need to navigate through dispersed and three‐dimensional cell clusters. Remote navigation is possible using robotic telepathology, but data are limited on the ease of use of current systems, particularly for pulmonary cytology.MethodsAir dried modified Wright‐Giemsa stained slides from 26 touch preparations of transbronchial biopsies and 27 smears of endobronchial ultrasound guided fine needle aspirations were scored for ease of adequacy assessment and ease of diagnosis on robotic (rmtConnect Microscope) and non‐robotic telecytology platforms. Diagnostic classifications were compared between glass slides and the robotic and non‐robotic telecytology assessments.ResultsCompared to non‐robotic telecytology, robotic telecytology had a greater ease of adequacy assessment and non‐inferior ease of diagnosis. The median time to diagnosis using robotic telecytology was 85 s (range 28–190 s). Diagnostic categories were concordant for 76% of cases in robotic versus non‐robotic telecytology and 78% of cases in robotic telecytology versus glass slide diagnosis. Weighted Cohen's kappa scores for agreement in these comparisons were 0.84 and 0.72, respectively.ConclusionsUse of a remote‐controlled robotic microscope improved the ease of adequacy assessment compared to non‐robotic telecytology and enabled strongly concordant diagnoses to be expediently rendered. This study provides evidence that modern robotic telecytology is a feasible and user‐friendly method of remotely and potentially intraoperatively rendering adequacy assessments and diagnoses on bronchoscopic cytology specimens.

AbstractIntroductionIn evaluating malignant pleural fluid cytology, metastatic adenocarcinomas and mesotheliomas are often differential diagnoses. GATA binding protein 3 (GATA3) has historically been used to confirm metastatic breast carcinomas; however, GATA3 has low specificity if mesothelioma is included in differential diagnoses. Trichorhinophalangeal syndrome type 1 (TRPS1) protein is expressed in all types of breast carcinomas, with reported high specificity and sensitivity. We investigated the performance of TRPS1 immunohistochemistry (IHC) and compared it to GATA3 in pleural fluids diagnosed with metastatic breast carcinoma and mesothelioma.MethodsThirty‐six consecutive ThinPrep pleural fluids and 4 pleural fine needle aspirations (FNAs) with diagnoses of metastatic breast carcinoma (21) and mesothelioma (19) were retrieved, and IHC with TRPS1 and GATA3 was performed on all. Immunoreactivity scores for TRPS1 were calculated by multiplying percentage of immunoreactive cells by staining intensity. Immunoreactivity scores were negative if 0 or 1, low positive if 2, intermediate positive if 3 or 4, or high positive if 6 or 9. Nuclear immunoreactivity of ≥10% with at least moderate intensity was judged GATA3 positive.ResultsGATA3 showed immunoreactivity in all metastatic breast carcinomas and 84% of mesotheliomas. TRPS1 was immunoreactive in all breast carcinoma cases (18 with a score of 9 and 3 with a score of 6). TRPS1 showed low positivity in 5% of mesothelioma cases with all other cases being negative.ConclusionWhen cytomorphologic differential diagnoses of mesothelioma exist, TRPS1 is a more specific marker than GATA3 for confirmation of metastatic breast carcinoma in pleural fluid cytology.

AbstractLymphangioleiomyomatosis (LAM) is a rare disease with variable presentations. The neoplastic cells in LAM demonstrate a unique and diagnostically important “myomelanocytic” phenotype. Cytologic reports of LAM are infrequent and have not in the past emphasized the floating island pattern in which circumscribed aggregates of lesional cells are rimmed by appliques of flattened endothelium. This case illustrates the cytology of LAM and emphasizes that the floating island cytoarchitectural pattern more classically associated with entities such as hepatocellular carcinomas may be seen in cytological preparations of LAM at unanticipated body sites.

The published literature on cervicovaginal cytology includes fewer than ten reported cases of Cokeromyces recurvatus identified in Pap test samples. We report a unique case of an asymptomatic 27-year-old female with persistent gynecologic tract colonization by C. recurvatus in which distinctive fungal microorganisms were identified in three samples collected over three consecutive years.

Abstract Objectives Autoimmune hepatitis (AIH) is a form of severe hepatitis that can recur after orthotopic liver transplant (OLT). Presentation of AIH in patients with OLT who do not have a history of AIH is called de novo AIH (DNAIH). We evaluated the clinicopathologic characteristics of AIH and DNAIH. Methods Clinicopathologic and outcome measures of 11 patients with recurrent AIH (RAIH) and 22 with DNAIH identified between 2000 and 2017 were compared. Results Both cohorts showed female predominance. The mean clinical follow-up was 13 and 7.8 years in the in the RAIH and DNAIH groups, respectively (P = .1). Moderate portal inflammation was more common in patients with RAIH (64% vs 27%, P = .043). A trend was observed for more cases of DNAIH showing severe inflammation (36% vs 9%, P = .09) and submassive necrosis compared with RAIH (23% vs 0%, P = .086). A trend for more advanced fibrosis was also noted in the RAIH group (27% vs 5%, P = .059). Three patients with RAIH lost their grafts because of RAIH. Five-year disease-specific graft survival (GS) (P = .012) and overall GS (P = .015) were worse in patients with RAIH. Complement component 4d immunohistochemistry was positive in 2 patients with RAIH and 3 with DNAIH but showed no correlation with GS or other parameters. Conclusions RAIH seems to have a more aggressive clinical course than DNAIH and warrants closer clinical follow-up and aggressive treatment.