E-cigarette or vaping-associated lung injury (EVALI) was initially recognized as a public health problem in 2019, but pathologic changes from vaping (if any) outside the context of EVALI remain unknown. We aimed to further characterize the pathology of EVALI and vaping-associated changes in otherwise healthy patients without EVALI, including those presenting with spontaneous pneumothorax. Archives were searched for lung specimens from these patient groups (2019 to 2025). Previously reported cases were excluded. Clinical history and pathology slides were reviewed. Sixty-three patients (43 men; mean age: 36.0±15.2 y) were enrolled, including 47 wedge resections, 15 transbronchial biopsies, and 1 core biopsy. All reported current or recent vaping, and 24 reported prior or concurrent cigarette smoking. Thirty-six met criteria for EVALI and showed characteristic histologic features; novel findings included 12 (35%) with coarse lipid vacuolization and 6 (18%) with grungy proteinosis-like deposits. Eleven patients with prior suspected EVALI had chronic respiratory symptoms with airway-centered macrophages and variable airway-centered fibrosis. The remaining 16 patients presented with spontaneous pneumothoraces without EVALI and had rare foamy or lightly pigmented macrophages in airspaces but no other abnormalities beyond secondary changes from the pneumothorax. To our knowledge, this is the largest pathologic study of EVALI and the first to review histologic findings in the lungs of otherwise healthy vapers. EVALI is less common than in 2019 but is still encountered. Outside the context of EVALI, patients who vape and develop spontaneous pneumothorax have minimal histologic changes. The role (if any) of vaping in the development of pneumothorax remains unknown.

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.

Lymphangioleiomyomatosis (LAM) is a low-grade neoplasm in the family of perivascular epithelioid cell (PEComa) tumors with myomelanocytic differentiation. Metastatic LAM causes cystic lung disease, but diagnosis may be hampered by low tumor burden, limited expression of melanocytic markers, and pathologic mimics that can also cause cystic lung metastases. Glycoprotein non-metastatic melanoma protein B (GPNMB) is an emerging marker for PEComas, but its diagnostic utility in distinguishing LAM from pathologic mimics remains incompletely understood. Biopsies of cystic lung metastases were retrieved including LAM (n = 17), gynecologic metastasizing leiomyoma or low-grade leiomyosarcoma (n = 10), endometrial stromal sarcoma (n = 6), cellular fibrous histiocytoma/dermatofibroma (n = 3), angiosarcoma (n = 3), and non-LAM PEComa (n = 1). GPNMB immunohistochemistry was performed on each case, quantified, and compared to melan-A, HMB45, MiTF, and cathepsin K. All 17 LAM cases and one non-LAM PEComa expressed GPNMB, with most LAM cases showing expression in >75% of tumor cells. GPNMB also highlighted alveolar macrophages and two metastatic dermatofibromas, representing potential diagnostic pitfalls. Cathepsin K also diffusely highlighted LAM and the non-LAM PEComa. In contrast, expression of other melanocytic markers in LAM varied and was often absent or minimal. Our data show that GPNMB is highly expressed by LAM in the lung, with immunohistochemistry performance similar to cathepsin K but superior to traditional melanocytic markers. GPNMB may be useful to confirm diagnostically challenging cases of LAM and to exclude most non-PEComatous mimics presenting with cystic lung metastases, but metastatic dermatofibromas can be a diagnostic pitfall.

Introduction: The incidence of recurrent lupus nephritis (RLN) after kidney transplantation (KTx) varies with higher rates of RLN reported in surveillance biopsy-based studies (vs. clinically indicated biopsies). Methods: We present a multisite retrospective study evaluating surveillance and clinically indicated biopsies in 209 first KTx recipients who had native lupus disease. Results: Of the 112 patients with satisfactory material for comprehensive histology, RLN was observed in 40 (35.7%). We describe the pathology of histologic RLN (HRLN; 40%) and clinical RLN (CRLN; 60%). African Americans had the highest recurrence rate (48.3%) of whom 50% had CRLN. HRLN was noted as early as 18 days, with early diagnosis (< 3 months of follow-up time) using surveillance biopsies. Mesangioproliferative pattern of glomerular injury (˷ class II lupus nephritis [LN] by International Society of Nephrology/Renal Pathology Society) was the most frequent pattern of RLN. IgG dominance or codominance was the most frequent Ig staining pattern. A full-house pattern of staining was only seen in 6% of HRLN and 37% of CRLN. C4d stain, as a stand-alone immunofluorescence (IF) test, was performed in the index renal allograft biopsy in 37.5% of patients, with RLN, prompting evaluation with a full IF panel. Electron microscopy (EM) confirmed the findings on IF. Graft loss because of lupus-associated pathology was observed in 50% of RLN subjects, of which thrombotic microangiopathy was seen in 25%. Conclusion: Our study demonstrates that RLN is frequent and may be clinically quiescent. Sequential biopsy evaluation provided an opportunity to study the natural evolution of the disease.

Introduction:Chronic changes in kidney histology are often approximated by using human vision but with limited accuracy. Methods:An interactive annotation tool trained an artificial intelligence (AI) model for segmenting structures on whole slide images (WSIs) of kidney tissue. A total of 20,509 annotations trained the AI model with 20 classes of structures, including separate detection of cortex from medulla. We compared the AI model detections with human-based annotations in an independent validation set. The AI model was then applied to 1426 donors and 1699 patients with renal tumor to calculate chronic changes as defined by measures of nephron size (glomerular volume, cortex volume per glomerulus, and mean tubular areas) and nephrosclerosis (globally sclerotic glomeruli, increased interstitium, increased tubular atrophy (TA), arteriolar hyalinosis (AH), and artery luminal stenosis from intimal thickening). We then assessed whether chronic kidney disease (CKD) outcomes were associated with these chronic changes. Results:During the AI model validation step, the agreement between the AI detections and human annotations was similar to the agreement between human pairs, except that the AI model showed less agreement with AH. Chronic changes calculated solely from AI-based detections associated with low glomerular filtration rate (GFR) during follow-up after kidney donation and with kidney failure after a radical nephrectomy for tumor. A chronicity score based on AI detections was calculated from cortex per glomerulus, percent glomerulosclerosis, TA foci density, and mean area of AH lesions and showed good prognostic discrimination for kidney failure (cross-validation C-statistic = 0.819). Conclusion:A multiclass AI model can help automate quantification of chronic changes on WSIs of kidney histology.

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<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 (<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.

Chronic changes in kidney histology are often approximated by using human vision but with limited accuracy. An interactive annotation tool trained an artificial intelligence (AI) model for segmenting structures on whole slide images (WSIs) of kidney tissue. A total of 20,509 annotations trained the AI model with 20 classes of structures, including separate detection of cortex from medulla. We compared the AI model detections with human-based annotations in an independent validation set. The AI model was then applied to 1426 donors and 1699 patients with renal tumor to calculate chronic changes as defined by measures of nephron size (glomerular volume, cortex volume per glomerulus, and mean tubular areas) and nephrosclerosis (globally sclerotic glomeruli, increased interstitium, increased tubular atrophy (TA), arteriolar hyalinosis (AH), and artery luminal stenosis from intimal thickening). We then assessed whether chronic kidney disease (CKD) outcomes were associated with these chronic changes. During the AI model validation step, the agreement between the AI detections and human annotations was similar to the agreement between human pairs, except that the AI model showed less agreement with AH. Chronic changes calculated solely from AI-based detections associated with low glomerular filtration rate (GFR) during follow-up after kidney donation and with kidney failure after a radical nephrectomy for tumor. A chronicity score based on AI detections was calculated from cortex per glomerulus, percent glomerulosclerosis, TA foci density, and mean area of AH lesions and showed good prognostic discrimination for kidney failure (cross-validation C-statistic = 0.819). A multiclass AI model can help automate quantification of chronic changes on WSIs of kidney histology.

AIMS:Polymyalgia 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 RESULTS:Institutional 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. CONCLUSIONS:Our 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.

BACKGROUND AND OBJECTIVE:The 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. METHODS:A 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. RESULTS:The 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. CONCLUSION:Combining 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.

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.

Lung cancer is the second most common cancer worldwide with over 2 million new cases in 2020 and is the number one cause of cancer death at 1.8 million according to the World Health Organization. Due to this frequency, lung specimens, either for diagnosis or at resection, are common in the pathology laboratory. This chapter is divided into four sections. In the first section, general considerations regarding the epidemiology, clinical presentation, and treatment of lung carcinomas will be introduced, followed by a discussion of specific subtypes of non-neuroendocrine, non-sarcomatoid lung carcinoma in the second section. The third and fourth sections will offer practical approaches to diagnosing lung carcinomas in small biopsies and surgical resections, respectively, discussing general considerations and addressing specific challenges commonly encountered with each of these two specimen types.

Context.— The pathologic diagnosis of pulmonary extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT) is challenging. Objective.— To evaluate the diagnostic usefulness and limitations of current diagnostic strategies for pulmonary MALT lymphoma. Design.— A retrospective review of 120 cases of pulmonary MALT lymphoma from 2014 through 2021 was performed. Results.— Clinicoradiologic presentations overlapped with previous observations in patients with MALT lymphoma, such as a wide age range, female predominance, frequent association with autoimmune disease or immunodeficiency, and broad imaging findings. The histopathologic diagnosis was based on a combination of morphology, immunohistochemistry, and demonstration of B-cell lineage clonality. Two-thirds (76 of 113) of MALT lymphomas had lymphoplasmacytoid cytomorphology. Occasionally, MALT lymphomas were associated with granulomas/giant cells (29%, 35 of 120) or immunoglobulin deposition disease (21%, 25 of 120), including light chain/heavy chain deposition disease, amyloidosis, and/or crystal storing histiocytosis. While CD5, CD10, Bcl-2, and Bcl-6 rarely revealed aberrancies, aberrant CD43 expression either on B-cells or on plasma cells was detected in 42% (27 of 64) of cases, including cases for which proof of clonality could not be obtained. κ/λ in situ hybridization was particularly useful for tumors with lymphoplasmacytoid morphology but performed poorly in lymphomas having no plasmacytic differentiation. κ/λ immunohistochemistry showed no additional usefulness when applied together with κ/λ in situ hybridization. Immunoglobulin gene rearrangement studies by polymerase chain reaction achieved high detection rates of clonality in all cytomorphologic subgroups. Conclusions.— Our study offers a practical evaluation of common diagnostic tests in pulmonary MALT lymphoma. We offer recommendations for a diagnostic workup that takes into consideration the usefulness and the specific limitations of the various diagnostic strategies.