Smith, Maxwell L.

IgA vasculitis (IgAV) and IgA nephropathy (IgAN) are closely related disorders that usually present in childhood. Pulmonary involvement is rare and histopathologic features thereof in adults are poorly understood. Institutional archives were searched for adults with IgAV or IgAN and diffuse parenchymal lung disease. Ten patients (6 men, median age 59 years) with lung tissue sampling were enrolled. Clinical history and pathology slides were reviewed. All patients were diagnosed with IgAV or IgAN before or concurrently with lung disease. Symptoms were nonspecific but hemoptysis was common. Radiologically, bilateral ground-glass opacities, consolidation, and reticulonodular densities were typical. Histologically, 6 cases (60%) featured acute or subacute diffuse alveolar hemorrhage (DAH) accompanied by definite (40%) or probable (20%) capillaritis. Two cases (20%) featured resolved DAH only, manifesting as alveolar hemosiderosis without other changes. And 2 cases featured organizing pneumonia without DAH. Five patients had a chronic interstitial pneumonia in the background, including 3 with nonspecific interstitial pneumonia (NSIP) and 2 with unclassifiable fibrosis. Findings were similar in IgAV versus IgAN. Of 4 patients with follow-up information, 2 died during hospitalization, one died 9 weeks later of secondary complications, and one was treated with rituximab and nintedanib and is alive 2 years later. To our knowledge, this is the largest series detailing pulmonary histopathologic findings in adults with IgAV or IgAN. Most cases show acute DAH with capillaritis; less commonly, old DAH, organizing pneumonia, NSIP, or unclassifiable fibrosis may be observed. Some patients do well with treatment but fatal cases also occur.

ABSTRACT We report the first known use of a trans-sigmoidal endoscopic ultrasound-guided biopsy in a patient with a simultaneous pancreas-kidney transplant, following failed conventional percutaneous biopsy methods. A 50-year-old man with a simultaneous pancreas-kidney transplant was suspected of graft dysfunction. Multiple percutaneous biopsies were unsuccessful due to anatomical barriers. After a multidisciplinary review, a trans-sigmoidal endoscopic ultrasound approach was attempted, yielding sufficient tissue for histopathology without complications. Histology revealed no evidence of acute rejection. This novel technique expands the potential for biopsy in complex transplant anatomies and should be considered in highly selected cases within specialized centers.

AimsPolymyalgia rheumatica (PMR) is a chronic autoimmune disorder that mainly affects older adults. Pulmonary disease in PMR is rare but may be under‐recognized and pathological descriptions thereof are few. We aimed to characterize diffuse parenchymal lung disease (DPLD) in PMR.Methods and resultsInstitutional archives were searched for patients having PMR and DPLD with lung tissue sampling. After excluding cases with infection, concomitant rheumatoid arthritis, or smoking‐related DPLD only, 11 patients (9 women, median age 75 years) were enrolled. Clinical history and pathology slides were reviewed. One of the 11 patients (9%) had concomitant giant cell arteritis; the remaining patients had no other rheumatological diseases. All had been treated for PMR with immunosuppression, and most presented years later (median 6 years) with non‐specific respiratory symptoms. Radiographically, bilateral ground‐glass opacities and reticulation were typical and were usually lower lobe predominant. Histologically, fibrosis was seen in 8 of 11 (73%) patients and was unclassifiable in four; non‐specific interstitial pneumonia was encountered in three patients, and usual interstitial pneumonia was seen in only one case. Evidence of acute lung injury occurred in 9 (82%) patients, including three with acute lung injury only, usually manifesting as organizing pneumonia. Diffuse alveolar haemorrhage was seen in four cases (29%), including two patients with haemoptysis and capillaritis.ConclusionsOur data corroborate prior reports of clinically significant DPLD in some patients with PMR. Histopathological findings mirror other rheumatological disorders and include diffuse alveolar haemorrhage with capillaritis. Additional studies are warranted to clarify the association between PMR and DPLD.

Introduction. Increased steatosis on preimplant liver frozen section is associated with delayed graft function and primary nonfunction. Efforts to standardize histologic assessment have proven difficult. Frozen section artifact and lipopeliosis complicate the detection of steatosis. We aimed to develop and validate an AI model to recognize large droplet fat and fat induced artifact (FIA)/lipopeliosis on preimplantation frozen section and to correlate the AI results with post-transplant clinical parameters. Methods. The model was applied to 161 consecutive liver transplant specimens with preimplant slides. Results were correlated with traditional and Banff histologic assessment and clinical parameters. Results. By traditional assessment, steatosis ranged from 0%–40%. The AI model identified a range of 0 to 15.9% steatosis. There was no difference in patient survival by any measures of steatosis. AI steatosis correlated with increased risk of early allograft dysfunction (OR = 1.63, P < .001), respiratory failure (OR = 1.21, P = .003), and more advanced fibrosis (OR = 1.18, P = .030), but was not correlated with graft or patient survival. FIA/lipopeliosis were identified in a range of 0 to 6.42%. In univariate analysis the percentage of FIA/lipopeliosis correlated with both graft and patient survival ( P = .044 and P = .009, respectively), but was not associated with increased risk of early allograft dysfunction, respiratory failure, or advanced fibrosis. Conclusions. We developed an AI model that quantitates large droplet fat and FIA/lipopeliosis on frozen section slides and found a correlation with post-transplant outcomes. Further studies on larger, multi-institutional cohorts with higher fat containing donors are necessary to determine the role this model may have in organ acceptance decisions.

Transbronchial cryobiopsies (CB) are increasingly replacing surgical biopsies (video-assisted thoracoscopic/VATS biopsies) for diagnosing diffuse parenchymal lung disease (interstitial lung disease, ILD), but there is very little guidance for pathologists on CB interpretation. Here we propose a fairly simple approach. First, if the diagnosis can be made on a traditional forceps biopsy, it can be made on a cryobiopsy. Many diseases with specific features will fall into this category (eg, sarcoidosis or Langerhans cell histiocytosis). More problematic are patterns such as usual interstitial pneumonia (UIP) or nonspecific interstitial pneumonia (NSIP), in which low-power architecture is the key to diagnosis. In this circumstance, an adequate sample is crucial to look for features such as fibroblast foci, because a combination of fibroblast foci plus any patchy old fibrosis, fibrotic architectural remodeling, or honeycombing, allows a diagnosis of a UIP pattern. However, in most instances, CB will not separate the UIP patterns seen in idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis, or connective tissue disease-interstitial lung disease (CTD-ILD), although giant cells/granulomas (uncommon findings) in this setting favor fibrotic hypersensitivity pneumonitis. Fibroblast foci can be difficult to differentiate from organizing pneumonia (OP), but granulation tissue plugs clearly in airspaces favor OP. Absent fibroblast foci, patchy old fibrosis, architectural distortion, and honeycombing by themselves do not allow a specific diagnosis. NSIP in CB microscopically looks like NSIP in VATS biopsies, and the presence of an NSIP or an NSIP+OP pattern is typical of CTD-ILD. All the above diagnoses require correlation with clinical and radiologic findings.

Abstract Introduction: Endobronchial optical coherence tomography (EB-OCT) imaging has unique potential to diagnose and quantify early, microscopic disease changes and assess therapeutic responsivity in interstitial lung disease (ILD). However, training and time requirements for interpretation and quantitative evaluation of large volumetric EB-OCT datasets need to be optimized to facilitate clinical adoption. We develop and validate a computationally-efficient, rapid EB-OCT quantitative image analysis framework using artificial intelligence (AI) with deep learning architecture for 2D feature segmentation, 3D feature burden and spatial distribution mapping, and computer-aided diagnosis in early ILD subjects. Methods: Volumetric EB-OCT datasets, consisting of 2D cross-sectional images, were acquired from multiple locations in the bilateral lungs of ILD and healthy control subjects. EB-OCT features were manually segmented from cross-sectional images by an expert EB-OCT reader, including fibrosis, microscopic honeycombing, traction bronchiolectasis, normal parenchyma, and emphysema. EB-OCT images with corresponding feature segmentations were used to train and validate a multiclass convolutional neural network. The 2D AI-based segmentation model was subsequently evaluated against manual ‘ground truth’ segmentations for each feature in a de novo ‘test’ dataset of independent subjects using dice similarity score, precision, recall/sensitivity, specificity, and balanced accuracy. To generate 3D AI-based quantitative feature burden and spatial distribution maps, AI-based feature segmentation was performed on each EB-OCT 2D cross-sectional image within a volumetric imaging site. Three external pathologist readers independently interpreted 3D AI-based features maps for each EB-OCT imaging site acquired for a subject (without access to EB-OCT images) and provided a single diagnosis of usual interstitial pneumonia (UIP) or non-UIP ILD. Performance of computer-aided ILD diagnosis was compared against independently evaluated histopathology, clinical diagnosis, and prior EB-OCT image interpretation. Results: EB-OCT was acquired in 51 subjects: 30 in vivo early ILD, 5 in vivo non-ILD controls, and 16 ex vivo end-stage ILD. Overall pixel similarity between 2D manual and AI-based segmentation demonstrated high accuracy for each ILD feature (mean balanced accuracy 0.9). 3D quantitative AI-based feature maps demonstrated strong agreement (Spearman ρ0.87, p&lt;0.001) between manual and AI-based segmentation. AI-based computer-aided diagnosis demonstrated 100% sensitivity/specificity for histologic UIP and clinical IPF for all pathologist readers, with improved diagnostic accuracy and significantly reduced training (45-minute training, 75% reduction) and interpretation time (&lt;1 minute per subject for computer-aided diagnosis, 88% reduction) compared to EB-OCT image interpretation. Conclusion: The AI-based EB-OCT image analysis framework could expedite computer-aided diagnosis of early ILD and robust, quantitative assessment of microscopic disease burden, progression, and therapeutic responsivity.

ABSTRACTBackground and ObjectiveThe diagnosis of interstitial lung diseases (ILDs) often relies on the integration of various clinical, radiological, and histopathological data. Achieving high diagnostic accuracy in ILDs, particularly for distinguishing usual interstitial pneumonia (UIP), is challenging and requires a multidisciplinary approach. Therefore, this study aimed to develop a multimodal artificial intelligence (AI) algorithm that combines computed tomography (CT) and histopathological images to improve the accuracy and consistency of UIP diagnosis.MethodsA dataset of CT and pathological images from 324 patients with ILD between 2009 and 2021 was collected. The CT component of the model was trained to identify 28 different radiological features. The pathological counterpart was developed in our previous study. A total of 114 samples were selected and used for testing the multimodal AI model. The performance of the multimodal AI was assessed through comparisons with expert pathologists and general pathologists.ResultsThe developed multimodal AI demonstrated a substantial improvement in distinguishing UIP from non‐UIP, achieving an AUC of 0.92. When applied by general pathologists, the diagnostic agreement rate improved significantly, with a post‐model κ score of 0.737 compared to 0.273 pre‐model integration. Additionally, the diagnostic consensus rate with expert pulmonary pathologists increased from κ scores of 0.278–0.53 to 0.474–0.602 post‐model integration. The model also increased diagnostic confidence among general pathologists.ConclusionCombining CT and histopathological images, the multimodal AI algorithm enhances pathologists' diagnostic accuracy, consistency, and confidence in identifying UIP, even in cases where specialised expertise is limited. image

ABSTRACTThis single‐center retrospective study investigated subclinical rejection prevalence and significance in simultaneous pancreas and kidney transplant (SPKT) recipients. We analyzed 352 SPKT recipients from July 2003 to April 2022. Our protocol included pancreas allograft surveillance biopsies at 1, 4, and 12months post‐transplant. After excluding 153 patients unable to undergo pancreas biopsy, our study cohort comprised 199 recipients. Among the 199 patients with protocol pancreas biopsies, 107 had multiple protocol pancreas biopsies in the first year, totaling 323. Subclinical rejection was identified in 132 episodes (41%). Of these, 72% were Grade 1, 20% were indeterminate, and 8% were Banff Grade 2 or higher. All episodes of subclinical rejection were treated. Rates of pancreas graft loss (10% vs. 7%) and clinical rejection (21% vs. 20%) at 3 years were similar between those with and without subclinical rejection. Subclinical rejection Banff Grade 2 or more was associated with poor pancreas graft survival HR of 5.5 (95% CI: 1.24–24.37, p = 0.025). Of 236 simultaneous protocol kidney and pancreas biopsies, 102 (43%) showed pancreas subclinical rejection, while only 17% had concurrent kidney subclinical rejection. Our findings suggest limited predictive value of pancreatic enzymes and euglycemia in detecting pancreas rejection. Furthermore, poor concordance existed between pancreas and kidney subclinical rejection.