Histopathology最新文献

Aims: Oesophageal adenocarcinoma (EAC) is a glandular or mucinous epithelial malignancy that can show immunohistochemical evidence of neuroendocrine differentiation (NED) and express the hormone serotonin. The objective of this study was to correlate the presence of NED and serotonin with clinicopathological characteristics and patient outcome after neoadjuvant chemoradiation.

Methods and results: A retrospective cohort of patients treated between 2002 and 2021 was established and included 218 oesophagectomy specimens with residual tumour. Representative full-face sections of tumour were stained for synaptophysin, chromogranin-A and serotonin by immunohistochemistry, and staining results were correlated with disease-free survival (DFS) and overall survival (OS). In total, 129 (59%) tumours showed evidence of NED, defined as immunohistochemical expression of synaptophysin or chromogranin-A, while 40 (18%) showed evidence of NED and expressed serotonin. Patients with neuroendocrine-positive tumours had significantly shorter median OS compared to those with neuroendocrine-negative tumours (22.5 versus 48.8 months, P = 0.006), but similar median DFS (13.3 versus 17.8 months, P = 0.34). Using Cox regression, the association between NED and OS was significant in univariate [hazard ratio (HR) = 1.68, 95% confidence interval (CI) = 1.16-2.45] and multivariate (HR = 1.65, 95% CI = 1.08-2.52) analysis. Patients with serotonin-expressing tumours had similar median OS (21.7 versus 25.9 months, P = 0.24) and DFS (7.3 versus 15.6 months, P = 0.12) compared to those with NED but lacking serotonin. Using Cox regression, serotonin expression was associated with reduced OS in univariate (HR = 1.62, 95% CI = 1.06-2.47) but not multivariate (HR = 1.03, 95% CI = 0.64-1.65) analysis.

Conclusions: Our findings support NED as independent predictor of OS in EAC after neoadjuvant chemoradiation. While a subset of tumours with NED expressed serotonin, this did not provide additional prognostic information.

Fat embolism (FE) is a historically recognised but still actively researched topic in forensic pathology. Several aspects remain not fully elucidated, such as its aetiopathogenesis, its causal role in death determination, the impact of interfering factors (e.g. cardiopulmonary resuscitation or other medical procedures) and both qualitative and quantitative diagnostic methodologies in clinical and forensic contexts. These issues are further underscored by the potential involvement of FE in the causal determination of non-traumatic deaths, which often raises questions of professional liability. The present study aims to provide a comprehensive and up-to-date overview of the most recent scientific evidence relevant to forensic pathology. Our systematic research has included 58 articles from 1990 to the present on the topic of FE and fat embolism syndrome (FES). From these articles, we identified 45 case reports, from which the authors' descriptions were extracted to provide information on individual cases and the operational methods of forensic pathologists. Additionally, 21 experimental studies were identified, and their key findings have been summarised narratively. It has emerged that both traumatic and non-traumatic cases are frequently reported in the forensic context, with orthopaedic and cosmetic surgery being among the highest-risk specialities. Experimental studies have re-evaluated the role of a patent foramen ovale in the pathogenesis of FE, as well as the impact of cardiopulmonary resuscitation in causing FE severe enough to result in death. Additionally, there are new findings regarding diagnostic techniques, including radiological and immunohistological methods; however, they have not yet fully bridged the reliability gap compared to an accurate autopsy-histological evaluation. The major critical points that emerged include the lack of complete and detailed information on premortem clinical conditions, the underutilisation of grading systems and the methodological heterogeneity applied, resulting in considerable variability regarding the organs studied histologically and the diagnostic techniques used. Despite the limitations associated with the analysis of case reports and the heterogeneity of included experimental studies, we believe that this study can provide a comprehensive overview of the FE topic. It furnishes pathologists with an updated overview useful for clinical practice and guiding future research trends, as well as facilitating the development of standardised procedures.

Aims: Hepatic inflammatory pseudotumours (IPTs) are nonneoplastic hepatic masses characterized by variably fibroblastic stroma and inflammatory infiltrate, hypothesized to arise as part of a response to infection or prior surgery. The aim of this study was to evaluate the clinicopathologic features and outcomes of biopsy-proven hepatic IPT as well as other cases with IPT-like histologic features.

Methods and results: A database search at our institution identified cases with a pathologic diagnosis of hepatic IPT (n = 80) between 2000 and 2023. Histologic features (stromal quality, inflammatory cell components, granulomas, and necrosis) were evaluated. Past medical and surgical history, microbiologic studies, and outcomes were reviewed retrospectively. Patients frequently had a past medical history of malignancy (34%), biliary disease (15%), or prior intraabdominal surgery (24%), and often presented with multifocal hepatic lesions (36%). Variable inflammatory backgrounds were present, including histiocytic (36%), lymphoplasmacytic (34%), or neutrophilic (24%). Specific organisms were identified in 15% of cases, most commonly Klebsiella and Staphylococcus species. Most patients with available clinical follow-up demonstrated radiologic resolution and/or had repeat negative biopsy; a minority of patients (8%) were subsequently diagnosed with neoplastic hepatic lesions. No significant association was seen between histologic features and the subsequent clinical or pathologic diagnosis of hepatic neoplastic lesions.

Conclusions: Hepatic IPT is a heterogeneous entity that can present in a variety of clinical scenarios and show a wide morphologic spectrum. These lesions often regress spontaneously or with antibiotics. A subset of cases with hepatic IPT-like histologic features were subsequently diagnosed with malignancy, emphasizing the need for continued follow-up and repeat biopsy depending on clinical and radiologic features.

Aims: This study evaluates the utility of breast specific markers in identifying breast cancer subtypes within metastatic settings. The subtype alteration in metastatic disease and its consequent impact on breast-specific marker expression is also examined.

Materials and methods: GATA-binding protein 3 (GATA3), mammaglobin (MMG), transcriptional repressor GATA binding 1 (TRSP1) and SRY-box transcription factor 10 (SOX10) expression were assessed in a large cohort of metastatic breast cancer (MBC) cases and correlated with the characteristics of both MBC and primary breast cancer (PBC).

Results: GATA3 was the most sensitive in MBC (83.1%), followed by TRPS1 (77.0%), MMG (58.5%) and SOX10 (7.1%). This trend was consistent in hormonal receptor (HR)+ and HR- MBC. Combining GATA3/TRPS1 yielded the highest detection rates in the overall cohort (90.1%) and HR+ MBC (97.1%), while TRSP1/MMG was most effective in HR- (76.2%) and TN (71.1%) MBC. Marker expression did not correlate with metastatic site, except SOX10 in lung metastases (P = 0.031). Subtype discordance between MBC and PBC occurred in 43 cases (24.4%), with GATA3 expression in HR- MBC significantly linked to subtype discordance (P = 0.005). Conversely, SOX10 expression was significantly associated with subtype concordance in HR- and TNBC (P ≤ 0.003). Despite a higher expression of GATA3 in all HR- cases, TRSP1 outperformed GATA3 in detecting concordant HR- cases (64.0% versus 38.5%). TRPS1 and SOX10 were expressed in more than 50% of concordant TNBC cases.

Conclusions: The expression of breast-specific markers is mainly determined by the PBC subtype. GATA3 retains high sensitivity in HR+ cancers, even after HR loss during metastasis. TRPS1 and SOX10 are identified as valuable markers in TNBC metastasis.

Aims: To test the efficacy of artificial intelligence (AI)-assisted Ki-67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance.

Methods and results: This study used 494 cases of Ki-67 slide images of IBC core needle biopsies. The methods were divided into two steps: (i) construction of a deep-learning model (DL); and (ii) DL implementation for Ki-67 analysis. First, a DL tissue classifier model (DL-TC) and a DL nuclear detection model (DL-ND) were constructed using HALO AI DenseNet V2 algorithm with 31,924 annotations in 300 Ki-67 digital slide images. Whether the class predicted by DL-TC in the test set was correct compared with the annotation of ground truth at the pixel level was evaluated. Second, DL-TC- and DL-ND-assisted digital image analysis (DL-DIA) was performed in the other 194 luminal-type cases and correlations with manual counting and clinical outcome were investigated to confirm the accuracy and prognostic potential of DL-DIA. The performance of DL-TC was excellent and invasive carcinoma nests were well segmented from other elements (average precision: 0.851; recall: 0.878; F1-score: 0.858). Ki-67 index data and the number of nuclei from DL-DIA were positively correlated with data from manual counting (ρ = 0.961, and 0.928, respectively). High Ki-67 index (cutoff 20%) cases showed significantly worse recurrence-free survival and breast cancer-specific survival (P = 0.024, and 0.032, respectively).

Conclusion: The performances of DL-TC and DL-ND were excellent. DL-DIA demonstrated a high degree of concordance with manual counting of Ki-67 and the results of this approach have prognostic potential.

Follicular lymphoma (FL) is the second most common type of lymphoma (20% of all non-Hodgkin lymphomas), derived from germinal centre (GC) B cells, and is characterised by its significant clinical, prognostic and biological heterogeneity, leading to complexity in management. Despite significant biological investigation and indisputable clinical progress since the advent of the immunotherapy era more than 20 years ago, much remains to be done to understand and cure this lymphoma. Today, FL is metaphorically a giant puzzle on the table with patches of sky, landscape and foliage clearly appearing. However, many of the remaining pieces are held by various stakeholders (e.g. clinicians, pathologists, researchers, drug developers) without global agreement on what the gaps are, or any clear blueprint on how to solve the puzzle of understanding the heterogeneity of this disease and create curative and tailored therapies. With the advent of new investigation and drug technologies, together with recent advances in our capacity to manage big data, the time seems ripe for a change of scale. More than ever, this will require collaboration between and within all stakeholders to overcome the current bottlenecks in the field. As for every investigator, we acknowledge that this first draft is necessarily biased, incomplete and some FL expert readers might recognise some remaining gaps not addressed. We hope they will reply to make this effort a collaborative one to assemble all the pieces in the most ideal fashion. As such, this review intends to be a first step and an interactive platform to a collaborative roadmap towards better understanding and care of FL.

Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.

Aims: Contralateral axillary lymph node metastasis (CAM) is a rare clinical condition in patients with breast cancer (BC). CAM can be either a locoregional event or a distant metastasis. Molecular application for clonal evolution in BC has not been reported in CAM cases.

Methods: We studied six patients with CAM with clinical, pathological and/or molecular evidence of distant metastasis; those patients had poor outcomes.

Results: Two cases with molecular analysis of paired primary and CAM established clonal evolution of the CAM with its corresponding primary with additional molecular alteration, increased tumour mutation burden, and copy number variations (CNVs) in the CAMs. Four cases containing alterations from genes potentially modulate chromatin organization, supporting chromatin and subsequent transcriptional signature changes are essential in CAM. Molecular analysis is critical to establish the connection between CAM and its primary counterpart. Distant CAM shows clonal evolution compared with its corresponding primary with additional molecular alterations, increased mutation burden and/or copy number variations.

Conclusion: CAM should be evaluated individually and handled in a personalized fashion. Evidence of a true metastatic CAM can be supported by distant metastasis to other organs, specific morphological features and/or clonal evolution.