Diagnostic Pathology最新文献

{"title":"A novel multimodal framework integrating pathomics, deep learning, and machine learning for breast cancer histological grades classification.","authors":"Han Ding, Zheng Dong","doi":"10.1186/s13000-026-01769-9","DOIUrl":"10.1186/s13000-026-01769-9","url":null,"abstract":"<p><p>OBJECTIVE: The present study is aimed at developing and testing a strong, interpretable, and multi-scale computational pathology framework for the automated histologic grade (grade-based) grading of invasive ductal carcinoma (IDC) from hematoxylin and eosin (H&E) whole-slide images (WSIs). MATERIALS AND METHODS: A multi-center dataset consisting of 3,660 WSIs from 925 patients with IDC from five institutions was assembled and quality controls were very stringent. Images were captured at four magnifications (4X, 10X, 20X, 40X), in order to capture both a view of global tissue architecture and fine detail of the nuclei. Multi-modality features were extracted: low and high magnification deep features (DINOv2 ViT-S/16, ConvNeXt V2 Small), 71 handcrafted radiomics descriptors extracted from H&E channels and three pseudo-nuclei indices. Correlation filtering and four feature selection approaches (mutual information, recursive feature elimination, ANOVA, LASSO) were used on a modality per modality basis. An Attention MIL framework with a novel cross scale attention module and cross scale consistency loss was used for feature fusion and grading. Three strategies of fusion (early, late, hybrid) were compared. Model performance was evaluated using 5-folder cross-validation, internal, and external validation on an independent cohort (n = 922 WSIs). RESULTS: The hybrid fusion of LASSO selected features resulted in the best performance as the accuracy was found to be 92.5% (internal) and 90.9% (external) and the AUC were found to be 93.5% and 92.1% respectively. High magnitude deep features proved to be the most informative single modality and multi-modality fusion increased both the accuracy and robustness. The proposed framework was better than end-to-end baselines (p < 0.001), and the recall was balanced across all grades. Attention heatmaps identified regions of diagnostic interest which were consistent with pathologist annotations. CONCLUSIONS: The multi-scaled, multi modal Attention-MIL framework provides accurate, interpretable and generalizable grading of IDC, outperforming conventional and end-to-end methods. Its inter-institutional strength and biological interpretability make it an appealing candidate for a clinical decision-support tool. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147431403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse expression of ubiquitin-specific peptidase 19 and caspase 7 correlates with gastric neoplastic transformation.","authors":"Philip Kelemen, Michael Naumann, Michael Vieth","doi":"10.1186/s13000-026-01777-9","DOIUrl":"10.1186/s13000-026-01777-9","url":null,"abstract":"<p><p>BACKGROUND: Ubiquitin-specific protease 19 (USP19) is a deubiquitinylase (DUB) that is part of the USP family, the largest group of DUBs in humans. Growing evidence has indicated that USP19 is involved in tumor progression and serves as a new prognostic marker for various malignant disorders. Interestingly, USP19 has been shown to have both promoting and inhibiting effects on the onset and development of different neoplasms, depending on the specific tissue type. DUBs including USP19 affect a variety of cell functions including apoptotic cell death. Herein, caspase 7 acts as a key executioner in apoptosis, and its expression levels serve as a prognostic and diagnostic marker in various cancers. This study analysed the expression of USP19 and caspase 7 in gastric pathology along the progression of stomach cells to gastric adenocarcinoma (Correa cascade). METHODS: We analysed the expression and subcellular localization of USP19 and caspase 7 by immunohistochemistry (IHC) in 296 paraffin-embedded human gastric tissue samples. The cohort included various gastric conditions such as autoimmune gastritis (A-gastritis), Helicobacter pylori gastritis (HP-gastritis), chemical gastropathy (C-gastritis), adenoma, and adenocarcinoma, using gastric mucosa without pathological changes as a reference point. RESULTS: We observed a significant upregulation of USP19 expression in HP-gastritis, adenoma and adenocarcinoma. In contrast, caspase 7 was significantly upregulated in A-gastritis and HP-gastritis and significantly downregulated in both adenoma and adenocarcinoma. CONCLUSIONS: USP19 and caspase 7 showed distinct expression patterns across different gastric pathologies. Both USP19 and caspase 7 overexpression maybe associated with inflammation, while USP19 overexpression and no caspase 7 expression could indicate neoplastic transformation. This inverse expression may help distinguish early and late neoplastic epithelial changes in chronic gastritis. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13064426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147431383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffuse co-expression of TTF-1 and p40 (ΔNp63) defines a distinct biphenotypic non-small cell lung carcinoma: clinicopathologic and molecular characterization of 19 cases.","authors":"Jielu Lin, Shaoling Li, Weijie Chen, Wei Wu, Wei Zhang, Yan Huang, Zhengwei Dong, Chunyan Wu, Likun Hou","doi":"10.1186/s13000-026-01775-x","DOIUrl":"10.1186/s13000-026-01775-x","url":null,"abstract":"<p><p>BACKGROUND: The diffuse co-expression of thyroid transcription factor-1 (TTF-1) and p40 defines a rare and diagnostically challenging subtype of non-small cell lung carcinoma (NSCLC). Due to its extreme rarity, the clinicopathologic and molecular characteristics of this entity remain poorly characterized. METHODS: We conducted a comprehensive analysis of the largest single-institution cohort to date (n = 19) of NSCLCs with diffuse TTF-1/p40 co-expression. All cases underwent detailed histopathologic review. Immunohistochemistry (IHC) utilized specific antibody clones (TTF-1: 8G7G3/1; p40). Molecular profiling was performed via next-generation sequencing in a subset of cases, including multi-region analysis for select tumors. RESULTS: The cohort mainly consisted of elderly male smokers (median age 65; 84.2% male; 73.7% smokers). Anatomically, 84.2% of tumors were located in the right lung, mainly in the upper lobe, with no clear preference for central or peripheral distribution. Histologically, all were poorly differentiated NSCLCs, classifiable into two patterns: a basaloid/squamous-like pattern and an inflammatory/plasmacytoid pattern. Molecular profiling revealed a hybrid genomic landscape, with individual tumors harboring alterations linked to both adenocarcinoma and squamous cell carcinoma, along with a high prevalence(58.8%) of TP53 mutations. In an index case, multi-region sequencing revealed a shared EGFR mutation across regions, with a TP53 mutation confined to the high-grade TTF-1/p40-positive area. Most patients (66.7%) presented with Stage III or IV disease, and 61.1% developed metastases, including four with distant spread and seven involving thoracic or supraclavicular lymph nodes or the pleura. Three patients died from the disease within 10–64 months after diagnosis. However, all patients with Stage I or II disease who underwent resection remained disease-free. CONCLUSIONS: NSCLC with diffuse TTF-1/p40 co-expression constitutes a distinct biphenotypic entity with unique clinicopathologic and molecular features. We suggest specific diagnostic criteria to ensure proper identification. Given the presence of targetable drivers and frequent PD-L1 expression, an integrated diagnostic approach is crucial for guiding personalized therapy and enhancing patient outcomes. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subclassification of atypical endometrial cells (Not Otherwise Specified vs favor neoplasia) on Pap tests: age-dependent risk of endometrial neoplasia.","authors":"Xin Zhou, Yi Zhou, Suming Huang, Yanqiu Wang, Qiuqin Zou, Xuxi Yang, Wanrun Lin, Feng Zhou","doi":"10.1186/s13000-026-01774-y","DOIUrl":"10.1186/s13000-026-01774-y","url":null,"abstract":"<p><p>BACKGROUND: Atypical endometrial cells (AEMc) detected in cervical cytology are uncommon yet significantly associated with an elevated risk of precancerous and malignant lesions. Nevertheless, limited data are available on the histopathological correlation of AEMc cases. This study aims to evaluate the immediate histological outcomes in women diagnosed with AEMc and to assess how HPV status and patient age influence oncogenic potential. METHOD: A retrospective analysis was conducted on 163 women diagnosed with AEMc at the International Peace Maternity and Child Health Hospital between January 2019 and December 2023. Each patient underwent liquid-based cytology (LBC) and high-risk HPV (hrHPV) testing for 14 genotypes, followed by histological evaluation within six months after the cytological assessment. RESULT: Among the 163 AEMc patients, 86.5% (141/163) were classified as AEMc-not otherwise specified (NOS) and 13.5% (22/163) as AEMc-favor neoplasia (FN). Histological analysis indicated that 2.5% (4/163) had precancerous lesions and 12.9% (21/163) had cancer. Immediate pathological severity was significantly higher in AEMc-FN than in AEMc-NOS (p = 0.015), with endometrial carcinoma more prevalent in AEMc-FN (27.3% vs. 9.9%, p = 0.037; odds ratio 3.30, 95% CI: 1.11–9.79). The diagnostic accuracy of AEMc-FN for endometrial carcinoma was 81.6% (95% CI: 74.8%–87.3%). hrHPV status did not reliably predict cancer risk stratification in AEMc, however, age was significantly associated with disease severity. Compared with younger patients, those aged > 65 years showed a markedly higher prevalence of high-grade endometrial glandular abnormalities (AEH +) and adenocarcinoma (AC) (p = 0.001 and p = 0.000401, respectively). With an age cutoff of 45 years, older AEMc patients had an increased prevalence of AEH + and AC, a trend also evident in the AEMc-NOS subgroup. CONCLUSION: Women aged ≥ 45 years, especially those classified as AEMc-NOS, exhibit a substantially higher risk of high-grade endometrial glandular lesions, underscoring the need for more vigilant follow-up. Given the significantly higher prevalence of endometrial carcinoma in AEMc-FN patients compared to AEMc-NOS patients, it may be advisable for cytopathologists to annotate the presence or absence of “favor neoplasia” when diagnosing AEMc. Refining the diagnostic criteria for AEMc-FN may facilitate earlier detection of endometrial cancer. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147343957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRDM1: a useful indicator of differentiation and prognosis in esophageal squamous cell carcinoma.","authors":"Anqi Huang, Xingran Jiang, Yanan Qi, Jiaqi Chen, Xinmeng Guo, Jun Lu, Mulan Jin","doi":"10.1186/s13000-026-01773-z","DOIUrl":"10.1186/s13000-026-01773-z","url":null,"abstract":"<p><p>BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the gastrointestinal tract with a high mortality rate. Although positive regulatory domain zinc finger protein 1 (PRDM1) has long been thought to play a key role especially in the differentiation of B cells, its role in ESCC has never been studied. This study aimed to examine the association between PRDM1 and ESCC clinical and pathological characteristics and prognosis. METHODS: Using immunohistochemical and reverse transcription quantitative polymerase chain reaction, we detected the expression level of PRDM1 in consecutive ESCC surgical resections from 163 patients. To interpret PRDM1 immunohistochemical positivity, we employed three methods: PRDM1 10 high-power field (HPF) positive cell count (PCC), PRDM1 1HPF PCC and PRDM1 positive hotspots (PPHs). Based on PPH assessment and histological morphology, we developed a novel histological grading scheme. To evaluate the relationship between PRDM1 differential expression and clinical–pathological parameters in ESCC, we used the chi-square test. To evaluate the relationship between PRDM1 expression and ESCC prognosis, we performed Kaplan–Meier survival analysis and Cox regression analysis. RESULTS: Immunohistochemical staining revealed that PRDM1 was expressed in tumor epithelium, stroma, and adjacent squamous epithelium in ESCC. And the expression level of PRDM1 in tumor epithelium significantly correlated with tumor differentiation (P < 0.05) and was closely related to the patient prognosis (P < 0.05). Moreover, survival analysis results indicated that our novel histological grading seem to be better than the traditional histological grading criteria in predicting the prognosis of ESCC patients. CONCLUSIONS: PRDM1 holds promise as a novel indicator for ESCC, with potential application value in the histological grading, diagnosis, and prognostic assessment of this disease. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.","authors":"Mohamed J Saadh, Tamara Nazar Saeed, Karar H Alfarttoosi, Suhas Ballal, Priya Priyadarshini Nayak, Abhayveer Singh, V Kavitha, Aziz Kubaev, Waam Mohammed Taher, Mariem Alwan, Mahmood Jasem Jawad, Ali M Ali Al-Nuaimi, Bagher Farhood","doi":"10.1186/s13000-026-01754-2","DOIUrl":"10.1186/s13000-026-01754-2","url":null,"abstract":"<p><p>OBJECTIVE: This study aims to create a machine-learning framework that integrates histopathological imaging, immune marker data, and clinical features to classify tumor types, determine histological grades, and predict survival outcomes in CRC patients. MATERIALS AND METHODS: This study analyzed 650 CRC cases, divided into training (520 cases) and testing (130 cases) subsets. Preprocessing involved quality control, color normalization, and selecting tumor regions (tumor center and invasive margins) with uniform dimensions (224 × 224 pixels). Six immune markers (CD3, CD8, CD45RO, PD-1, LAG-3, Tim-3) were selected for their relevance to CRC progression and immune response. A fine-tuned EfficientNet model was used to extract high-dimensional features from the images. These imaging features were combined with clinical data. To refine the dataset, feature selection methods such as PCA, RFE, and LASSO were applied to include only the most relevant variables. Machine learning models (XGBoost, CatBoost, Random Forest) and ensemble models were developed for tumor type and grade prediction. For survival prediction, regression models such as SVR, Random Forest Regressor, and stacking regressors were used. Model performance was evaluated using metrics such as accuracy, AUC, MSE, and the C-index. RESULTS: The evaluation highlighted the value of feature selection and ensemble learning in CRC classification and survival prediction. For tumor type classification, XGBoost with RFE achieved a testing AUC of 86.15% and accuracy of 85.33%, while stacking-based models with RFE performed better, with a testing AUC of 96.73% and accuracy of 96.92%. Histological grade classification followed a similar trend, with stacking-based models achieving a testing AUC of 96.37% and accuracy of 96.92%. In survival prediction, the Stacking Regressor with RFE produced the best results, with testing MSEs of 132.87 for Disease-Free Survival (days) and 115.78 for Survival (days), along with the highest C-index of 0.84 for both tasks. CONCLUSIONS: The proposed framework demonstrates the potential of integrating histopathological imaging, immune markers, and machine learning to improve CRC prognosis and enable personalized treatment, setting a benchmark for future computational pathology research. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13032617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147276000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathomic model to predict the expression of UQCRH and overall survival of lung adenocarcinoma patients.","authors":"Yong Chen, Jie Liu, Guoping Li, Huifang Huang","doi":"10.1186/s13000-026-01770-2","DOIUrl":"10.1186/s13000-026-01770-2","url":null,"abstract":"<p><p>BACKGROUND: Ubiquinol-cytochrome c reductase hinge protein (UQCRH) is a component of mitochondrial respiratory chain complex CIII. Its relationship with human cancer has been less studied. Pathomics uses artificial intelligence algorithms to collect histopathological image features and perform joint analysis by combining gene and transcriptome data. In this study, a pathomics prediction model was established based on UQCRH expression and histopathological images of lung adenocarcinoma (LUAD). Prognostic value and other analyses were conducted based on this model. METHODS: The expression level of UQCRH in 33 types of human cancers was measured. Its relationship with the survival of the primary LUAD samples with complete pathological images, gene expression data, and clinical information were divided into high and low expression groups based on the expression level threshold of the UQCRH gene (Table S1). LUAD patients was studied. Pathomic prediction model was established by using machine learning algorithms according to the UQCRH expression level and the characteristics of LUAD histopathological images. Based on this prediction model, survival analysis, molecular pathways, immune infiltration, immunological subtypes, ICI treatment prediction, and drug sensitivity analyses were performed. RESULTS: UQCRH is highly expressed in various cancers, including LUAD. In addition, we verified that UQCRH is overexpressed in human LUAD tissues. High expression of UQCRH is worse prognostic factor for LUAD patients. A pathomic prediction model was constructed based on the UQCRH expression level and histopathology image features. The pathomic score showed good correlation with the UQCRH expression level. Patients in the high-risk group of the pathomic prediction model had worse prognosis and higher tumor proliferation ability, but may have better response to immune checkpoint inhibitors (ICIs) therapy. CONCLUSION: We have established a pathomic prediction model for LUAD based on gene expression values and according to histopathological image features, which can predict patient survival prognosis and has potential guiding value for ICIs therapy. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":"21"},"PeriodicalIF":3.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12918428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.","authors":"Wen-Yu Chuang, Wei-Hsiang Yu, Yu-Jen Liu, Chao-Cheng Huang, Kam-Fai Lee, Cheng-Cheng Hwang, Liang-Che Chang, Shir-Hwa Ueng, Chi-Ju Yeh, Huei-Chieh Chuang, Jui Lan, Hui-Shan Huang, Shang-Hung Chang, Tong-Hong Wang, Tsung-Chieh Lin, Chun-Te Wu, Jau-Song Yu, Chuen Hsueh, Chang-Fu Kuo, Chao-Yuan Yeh","doi":"10.1186/s13000-026-01747-1","DOIUrl":"10.1186/s13000-026-01747-1","url":null,"abstract":"<p><p>BACKGROUND: Artificial intelligence (AI) has achieved good performance in image recognition, including identifying cancer cells in pathology images. However, the best mode of AI assistance in diagnostic pathology remains to be explored. METHODS: We compared the influence of two deep-learning assistance modes on pathologists in cancer identification. Ten board-certified pathologists classified 60 cases of nasopharyngeal biopsy as carcinoma or benign with or without AI assistance, which was either a heatmap of cancer probability accompanying a whole-slide image (AI-heatmap mode) or ten high-power field images with the highest cancer probability (AI-HPF mode). RESULTS: Both assisting modes significantly accelerated the diagnostic process, lowered the subjective difficulty, and maintained high accuracy compared to the unassisted mode. Notably, the acceleration of diagnosis was more significant in AI-HPF mode than in AI-heatmap mode (time reduction: 35.1% vs. 28.1%; P = 0.040), especially for benign cases (time reduction: 49.4% vs. 32.9%; P = 0.0000072). For benign cases, an increased area proportion of false-positive AI prediction slowed down the diagnostic process in AI-heatmap mode (P = 0.00000084) but not in AI-HPF mode (P = 0.62). CONCLUSIONS: We show for the first time that an AI-HPF assistance mode was superior to the commonly used AI-heatmap mode in accelerating cancer identification by pathologists. In our scenario, the AI-HPF mode maintained high diagnostic accuracy and was robust to the influence of false-positive AI prediction. The potential risk caused by AI assistance is also discussed. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13005349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Clinicopathological profile of paragangliomas: A 5-Year retrospective analysis from a single tertiary Centre\".","authors":"Geetha V, Megha Murali, Bhavna Nayal, Deepak Nayak, Vidya Monappa, Shreya Garg, P S Priya, Girish Menon, Raghavendra Nayak, Kailesh Pujary, R Balakrishnan, Suresh Pillai","doi":"10.1186/s13000-026-01771-1","DOIUrl":"10.1186/s13000-026-01771-1","url":null,"abstract":"<p><p>INTRODUCTION: Paragangliomas are rare neuroendocrine tumours arising from neural crest–derived paraganglionic cells and form part of the pheochromocytoma–paraganglioma (PPGL) spectrum. The World Health Organization (WHO) 5th edition classification emphasizes autonomic origin, distinguishing sympathetic from parasympathetic paragangliomas, and recognizes that all paragangliomas possess metastatic potential. OBJECTIVES: To evaluate the clinicopathological features of paragangliomas diagnosed over a 5-year period at a single tertiary care centre. METHODS: Twenty histologically confirmed cases of paraganglioma resected between 2018 and 2023 were retrospectively reviewed. Hematoxylin and eosin (H&E) and immunohistochemistry (IHC) slides were re-evaluated, and relevant clinical and radiological data were extracted from hospital records. RESULTS: The cohort showed a female predominance (male-to-female ratio 1:1.8) with a median age of 49 years. The most common tumour site was the jugulotympanic region (30%), followed by intradural extramedullary (IDEM) locations (15%) and the retroperitoneum (15%). Two patients (10%) had metastatic disease involving the spine. Radiological findings were concordant with histology in 50% of cases. IHC was performed in 75% of cases and aided differentiation from histologic mimics. The Ki-67 proliferation index ranged from 1 to 2% in most cases, with one exception (4%). Follow-up data were available for 13 patients; one case showed local recurrence, and no syndromic associations were identified. CONCLUSION: Histological features alone are insufficient to predict tumour behaviour. Metastasis remains the only definitive criterion for malignancy, particularly when identified at sites lacking normal chromaffin tissue. Comprehensive clinicopathological assessment, incorporating radiology and immunohistochemistry, is essential for accurate diagnosis and management, and integration of molecular markers may further refine risk stratification in future studies. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13005369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinicopathological and prognostic insights into Embryonal Tumors with Multilayered Rosettes (ETMRs).","authors":"Noura A A Ebrahim, Heba A Abdelbaky, Ahmed Mustafa Abd Elsalam, Mustafa A Hussein, Nancy H Amin","doi":"10.1186/s13000-026-01766-y","DOIUrl":"10.1186/s13000-026-01766-y","url":null,"abstract":"<p><p>BACKGROUND: Embryonal tumors with multilayered rosettes (ETMRs) are rare, highly aggressive pediatric brain neoplasms characterized by early onset and dismal prognosis. This study presents a comprehensive clinicopathological and molecular analysis of ETMR cases diagnosed over a 14-year period at National Cancer Institute, Cairo University, with a focus on diagnostic features, clinical presentation, and survival outcomes. METHODS: A retrospective review of 35 patients with histopathologically confirmed ETMRs was conducted. Demographic data, clinical symptoms, neuroimaging findings, histopathologic features-including rosette formation, mitotic activity, necrosis, and Ki67 proliferation index-as well as molecular analyses for C19MC amplification and LIN28A expression were evaluated. Kaplan-Meier survival curves and univariable Cox regression were used to assess prognostic associations. RESULTS: The cohort comprised 17 females and 18 males, with a median age of 36 months. Common presenting symptoms included signs of raised intracranial pressure, seizures, and motor deficits. Gross total resection was achieved in 43% of patients, and 48% received adjuvant chemoradiotherapy.Histopathologic examination consistently revealed ependymoblastic rosettes (true multilayered rosettes) and high mitotic activity. LIN28A was diffusely expressed in all assessable cases. Molecular confirmation by PCR testing was done in 20 cases. The median overall survival was 19 months. Factors associated with inferior survival included incomplete surgical resection, absence of adjuvant therapy, and presence of necrosis and high mitotic index. CONCLUSION: ETMRs demonstrate consistent histological and immunohistochemical features that can guide diagnosis in resource-limited settings. Despite therapeutic advances, prognosis remains poor, underscoring the urgent need for novel therapeutic strategies. Molecular testing for C19MC amplification and LIN28A expression supports diagnostic confirmation and may hold future prognostic or therapeutic relevance. </p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}