Histopathological Classification Research Articles

Endometrial hyperplasia, benign endometrial tumors and endometrial carcinoma: A review study

Background. During the reproductive phase, the endometrium undergoes cyclical structural changes. Endometrial hyperplasia (EH) is a prevalent condition that arises from a combination of estrogen exposure and insufficient progesterone levels. Endometrial cancer (EC) is the second most common gynecologic malignancy, with the likelihood of EH progressing to EC ranging from 1% to 29%. The objective of this study is to conduct a comprehensive assessment of various forms of endometrial hyperplasia, benign endometrial tumors, and endometrial cancer (EC). Methods. A review of published papers from major databases, including PubMed, Scopus, and Google Scholar, was conducted. The search included original research articles and reviews on EH, benign endometrial tumors, and EC. Case reports, series, and clinical images were excluded. Results. Information on the selected topics was summarized, including epidemiology, etiology, histopathological classification, anatomy, biological and molecular characteristics, detection and prevention, clinical manifestations, staging, diagnostic workup, prognostic factors, and management. Conclusion. Endometrial hyperplasia and endometrial cancer are two of the most common problems in women. EH is a major precancerous condition for EC, and EC is associated with a poor prognosis if diagnosed late. The epidemiology and outcomes of both conditions vary according to several factors, including ethnicity, environment, socioeconomic status, heredity, drugs, hormones, and the aging process.

Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation

As an effective computer-aided diagnostic tool, deep learning has been successfully applied to the classification of breast histopathological images. However, the performance of the deep model is data-driven, and it is difficult to obtain satisfied results when the number of histopathological images is small and labelling histopathological images is difficult. Moreover, in traditional deep learning methods, the representation of features is monotonous, which leads to the limitation of the classification performance of the model. This study proposes an auto-encoder reconstructed semi-supervised domain adaptation for a breast histopathological image classification algorithm. First, the model was pre-trained and transferred to extract high-level features of the sample images. Then, the encoding and decoding parts of the auto-encoder were used to reconstruct the feature representation learning and the sample feature reconstruction learning, respectively. This ensured that the useful information for the classification was purified and retained. At the same time, the domain discriminator was used to confuse the source and target domain features to enhance the learning ability of the model. Finally, the distribution difference of features at different depths of the auto-encoder was measured to minimize the discrepancy of feature distribution between domains, so as to complete the classification of histopathological images. Compared to the results of the comparative and ablation algorithms from the BreakHis to SNL datasets, the proposed method achieved the best results in terms of F1 score (93.40%), accuracy (95.24%), sensitivity (94.66%), and specificity (95.56%). The experimental results demonstrate that the proposed method achieves a remarkable classification performance.

Difficulty of diagnostic accuracy of periprosthetic joint infection: a retrospective analysis of revision surgery of total hip arthroplasty and total knee arthroplasty in a tertiary hospital

BackgroundDiagnostic accuracy of periprosthetic joint infection still remains controversial and an unsolved problem with respect to clinical signs and laboratory measures. Influencing factors of diagnosis like age, sex, abnormal physical findings and comorbidities are published with different results. The aim of our study was to find factors strengthening the diagnosis.MethodsWe therefore retrospectively investigated all revision surgeries of total knee arthroplasty and total hip arthroplasty in the years 2019 and 2020 in a tertiary hospital with special regard to diagnostic accuracy of periprosthetic joint infection and switch in diagnosis to aseptic mechanical loosening or vice versa. All patients were divided into 4 non-hierarchical groups: suspected and discharge diagnosis periprosthetic joint infection (P1), suspected and discharge diagnosis mechanical loosening (P2), suspected diagnosis mechanical loosening and discharge diagnosis periprosthetic joint infection (P3), suspected diagnosis PJI and discharge diagnosis mechanical loosening (P4).ResultsIn the years 2019–2020, 106 patients underwent revision surgery of total knee arthoplasty and total hip arthroplasty. 33 patients showed periprosthetic joint infection (31.1%) according to Infectious Diseases Society (IDSA) criteria, 73 patients showed mechanical loosening (68.9%). Of the periprosthetic joint infection -patients, 15 were men (46%) and 18 were women (54%). The patients with mechanical loosening were 27 men (37%) and 46 women (63%). In group P1 (25 patients), 22 could be classified according to the histopathological classification Krenn and Morawietz grade 2 and grade 3, 2 patients to grade 4 and one patient could not be classified. In group P3 (8 patients) all could be classified according to the classification Krenn and Morawietz grade 2 and 3. Groups P1 – P4 were correlated with categorial basic data: All Groups P1 – P4 showed a statistical correlation towards American Society of Anesthesiologists (ASA) categorization 3–4 versus ASA 2 (p = 0.01). In the pairwise comparison in the exact Fisher´s exact test P1 and P2 showed a statistical correlation towards ASA categorization 3–4 versus ASA 2 (p = 0.01). Charlson Comorbidity Index (CCI) categories 5–7 versus 0,1–2 and 3–4 showed a statistical correlation towards groups P1-P4 (p = 0.007) and in the pairwise comparison in the exact Fisher´s exact test a discrimination of P1 and P2 (p = 0.001) and P1 and P3 (p = 0.007). The preoperative corticoid therapy showed a statistical correlation to groups P1-P4 (p = 0.05) and in the pairwise comparison in the exact Fisher test a discrimination of P1 and P4 (p = 0.02).ConclusionWe therefore conclude that diagnosis of periprosthetic joint infection still remains difficult. Switches in diagnosis during hospital stay from periprosthetic joint infection to aseptic mechanical loosening and vice versa are not unusual and the role of different diagnostic tools needs further investigation. Patients categorized according to ASA and CCI as severely ill might be more likely to be diagnosed correctly with periprosthetic joint infection.

Weakly supervised learning in thymoma histopathology classification: an interpretable approach

IntroductionThymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.MethodsWe applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. The model features an attention-based mechanism that generates heatmaps, enabling visual interpretation of decisions. These heatmaps align with clinically validated morphological differences between thymoma subtypes. Additionally, we embedded domain-specific pathological knowledge into the interpretability framework.ResultsThe model achieved a classification AUC of 0.9172. The generated heatmaps accurately reflected the morphological distinctions among thymoma subtypes, as confirmed by pathologists. The model's transparency allows pathologists to visually verify AI decisions, enhancing diagnostic reliability.DiscussionThis model offers a significant advancement in thymoma classification, combining high accuracy with interpretability. By integrating weakly supervised learning, MIL, and attention mechanisms, it provides an interpretable AI framework that is applicable in clinical settings. The model reduces the diagnostic burden on pathologists and has the potential to improve patient outcomes by making AI tools more transparent and clinically relevant.

TMJ synovial chondromatosis - an evaluation of 37 patients.

The authors evaluated a cohort of 37 patients with histologically verified synovial chondromatosis (SC) between 2013 and 2022. The cohort consisted of 37 patients (26 women, 11 men). 36 patients had unilateral involvement, while one patient had bilateral involvement. The average age of the patients was 54.77 years. The authors used the Milgram histopathological classification. They evaluated SC localisation, clinical symptoms, diagnostics and treatment (including recurrence incidence) in this cohort. In 31 patients (83.7%) SC affected only the upper joint space in one patient (2.7%) the lower space, and in five patients (13.6%) both spaces. 12 patients (32%) were Milgram Stage 1 (presence of synovial metaplasia without loose bodies), eight patients (22%) were Stage 2 (presence of synovial changes, loose bodies), and 17 patients (46%) were Stage 3 (presence of loose bodies, no synovial changes). Pain was the dominant clinical symptom (32 patients, 86.4%). Treatment consisted of arthroscopy and open surgery. Two patients underwent primary reconstruction and total TMJ replacement. Treatment was successful in 89.2% of cases (33 patients), with four (10.8%) patients suffering recurrence. As this patient cohort shows, pain was the dominant symptom in patients with SC. Magnetic resonance imaging is fundamental in the diagnosis of SC, demonstrating pathological findings even in patients for whom an initial X-ray was negative. These were mainly patients with Milgram Stages 1 and 2 without calcification, loose bodies or pathological changes of the bone structures. This is why the authors recommend MRI for any patient experiencing pain for more than three months, and if this reveals an effusion, joint distension or intraarticular soft tissue mass, they will always indicate arthroscopy. Thorough follow-up of patients is recommended, although SC recurrence is not very frequent. The authors recommend follow-up one, three and six months after surgery, and then annually for the first five years after surgery. They recommend follow-up MRI one, two and five years after surgery.

Classification of pediatric soft and bone sarcomas using DNA methylation-based profiling

Pediatric sarcomas present heterogeneous morphology, genetics and clinical behavior posing a challenge for an accurate diagnosis. DNA methylation is an epigenetic modification that coordinates chromatin structure and regulates gene expression, determining cell type and function. DNA methylation-based tumor profiling classifier for sarcomas (known as sarcoma classifier) from the German Cancer Research Center (Deutsches Krebsforschungszentrum) was applied to 122 pediatric sarcomas referred to a reference pediatric oncology hospital. The classifiers reported 88.5% of agreement between histopathological and molecular classification confirming the initial diagnosis of all osteosarcomas and Ewing sarcomas. The Ewing-like sarcomas were reclassified into sarcomas with BCOR or CIC alterations, later confirmed by orthogonal diagnostic techniques. Regarding the CNAs profile, osteosarcomas had several chromosomal gains and losses as well as chromothripsis, whereas Ewing sarcomas had few large events, such as amplifications of chromosomes 8 and 12. The molecular classification together with clinical and histopathological assessment could improve the diagnosis of pediatric sarcomas although there are limitations to deal with more rare classes. This study provides an increase in the number of sarcomas evaluated for DNA methylation profiling in the pediatric population.

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this method in optical liver biopsy has a high potential for studying the shift in energy-type production from oxidative phosphorylation to glycolysis and changes in the antioxidant defense of malignant cells. On the other hand, machine learning methods have proven to be an excellent solution to classification problems in medical practice, including biomedical optics. We aim to combine time-resolved fluorescence measurements and machine learning to automate the division of liver parenchyma and tumors (primary malignant, metastases and benign tumors) into classes. An optical biopsy was performed using a developed setup with a fine-needle optical probe in clinical conditions under ultrasound control. Fluorescence decays were recorded in a conditionally healthy liver and lesions during percutaneous needle biopsy. The labeled data set was created on the basis of the recorded fluorescence results and the histopathological classification of the biopsies obtained. Several machine learning methods were trained using different separation strategies of the training test set, and their respective accuracy was compared. Our results show that each of the tumor types had its own characteristic metabolic shifts recorded by the time-resolved fluorescence spectroscopy. The application of machine learning demonstrates a reliable separation of the liver and all tumor types into cancer and noncancer classes with sensitivity, specificity and corresponding accuracy greater than 0.91, 0.79 and 0.90, using the random forest method. We also show that our method is capable of giving a preliminary diagnosis of the type of liver tumor (primary malignant, metastases and benign tumors) with a sensitivity, specificity and accuracy of at least 0.80, 0.95 and 0.90. These promising results highlight its potential as a key tool in the future development of diagnostic and therapeutic strategies for liver cancers. Lasers Surg. Med. 00:00-00, 2024. 2024 Wiley Periodicals LLC.

Histopathologic classification and immunohistochemical features of papillary renal neoplasm with potential therapeutic targets.

Papillary renal cell carcinoma (pRCC) is the second most common histological subtype of renal cell carcinoma and is considered a morphologically and molecularly heterogeneous tumor. Accurate classification and assessment of the immunohistochemical features of possible therapeutic targets are needed for precise patient care. We aimed to evaluate immunohistochemical features and possible therapeutic targets of papillary renal neoplasms. We collected 140 papillary renal neoplasms from three different hospitals and conducted immunohistochemical studies on tissue microarray slides. We performed succinate dehydrogenase B, fumarate hydratase, and transcription factor E3 immunohistochemical studies for differential diagnosis and re-classified five cases (3.6%) of papillary renal neoplasms. In addition, we conducted c-MET, p16, c-Myc, Ki-67, p53, and stimulator of interferon genes (STING) immunohistochemical studies to evaluate their pathogenesis and value for therapeutic targets. We found that c-MET expression was more common in pRCC (classic) (p = .021) among papillary renal neoplasms and Ki-67 proliferation index was higher in pRCC (not otherwise specified, NOS) compared to that of pRCC (classic) and papillary neoplasm with reverse polarity (marginal significance, p = .080). Small subsets of cases with p16 block positivity (4.5%) (pRCC [NOS] only) and c-Myc expression (7.1%) (pRCC [classic] only) were found. Also, there were some cases showing STING expression and those cases were associated with increased Ki-67 proliferation index (marginal significance, p = .063). Our findings suggested that there are subsets of pRCC with c-MET, p16, c-MYC, and STING expression and those cases could be potential candidates for targeted therapy.

Diagnosis and Treatment of Renal ANCA Vasculitis: A Summary of the Consensus Document of the Catalan Group for the Study of Glomerular Diseases (GLOMCAT).

The document provides a comprehensive overview of the diagnosis, monitoring, and treatment of anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) with renal involvement, focusing on granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA). It outlines the definitions, clinical presentation, histopathological classification, monitoring strategies, induction and maintenance treatments, as well as special considerations for relapsing, refractory, and frail patients with renal AAV. The document was prepared by the Catalan Group for the Study of Glomerular Diseases (GLOMCAT), which comprises nephrologists with extensive experience in the diagnosis and treatment of AAV patients. Several virtual and face-to-face meetings were held for coordination, section assignments, and content discussion. An exhaustive and systematic search of the literature was carried out, which included, among others, the following databases: PubMed, EMBASE, Cochrane Library, Google Scholar, and ClinicalTrials.gov, as well as the abstract books of national and international congresses. Overall, the document provides a comprehensive guide for clinicians managing patients with renal AAV, offering evidence-based recommendations for diagnosis, monitoring, and treatment across various clinical scenarios.

A Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification.

Lung and colon cancers are among the most prevalent and lethal malignancies worldwide, underscoring the urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning and machine learning framework for the classification of Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, and Lung Squamous Cell Carcinoma from histopathological images. Current approaches primarily rely on the LC25000 dataset, which, due to image augmentation, lacks the generalizability required for real-time clinical applications. To address this, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image quality, and 1000 new images from the National Cancer Institute GDC Data Portal were introduced into the Colon Adenocarcinoma, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma classes, replacing augmented images to increase dataset diversity. A hybrid feature extraction model combining MobileNetV2 and EfficientNetB3 was optimized using the Grey Wolf Optimizer (GWO), resulting in the Lung and Colon histopathological classification technique (MEGWO-LCCHC). Cross-validation and hyperparameter tuning with Optuna were performed on various machine learning models, including XGBoost, LightGBM, and CatBoost. The MEGWO-LCCHC technique achieved high classification accuracy, with the lightweight DNN model reaching 94.8%, LightGBM at 93.9%, XGBoost at 93.5%, and CatBoost at 93.3% on the test set. The findings suggest that our approach enhances classification performance and offers improved generalizability for real-world clinical applications. The proposed MEGWO-LCCHC framework shows promise as a robust tool in cancer diagnostics, advancing the application of AI in oncology.

Reevaluating the variation of cesarean scar defect.

The increasing incidence of cesarean sections has led to a higher prevalence of cesarean scar defects (CSDs), predominantly characterized by postmenstrual bleeding in affected women. CSD manifests in various forms, including isolated myometrial defects, intrauterine cystic protrusions, and extrauterine cystic extensions. The pathophysiological mechanisms underpinning CSD and its associated symptoms remain insufficiently understood. This review explores the pathogenesis of CSD, highlights its morphological characteristics with an emphasis on variable clinical diversity, and addresses the challenges for future research. A comprehensive narrative review was conducted using electronic databases, including PubMed and Google Scholar, to identify 41 relevant literatures published up to December 31, 2023. The myometrium comprises two distinct layers-the inner and outer myometrium-with differences in their origin, structure, and function. Disruptions within these layers contribute to CSD development. Histopathologically, symptomatic CSD may be linked to uterine scar endometriosis, cystic adenomyosis, or endometrial cysts, suggesting that CSD, particularly those necessitating surgical intervention, are not limited to myometrial defects but may also involve iatrogenic endometriosis or adenomyosis, thereby exacerbating clinical symptoms. This review provides an updated understanding of the histopathological features and classification of CSD, with an emphasis on elucidating its underlying pathogenesis.

Efficient feature selection for histopathological image classification with improved multi-objective WOA

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

Aggressiveness evaluation of borderline serous ovarian tumors by analysis of Psammoma bodies present in cancer tissues using micro-FTIR spectroscopy

Borderline ovarian tumor is a type of tumor with generally low malignant potential. However, these tumors pose diagnostic challenges with benign and malignant epithelial ovarian tumors because the clinical symptoms are similar and investigation procedures for specific diagnosis are still debated. In addition, a small number of borderlines transform into high-grade serous ovarian carcinoma with a poor prognosis. Therefore, tools improving a better characterization of high-risk subtypes of borderline tumors to enable understanding of possible unfavorable evolution are essential for patients’ management. Psammoma bodies (PBs) are microcalcifications found both in serous epithelial ovarian cancer and serous borderline tumors with possible correlation with disease progression.In this work, the chemical composition of PBs found in the tissues of borderline, high-grade and low-grade ovarian tumors was evaluated using micro-FTIR spectroscopy. Applying principal component analysis to spectral data, it was observed that among the borderline tumors analyzed (1-bl,2-bland3-bl), the PBs of3-blshowed a different chemical content from that of the PBs of the high-grade and low-grade tumors, while the PBs of1-bland2-blappeared to have similar chemical content to the PBs of high- and low-grade tumors. The discriminating wavenumbers were found to be those related to carbonate CO32− (1454, 1413 cm−1and 872 cm−1) and phosphate PO43−(1018 cm−1and 960 cm−1) present in microcalcifications. A higher ratio between peak intensities at 1413 cm−1and 1018 cm−1(I1413/I1018) was observed in the PBs of3-blcompared with those of high- and low-grade ovarian carcinoma patients, which correlates with higher CO32− content. On the other hand, the PBs of1-bland2-blshowed a CO32−level close to that of the PBs of high- and low-grade patients. Several studies on microcalcifications in breast carcinoma have reported that increased carbonate content is related to decreased tumor aggressiveness. Therefore, case bl-3 appeared to be the less aggressive. The results obtained from FTIR analysis were in agreement with histopathological tumor classification and molecular analysis for BRAFV600E. The FTIR technique could become a reliable tool for identifying borderline low- and high-risk tumors.

GLUT1 and cerebral glucose hypometabolism in human focal cortical dysplasia is associated with hypermethylation of key glucose regulatory genes.

Focal cortical dysplasia (FCD) is recognized as a significant etiological factor in pharmacoresistant intractable epilepsy, linked with disturbances in neurovascular metabolism. Our study investigated regulation of glucose-transporter1 (GLUT1) and cerebral hypometabolism within FCD subtypes. Surgically excised human brain specimens underwent histopathological categorization. A subset of samples (paired with matching blood) was assessed for DNA methylation changes of glucose metabolism-related genes. We evaluated GLUT1, VEGFα, MCT2, and mTOR expression by western blot analysis, measured glucose-lactate concentrations, and established correlations with patients' demographic and clinical profiles. Furthermore, we investigated the impact of DNA methylation inhibitor decitabine and hypometabolic condition on the uptake of [3H]-2-deoxyglucose and ATPase in epileptic brain endothelial cells (EPI-EC). We observed hypermethylation of GLUT1 and glucose metabolic genes in FCD brain/blood samples and could distinguish FCDIIa/b from mMCD, MOGHE and non-lesional types in brain. Low GLUT1 and glucose-lactate ratios corresponded to elevated VEGFα and MCT2 in FCDIIa/b vs non-lesional tissues, independent of age, gender, seizure-onset, or duration of epilepsy. Increased mTOR signaling in FCDIIa/b tissues was evident. Decitabine stimulation increased GLUT1, decreased VEGFα expression, restored glucose uptake and ATPase activity in EPI-ECs and reduced mTOR and MCT2 levels in HEK cells. We demonstrated: 1) hypermethylation of glucose regulatory genes distinguish FCDIIa/b from mMCD, MOGHE and non-lesional types, 2) glucose uptake reduction is due to GLUT1 suppression mediated possibly by a GLUT1-mTOR mechanism; and 3) DNA methylation regulates cellular glucose update and metabolism. Together, these studies may lead to GLUT1-mediated biomarkers, glucose metabolism and identify early intervention strategies in FCD.

Comprehensive Analysis of Receptor Status, Histopathological Classifications (B1-B5), and Cumulative Histological Dimensions in Breast Cancer: Predictors of Malignancy and Diagnostic Implications.

Breast cancer has become one of the most serious and widespread public health concerns globally, affecting an increasing number of women-and, in rare cases, men-across the world. It is the most common cancer among women across all countries. In this study, we aimed to evaluate the influence of demographic factors, medical and reproductive history, diagnostic techniques, and hormone receptor status on the development and progression of breast cancer. A total of 687 female patients from Romania underwent standard breast examination techniques, including clinical breast examination, mammography, ultrasonography, and, ultimately, breast biopsy. Statistical analysis was performed using the R programming language and RStudio software. The study included a comparative analysis and a prediction analysis for malignancy and tumor size (cumulative histological dimension) through logistic and linear regression models. The comparative analysis identified several variables associated with malignancy: older age (p < 0.001), non-vulnerability (p = 0.04), no daily physical activity (p = 0.002), no re-biopsy (p < 0.001), immunohistochemistry use (p < 0.001), use of larger gauge needles (p < 0.001), ultrasound-guided biopsy (p < 0.001), and vacuum biopsy (p < 0.001). The hormone receptor statuses-estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR)-showed statistically significant differences in distribution across breast cancer B classifications. Logistic regression analysis identified ER, PR, and age as significant predictors of malignancy. Linear regression analysis revealed histopathological results, living environment, geographical region, vulnerability, prior breast examination, and the number of histological fragments as significant predictors of cumulative histological dimension. Our predictive models demonstrate the impact of demographic factors, medical history, diagnostic techniques, and hormone receptor status on breast cancer development and progression, accounting for a significant portion of the variance in malignancy and cumulative histological dimension.

Histopathological image classification of colorectal cancer using a novel supervised contrastive learning approach

Histopathological image classification of colorectal cancer using a novel supervised contrastive learning approach

A comparative study of postadrenalectomy hyperuricemia and renal impairment in patients with unilateral primary aldosteronism: does histopathology subtype matter?

BackgroundPrimary aldosteronism (PA), which is present in 5–18% of hypertensive patients, is a leading cause of secondary hypertension. Adrenalectomy is often recommended for patients with unilateral primary aldosteronism (uPA), yielding good long-term outcomes. PA patients without hyperuricemia and chronic renal failure before adrenalectomy were enrolled in this cohort study. Serum uric acid (SUA) and renal filtration were measured one year post-adrenalectomy. Their relationships with pathologic features, histopathological subtype (classical or nonclassical (HISTALDO consensus)), and vessel stiffness were explored. The aim of this cohort study is to evaluate the correlation between post-adrenalectomy serum uric acid (SUA) levels and estimated glomerular filtration rate (eGFR) with the pathologic features delineated by the HISTALDO consensus. Additionally, the study seeks to assess the impact of these biochemical markers on peripheral vessel stiffness and brachial-ankle pulse wave velocity (baPWV) at a one-year follow-up visit.MethodsThis prospective cohort study included patients (N = 100) diagnosed with uPA who underwent adrenalectomy from Jan 1, 2007 to Dec 31, 2022.ResultsAt follow-up, elevated SUA, hyperuricemia, and a > 25% eGFR decrease were significantly more common in the classical than the nonclassical group. The incidence of postoperative hyperuricemia, herein referred to as post-adrenalectomy hyperuricemia (PAHU), was 29% (29/100) overall, 34.8% (23/66) in the classical group and 17.6% (6/34) in the nonclassical group. The incidence of eGFR reduction > 25% was 33% (33/100), 43.9% (29/66), and 11.8% (4/34), respectively. baPWV decreased more in the classical group than the nonclassical group.ConclusionFor PA patients with PAHU and/or renal impairment, we suggest monitoring SUA, pH, urine uric acid, and urine crystals and performing a KUB study and peripheral vascular and renal sonography (on which pure uric acid stones in the KUB are radiolucent) to determine whether drug intervention is required for cases of asymptomatic PAHU, especially patients in male gender, classical histopathology, or renal impairment.

Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

Distinct copy number signatures between residual benign and transformed areas of carcinoma ex pleomorphic adenoma

The mechanisms involved with the pathogenesis of carcinoma ex pleomorphic adenoma (CXPA) seem to be associated with the accumulation of molecular alterations in the pleomorphic adenoma (PA). In this sense, using array-based comparative genomic hybridization (aCGH) a rare series of 27 cases of CXPA and 14 residual PA (rPA) adjacent to the transformation area, we investigated the profile of the copy number alterations (CNAs) comparing benign residual and transformed areas. The main findings were correlated with the histopathological classification by histologic subtype and degree of invasion. The distribution of losses (p = 0.187) and amplifications (p = 0.172) was not statistically different between rPA and CXPA. The number of gains was increased in the transformed areas compared to the benign residual areas (p = 0.005). PLAG1 gain was maintained along the malignant transformation, as it was observed in both residual PA and CXPA samples, likely being an earlier event during transformation. The amplification of GRB7 and ERBB2 may also be an initial step in the malignant transformation of PA to CXPA (salivary duct carcinoma subtype). Furthermore, the amplification of HMGA2 and RPSAP52 were the most prevalent alterations among the studied samples. It was noteworthy that amplified genes in the transformed areas of the tumors were enriched for biological processes related to immune signaling. In conclusion, our results underscored for the first-time crucial CNAs in CXPA, some of them shared with the residual benign area adjacent to the transformation site. These CNAs included PLAG1 gain, as well as amplification of GRB7, ERBB2, HMGA2, and RPSAP52.

A Review of Surgical Outcomes of Management of Sinonasal Malignancies: A 8-Year of Clinical Experience (2013-2021) at the Tertiary Centre, Sarawak.

Sinonasal carcinoma, a rare and challenging malignancy, originating in the nasal cavity and paranasal sinuses, poses diagnostic and management complexities. This 8-year retrospective analysis at Sarawak General Hospital aims to elucidate demographic trends, histopathological entities, and management outcomes, shedding light on this multifaceted malignancy. Emphasizing the significance of accurate histopathological classification, the study explores the impact on prognostication and treatment strategies. Spanning 2013 to 2021, the study involved 54 patients with sinonasal malignancies. Demographic, clinical, and histopathological details were examined, adhered to the AJCC staging criteria. Analysis involved demographic distributions, tumour characteristics, treatment modalities, and instances of treatment failure. Statistical analysis was done using SPSS version 29.02. The cohort, predominantly male (57.4%) and of Iban ethnicity (44.4%), with a mean age of 52.8 years, exhibited diverse histopathologies, with squamous cell carcinoma as the most common (38.9%). Epistaxis and nasal blockage were common clinical presentations. Advanced stages (III and IV) were prevalent, with the nasal cavity as the primary site. Surgical interventions, mainly endoscopic endonasal excision, were complemented by adjuvant therapies. Complications occurred in 24% of cases. The study highlights a male predilection, occupational risk factors, and a significant association between tobacco smoking and sinonasal cancers. Surgical interventions predominantly utilized the endoscopic approach. Despite a mean survival of 46.6 months, treatment failure occurred in 29.6% of cases, with recurrence and metastasis. Histopathological analysis revealed comparable 5-year disease survival rates between squamous and non-squamous histologies. Treatment failure was significantly associated with the mode of surgery, with open surgery showing a lower incidence. However, nodal status, histopathology types, T staging, and overall staging did display positive associations with treatment failure. This 8-year review provides comprehensive insights into sinonasal carcinoma, addressing demographic, clinical, and histopathological dimensions. The study underlines the complexity of managing this challenging malignancy, emphasizing the need for a holistic approach to patient care. The findings contribute to the understanding of sinonasal carcinoma, guiding clinical decision-making and fostering further research.