Feasibility of MALDI-MSI-Based Proteomics Using Bouin-Fixed Pathology Samples: Untapping the Goldmine of Nephropathology Archives.

The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.

Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL.

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases and further complicated by the inclusion of noninvasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTP. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye and (2) develop a deep learning model for multiclass segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable internuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available next-generation sequencing data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole-slide images of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all whole-slide image tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

Computer-assisted urine cytology: Faster, cheaper, better?

Recent advancements in computer-assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes.

Systemic vasculitis involving the kidney: the nephropathologist's point of view.

Kidneys are often targets of systemic vasculitis (SVs), being affected in many different forms and representing a possible sentinel of an underlying multi-organ condition. Renal biopsy still remains the gold standard for the identification, characterization and classification of these diseases, solving complex differential diagnosis thanks to the combined application of light microscopy (LM), immunofluorescence (IF) and electron microscopy (EM). Due to the progressively increasing complexity of renal vasculitis classification systems (e.g. pauci-immune vs immune complex related forms), a clinico-pathological approach is mandatory and adequate technical and interpretative expertise in nephropathology is required to ensure the best standard of care for our patients. In this complex background, the present review aims at summarising the current knowledge and challenges in the world of renal vasculitis, unveiling the potential role of the introduction of digital pathology in this setting, from the creation of hub-spoke networks to the future application of artificial intelligence (AI) tools to aid in the diagnostic and scoring/classification process.

Digital transition in pathology lab: a survey from the Lombardy region.

Objective: Digital pathology is an opportunity to revise the routine and old artisanal workflow, moving to standard operating procedures, quality control and reproducibility. Here the results of a survey promoted by the Coordinamento della Medicina di Laboratorio (CRC Med Lab) of the Lombardy region in Italy are reported to shed light on the current situation of digital adoption in the country. Methods: The survey composed of 58 questions was sent to 60 pathology laboratories. The results were collected and most significant answers were reported and discussed. Results: Answers were received from 57 (95%) laboratories, a minority organized in spoke-hub networks (16%) with a centralized processing phase (11%). Hybrid manual/computer-assisted traceability was prevalent (36%), with QR/barcode labeling starting within the pathology lab (23%). Different laboratory information systems (LIS) were employed, mostly with alert functions and/or multimedial file attachments (56% and 46%, respectively). The majority opted for a semi-automated tracking management (44, 77%) and 18 centers (32%) were partly digitizing the routine (¾ scanning < 25% of slides). Whole slide images were retained for 3.7 years in average; in-house blocks/slides archiving was still preferred (30, 53%), with 1838 (±1551) and 1798 (±1950) days (5 years) internal permanence for blocks and slides that are stored in out-source (mean turnaround time for return on-demand 3.7±2.1, range 1-10 days). Conclusions: The advantages of digital pathology must be balanced against the challenges faced in the structural revision of the pathology workflow. This regional scouting can represent the foundation to build an efficient and connected digital pathology system in the territory.

Congo Red Staining in Digital Pathology: The Streamlined Pipeline for Amyloid Detection Through Congo Red Fluorescence Digital Analysis.

Renal amyloidosis is a rare condition caused by the progressive accumulation of misfolded proteins within glomeruli, vessels, and interstitium, causing functional decline and requiring prompt treatment due to its significant morbidity and mortality. Congo red (CR) stain on renal biopsy samples is the gold standard for diagnosis, but the need for polarized light is limiting the digitization of this nephropathology field. This study explores the feasibility and reliability of CR fluorescence on virtual slides (CRFvs) in evaluating the diagnostic accuracy and proposing an automated digital pipeline for its assessment. Whole-slide images from 154 renal biopsies with CR were scanned through a Texas red fluorescence filter (NanoZoomer S60, Hamamatsu) at the digital Nephropathology Center of the Istituto di Ricovero e Cura a Carattere Scientifico San Gerardo, Monza, Italy, and evaluated double-blinded for the detection and quantification through the amyloid score and a custom ImageJ pipeline was built to automatically detect amyloid-containing regions. Interobserver agreement for CRFvs was optimal (k = 0.90; 95% CI, 0.81-0.98), with even better concordance when consensus-based CRFvs evaluation was compared to the standard CR birefringence (BR) (k = 0.98; 95% CI, 0.93-1). Excellent performance was achieved in the assessment of amyloid score overall by CRFvs (weighted k = 0.70; 95% CI, 0.08-1), especially within the interstitium (weighted k = 0.60; 95% CI, 0.35-0.84), overcoming the misinterpretation of interstitial and capsular collagen BR. The application of an automated digital pathology pipeline (Streamlined Pipeline for Amyloid detection through CR fluorescence Digital Analysis, SPADA) further increased the performance of pathologists, leading to a complete concordance with the standard BR. This study represents an initial step in the validation of CRFvs, demonstrating its general reliability in a digital nephropathology center. The computational method used in this study has the potential to facilitate the integration of spatial omics and artificial intelligence tools for the diagnosis of amyloidosis, streamlining its detection process.

Spatial transcriptome of a germinal center plasmablastic burst hints at MYD88/CD79B mutants-enriched diffuse large B-cell lymphomas.

The GC reaction results in the selection of B cells acquiring effector Ig secreting ability by progressing toward plasmablastic differentiation. This transition is associated with exclusion from the GC microenvironment. The aberrant expansion of plasmablastic elements within the GC fringes configures an atypical condition, the biological characteristics of which have not been defined yet. We investigated the in situ immunophenotypical and transcriptional characteristics of a nonclonal germinotropic expansion of plasmablastic elements (GEx) occurring in the tonsil of a young patient. Compared to neighboring GC and perifollicular regions, the GEx showed a distinctive signature featuring key regulators of plasmacytic differentiation, cytokine signaling, and cell metabolism. The GEx signature was tested in the setting of diffuse large B-cell lymphoma (DLBCL) as a prototypical model of lymphomagenesis encompassing transformation at different stages of GC and post-GC functional differentiation. The signature outlined DLBCL clusters with different immune microenvironment composition and enrichment in genetic subtypes. This report represents the first insight into the transcriptional features of a germinotropic plasmablastic burst, shedding light into the molecular hallmarks of B cells undergoing plasmablastic differentiation and aberrant expansion within the noncanonical setting of the GC microenvironment.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology.

INTRODUCTION: Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions. AREAS COVERED: This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions. EXPERT COMMENTARY: Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

The therapeutic potential of an allosteric non-competitive CXCR1/2 antagonist for diabetic nephropathy.

AIMS: Diabetic nephropathy is a major consequence of inflammation developing in type 1 diabetes, with interleukin-8 (IL-8)-CXCR1/2 axis playing a key role in kidney disease progression. In this study, we investigated the therapeutic potential of a CXCR1/2 non-competitive allosteric antagonist (Ladarixin) in preventing high glucose-mediated injury in human podocytes and epithelial cells differentiated from renal stem/progenitor cells (RSC) cultured as nephrospheres. MATERIALS AND METHODS: We used human RSCs cultured as nephrospheres through a sphere-forming functional assay to investigate hyperglycemia-mediated effects on IL-8 signalling in human podocytes and tubular epithelial cells. RESULTS: High glucose impairs RSC self-renewal, induces an increase in IL-8 transcript expression and protein secretion and induces DNA damage in RSC-differentiated podocytes, while exerting no effect on RSC-differentiated epithelial cells. Accordingly, the supernatant from epithelial cells or podocytes cultured in high glucose was able to differentially activate leucocyte-mediated secretion of pro-inflammatory cytokines, suggesting that the crosstalk between immune and non-immune cells may be involved in disease progression in vivo. CONCLUSIONS: Treatment with Ladarixin during RSC differentiation prevented high glucose-mediated effects on podocytes and modulated either podocyte or epithelial cell-dependent leucocyte secretion of pro-inflammatory cytokines, suggesting CXCR1/2 antagonists as possible pharmacological approaches for the treatment of diabetic nephropathy.

Goodpasture syndrome and anti-glomerular basement membrane disease.

Anti-glomerular basement membrane (anti-GBM) disease is a rare life-threatening small vessel vasculitis that typically affects the capillaries of kidneys and lungs, with most of patients developing rapidly progressive crescentic glomerulonephritis, and 40%-60% concomitant alveolar haemorrhage. It is caused by the deposition in alveolar and glomerular basement membrane of circulating autoantibodies directed against antigens intrinsic to the basement membrane. The exact mechanism that induces the formation of autoantibodies is unknown, but probably environmental factors, infections or direct damage to kidneys and lungs may trigger the autoimmune response in genetically susceptible individuals. Initial therapy includes corticosteroids and cyclophosphamide to prevent autoantibodies production, and plasmapheresis to remove the circulating autoantibodies. Good renal outcomes may be achieved by a prompt treatment initiation. However, when patients present with severe renal failure requiring dialysis or with a high proportion of glomerular crescents at biopsy, renal outcomes are bad. Relapses are rare and when renal involvement is present, the suspect of concomitant diseases, such as ANCA-associated vasculitis and membranous nephropathy, should be raised. Imlifidase is showing promising results, which if confirmed will cause a paradigm shift in the treatment of this disease.

Spatially Resolved Molecular Approaches for the Characterisation of Non-Invasive Follicular Tumours with Papillary-like Features (NIFTPs).

Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are low-risk thyroid lesions most often characterised by RAS-type mutations. The histological diagnosis may be challenging, and even immunohistochemistry and molecular approaches have not yet provided conclusive solutions. This study characterises a set of NIFTPs by Matrix-Assisted Laser Desorption/Ionisation (MALDI)-Mass Spectrometry Imaging (MSI) to highlight the proteomic signatures capable of overcoming histological challenges. Archived formalin-fixed paraffin-embedded samples from 10 NIFTPs (n = 6 RAS-mutated and n = 4 RAS-wild type) were trypsin-digested and analysed by MALDI-MSI, comparing their profiles to normal tissue and synchronous benign nodules. This allowed the definition of a four-peptide signature able to distinguish RAS-mutant from wild-type cases, the latter showing proteomic similarities to hyperplastic nodules. Moreover, among the differentially expressed signals, Peptidylprolyl Isomerase A (PPIA, 1505.8 m/z), which has already demonstrated a role in the development of cancer, was found overexpressed in NIFTP RAS-mutated nodules compared to wild-type lesions. These results underlined that high-throughput proteomic approaches may add a further level of biological comprehension for NIFTPs. In the future, thanks to the powerful single-cell detail achieved by new instruments, the complementary NGS-MALDI imaging sequence might be the correct methodological approach to confirm that the current NIFTP definition encompasses heterogeneous lesions that must be further characterised.

The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board.

Objective: The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered. Methods: On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results: Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion: Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.

Natural Language Processing to extract SNOMED-CT codes from pathological reports.

Objective: The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods: Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results: The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions: AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.

Dissecting the histological features of lupus nephritis highlights new common patterns of injury in class III/IV.

OBJECTIVE: The International Society of Nephrology/Renal Pathology Society classification is the gold standard for the characterisation of lupus nephritis (LN) on renal biopsy, with therapeutic repercussions. Its recent revision simplified the current class subdivisions, eliminating the S/G forms of class IV, although data on a possible pathogenetic/clinical value of this subdivision are still contradictory. METHODS: 353 renal biopsies from Belimumab International Study in LN were assessed through central pathology review. Univariate logistic models and a decision tree were performed on 314 adequate biopsies to evaluate the impact of histological features on focal/diffuse classes. Removing class I/II (n=6) and 'pure' class V (n=34), principal component analysis (PCA) and heatmap were used to explore similarities among III, IVS and IVG biopsies either incorporating or not the mixed classes (+V, n=274). Finally, a method aimed at partitioning the cases into k clusters based on their similarity (KMeans), was used to study features from the cohort of 'pure' class III/IVS/IVG cases (n=214) to determine alternative subdivisions based on phenotypic data. RESULTS: Segmental endocapillary hypercellularity (EH) was prevalent in class III, global EH, wire loops, hyaline thrombi and double contours were hallmarks of class IVG, with IVS cases showing intermediate characteristics. Heatmap and PCA confirmed the segregation of these features among classes, showing better segregation for focal/diffuse LN as compared with the mixed classes (+V). KMeans revealed the presence of two main clusters, membranoproliferative-like (n=83) or vasculitis-like (n=131). CONCLUSIONS: This study reveals new phenotypic forms of LN surpassing the traditional classes as determined by the current classification. Future validation and confirmation are required to confirm these findings.

The "digital biopsy" in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT.

PURPOSE: The present pilot study investigates the putative role of radiomics from [18F]FDG PET/CT scans to predict PD-L1 expression status in non-small cell lung cancer (NSCLC) patients. METHODS: In a retrospective cohort of 265 patients with biopsy-proven NSCLC, 86 with available PD-L1 immunohistochemical (IHC) assessment and [18F]FDG PET/CT scans have been selected to find putative metabolic markers that predict PD-L1 status (< 1%, 1-49%, and ≥ 50% as per tumor proportion score, clone 22C3). Metabolic parameters have been extracted from three different PET/CT scanners (Discovery 600, Discovery IQ, and Discovery MI) and radiomics features were computed with IBSI compliant algorithms on the original image and on images filtered with LLL and HHH coif1 wavelet, obtaining 527 features per tumor. Univariate and multivariate analysis have been performed to compare PD-L1 expression status and selected radiomic features. RESULTS: Of the 86 analyzed cases, 46 (53%) were negative for PD-L1 IHC, 13 (15%) showed low PD-L1 expression (1-49%), and 27 (31%) were strong expressors (≥ 50%). Maximum standardized uptake value (SUVmax) demonstrated a significant ability to discriminate strong expressor cases at univariate analysis (p = 0.032), but failed to discriminate PD-L1 positive patients (PD-L1 ≥ 1%). Three radiomics features appeared the ablest to discriminate strong expressors: (1) a feature representing the average high frequency lesion content in a spherical VOI (p = 0.009); (2) a feature assessing the correlation between adjacent voxels on the high frequency lesion content (p = 0.004); (3) a feature that emphasizes the presence of small zones with similar grey levels inside the lesion (p = 0.003). The tri-variate linear discriminant model combining the three features achieved a sensitivity of 81% and a specificity of 82% in the test. The ability of radiomics to predict PD-L1 positive patients was instead scarce. CONCLUSIONS: Our data indicate a possible role of the [18F]FDG PET radiomics in predicting strong PD-L1 expression; these preliminary data need to be confirmed on larger or single-scanner series.

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies.

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2-100.0%) sensitivity and 96.0% (95% CI = 86.3-99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3-89.5%) and a sensitivity of 43.1% (95% CI = 30.9-56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8-89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up.

Proteomics for the study of new biomarkers in Fabry disease: State of the art.

Nephropathy represents a major complication of Fabry Disease and its accurate characterization is of paramount importance in predicting the disease progression and assessing the therapeutic responses. The diagnostic process still relies on performing renal biopsy, nevertheless many efforts have been made to discover early reliable biomarkers allowing us to avoid invasive procedures. In this field, proteomics offers a sensitive and fast method leading to an accurate detection of specific pathological proteins and the discovery of diagnostic and prognostic biomarkers that reflect disease progression and facilitate the evaluation of therapeutic responses. Here, we report a review of selected literature focusing on the investigation of several proteomic techniques highlighting their advantages, limitations and future perspectives in their application in the routine study of Fabry Nephropathy.

Ex vivo thyroid fine needle aspirations as an alternative for MALDI-MSI proteomic investigation: intra-patient comparison.

Fine needle aspiration (FNA) is the reference standard for the diagnosis of thyroid nodules. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has been successfully used to discriminate the proteomic profiles of benign and malignant thyroid FNAs within the scope of providing support to pathologists for the classification of morphologically borderline cases. However, real FNAs provide a limited amount of material due to sample collection restrictions. Ex vivo FNAs could represent a valuable alternative, increasing sample size and the power of statistical conclusions. In this study, we compared the real and ex vivo MALDI-MSI proteomic profiles, extracted from thyrocyte containing regions of interest, of 13 patients in order to verify their similarity. Statistical analysis demonstrated the mass spectra similarity of the proteomic profiles by performing intra-patient comparison, using statistical similarity systems. In conclusion, these results show that post-surgical FNAs represent a possible alternative source of material for MALDI-MSI proteomic investigations in instances where pre-surgical samples are unavailable or the number of cells is scarce.

The spectrum of the cytopathological features of primary effusion lymphoma and human herpes virus 8-related lymphoproliferative disorders.

INTRODUCTION: Human herpes virus 8-related lymphoproliferative disorders are a complex and heterogeneous group of entities and some of them are eminently diagnosed by cytopathology. In a routine laboratory, these lesions account for less than 1% of the effusion fluids samples. However, they represent up to 30% of all the lymphoma diagnosis from effusion cytological samples and their consideration in the diagnostic flow chart is mandatory, especially in human immunodeficiency virus-positive patients. METHODS: A retrospective series of cytological specimens from cavity effusions (n = 605) were analysed. Five human herpes virus 8-related lymphoproliferative processes were recruited. A combination of morphological criteria (enhanced with May-Grünwald Giemsa staining), cell block-based immunocytochemistry and flow cytometry were undertaken for final characterisation. RESULTS: The identification of malignant cells may be difficult. Some specimens are particularly rich, easily leading to suspect a lymphoproliferative process, whereas in other cases, the presence of abundant reactive mesothelial cells, histiocytes, neutrophils, small reactive T and B lymphocytes may obscure the neoplastic process. The biological behaviour may be very heterogeneous and a standardised therapy for these cases is still lacking, although some patients may benefit from antiretroviral therapy in a human immunodeficiency virus setting. CONCLUSIONS: The present case series highlights some characteristic findings of these entities to reaffirm useful cytopathological diagnostic criteria, stressing the crucial role of the appropriate technical processing of effusion fluids to obtain the best performances.