Pathological Classification Research Articles

A Deep-Learning Model for Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy.

To develop and validate a deep-learning classifier trained on voice data extracted from videolaryngostroboscopy recordings, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), and VF lesions, including benign and malignant pathologies. Patients with UVFP (n = 105), VF lesions (n = 63), and HVF (n = 41) were retrospectively identified. Voice samples were extracted from stroboscopic videos (Pentax Laryngeal Strobe Model 9400), including sustained /i/ phonation, pitch glide, and /i/ sniff task. Extracted audio files were converted into Mel-spectrograms. Voice samples were independently divided into training (80%), validation (10%), and test (10%) by patient. Pretrained ResNet18 models were trained to classify (1) HVF and pathological VF (lesions and UVFP), and (2) HVF, UVFP, and VF lesions. Both classifiers were further validated on an external dataset consisting of 12 UVFP, 13 VF lesions, and 15 HVF patients. Model performances were evaluated by accuracy and F1-score. When evaluated on a hold-out test set, the binary classifier demonstrated stronger performance compared to the multi-class classifier (accuracy 83% vs. 40%; F1-score 0.90 vs. 0.36). When evaluated on an external dataset, the binary classifier achieved an accuracy of 63% and F1-score of 0.48, compared to 35% and 0.25 for the multi-class classifier. Deep-learning classifiers differentiating HVF, UVFP, and VF lesions were developed using voice data from stroboscopic videos. Although healthy and pathological voice were differentiated with moderate accuracy, multi-class classification lowered model performance. The model performed poorly on an external dataset. Voice captured in stroboscopic videos may have limited diagnostic value, though further studies are needed. 4 Laryngoscope, 2025.

Clinical outcomes following treatment for small saphenous vein insufficiency: An AVLS PRO venous registry study.

Compared to the great saphenous vein, there is a relative paucity of data in the literature focused on treating the small saphenous vein (SSV). Our goal was to evaluate and analyze national registry data related to the diagnosis and treatment of the SSV and identify practice patterns and clinical outcomes. Subjects undergoing SSV interventions between April 2014 and March 2023 were identified in the AVLS PRO Venous Registry. Data points included Clinical, Etiological, Anatomical, Pathological (CEAP) classifications; Venous Clinical Severity Score (VCSS), Venous Quality of Life (VVSymQ®) instrument scores, Heaviness, Aching, Swelling, Throbbing, Itching (HASTI) symptoms, and Short Form Health Survey (SF-36.) Duplex ultrasound findings included vein diameter, length, and the overall recanalization rate. A total of 92,175 SSV interventions were identified. Over two-thirds of subjects presented with a CEAP classification of C3 (31%) and C4 (C4 40%). Clinical results demonstrated significant improvement immediately after treatment in the first month and maintained beneficial results at 1year or longer (p < 0.001.) This improvement was reflected in the significant decrease in VCSS scores (6 ± 3.4 to 5 ± 2.9; p < 0.0001), VVSymQ (9.5 ± 6.0 to 5.9 ± 4.4; p < 0.0001), and HASTI (9.4 ± 5.9 to 6.6 ± 5.2; p < 0.0001). The recanalization rate at any follow-up was 3%; 48% of those occurring within the first year. Predictors associated with recanalization were larger vein diameter (mean 4.0mm, 95% CI 3.9-4.1, p < 0.0001) and shorter SSV length (mean 15.27mm, 95 % CI 14.4-16.1, p < 0.0001). Treatment of SSV insufficiency improved clinical outcomes by both patient and physician-derived outcome measures. Only 3% of SSV demonstrated recanalization, and nearly half occurred within the first year following the procedure. Predictors of recanalization included larger pre-procedure vein diameters and treatment of a shorter length of SSV.

Case report: Primary intraosseous meningioma: a radiological study of two cases confirmed pathologically

IntroductionPrimary intraosseous meningioma (PIM) is a rare lesion often misidentified preoperatively due to its ambiguous benign or malignant characteristics. In this report, we introduce two novel cases of PIM and explore the potential correlation between pathological classification and imaging features. Our aim is to enhance our understanding of PIM and improve its preoperative diagnosis.Case presentationThe first case is a 68-year-old female patient presenting with a brain mass located in the temporal region. Computed tomography (CT) imaging demonstrated the destruction of adjacent bone structures. A right frontal temporal craniectomy was subsequently performed and histological examination pathologically confirmed the lesion was the chordoid variant of PIM. The second case is a 56-year-old male patient who exhibited an irregular soft-tissue mass in the right sphenoid as visualized on brain CT. The patient underwent a surgical intervention for a skull base neoplasm. Postoperative pathological analysis confirmed the presence of the meningothelial variant of PIM. Upon pathological examination, the two cases were respectively classified as atypical meningioma (Grade II) and benign meningioma (Grade I).ConclusionsWhile pathological examination remains indispensable for the definitive confirmation of PIM, the early identification of PIM is critically dependent on radiological imaging methods. The imaging characteristics of PIM exhibit variability across different pathological grades, a factor that can significantly aid in both the diagnostic process and the formulation of appropriate treatment strategies.

Re-thinking some anatomical pathology classifications with an emphasis on solar keratoses and basal cell carcinomas

Re-thinking some anatomical pathology classifications with an emphasis on solar keratoses and basal cell carcinomas

Unique Molecular Alteration of Lobular Breast Cancer: Association with Pathological Classification, Tumor Biology and Behavior, and Clinical Management

Invasive lobular carcinoma (ILC), accounting for up to 15% of diagnosed breast cancers, has garnered significant attention due to the loss of the epithelial cell–cell adhesion molecule E-cadherin. This loss contributes to its distinct biological, morphological, and clinical characteristics compared to non-lobular breast cancers. The use of immunohistochemistry (IHC) for E-cadherin and/or the associated cadherin–catenin complex, such as p120-catenin and beta-catenin, in morphologically equivocal cases, has been increasingly adopted in pathology practice. This approach has substantially improved diagnostic accuracy, interobserver reproducibility, and the identification of new morphologic variants of ILC. ILCs exhibit unique tumor biology, which presents considerable challenges in clinical management, especially in preoperative imaging evaluation, surgical management, and neoadjuvant treatment. Recent advances in translational and clinical research have enhanced our understanding of ILC and have spurred the development of new clinical trials specifically targeting these cancers. This review highlights recent progress in various aspects of ILC, including its unique molecular alteration, pathological classification and diagnostic approach, tumor biology and behavior, key clinical management challenges, and ongoing clinical trials, as well as the role of artificial intelligence in diagnosing ILC radiologically and pathologically. The goal of this review is to provide an updated understanding of the tumor biology, clinical manifestations, and molecular landscape of ILC and to help refine current tumor classification and diagnosis, subsequently improving management strategies and overall outcomes for lobular carcinoma patients.

Construction of nomogram model based on contrast-enhanced ultrasound parameters to predict the degree of pathological differentiation of hepatocellular carcinoma

ObjectiveTo predict the degree of pathological differentiation of hepatocellular carcinoma (HCC) by quantitative analysis the correlation between the perfusion parameters of contrast-enhanced ultrasound (CEUS) and the pathological grades of HCC using VueBox® software.MethodsWe enrolled 189 patients who underwent CEUS and liver biopsy at our hospital from July 2019 to September 2024 and were pathologically confirmed with primary HCC. The Edmondson-Steiner pathological classification system was used as the gold standard for dividing the patients into the low-grade and high-grade groups. The patients were randomly divided into training set and testing set in a ratio of 7:3, in which the parameters of the training set were analyzed by univariate analysis and then stepwise regression to construct the prediction model, and the diagnostic efficacy of the validation model was evaluated by discrimination, calibration, and clinical applicability.ResultsA total of 189 patients with primary hepatocellular carcinoma were enrolled, including 118 patients in the low-grade group and 71 patients in the high-grade group; they were randomly divided into training set of 128 patients and testing set of 61 patients. The prediction model was constructed by logistic regression in the training set, and the final model included three variables: mTTI, FT, and maximum diameter of a single lesion, resulting in the equation was Y=−2.360+1.674X1+1.019X2+0.753X3(2)+1.570X3(3).The area under the ROC curve (AUC) of the training set was 0.831, with a sensitivity of 82.0% and a specificity of 79.5%; the area under the ROC curve (AUC) of the testing set was 0.811, with a sensitivity of 81.0% and a specificity of 70.0%.ConclusionThe regression model constructed by combining multiple parameters can effectively improve the diagnostic performance of CEUS in predicting the pathological differentiation grade of HCC, thus providing a clinical basis and empirical support for the use of CEUS as a diagnostic imaging method for this disease.

A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism

This study introduces an innovative deep learning framework to address the limitations of traditional pathological image analysis and the pressing demand for medical resources in tumor diagnosis. With the global rise in cancer cases, manual examination by pathologists is increasingly inadequate, being both time-consuming and subject to the scarcity of professionals and individual subjectivity, thus impacting diagnostic accuracy and efficiency. Deep learning, particularly in computer vision, offers significant potential to mitigate these challenges. Automated models can rapidly and accurately process large datasets, revolutionizing tumor detection and classification. However, existing methods often rely on single attention mechanisms, failing to fully exploit the complexity of pathological images, especially in extracting critical features from whole-slide images. We developed a framework incorporating a cascaded attention mechanism, enhancing meaningful pattern recognition while suppressing irrelevant background information. Experiments on the Camelyon16 dataset demonstrate superior classification accuracy, model generalization, and result interpretability compared to state-of-the-art techniques. This advancement promises to enhance diagnostic efficiency, reduce healthcare costs, and improve patient outcomes.

STAT4 gene polymorphism may be associated with microscopic polyangiitis susceptibility in a Chinese Guangxi population: A case-control analysis based on propensity score matching.

Microscopic polyangiitis (MPA) is a severe multisystem autoimmune disease featured by small-vessel vasculitis with few or no immune complex, also has a significant genetic predisposition. Growing evidence has confirmed that STAT4 gene is tightly associated with multiple autoimmune diseases, but its contribution to MPA onset is still elusive. The aim was to investigated the association between STAT4 gene polymorphisms (rs7572482, rs7574865 and rs12991409) and MPA susceptibility in a Guangxi population of China. 260 MPA patients and 295 healthy adult volunteers were selected, 1:1 propensity score matching (PSM) was performed to control potential confounding variables, then 199 MPA patients and 199 healthy adult volunteers matched in gender, ethnicity and age were included in this study. High-throughput sequencing and multiplex PCR were applied to detect the target STAT4 SNPs. SHEsis and SNPstats were used to evaluated the allele frequency, genotype frequency, linkage disequilibrium (LD), haplotype, and the association between SNPs and the MPA susceptibility in multiple genetic models. SNP-SNP interactions were explored based on generalized multifactor dimensionality reduction (GMDR) algorithm. Some clinical indicators, such as renal pathology and therapeutic effects, were collected and compared. The allele and genotype frequencies of rs7574865 displayed significant diversities between case group and control group (p < 0.05). Strong LD was found between rs7572482 and rs12991409 (D'=0.9). The haplotype GGT was related to a reduced risk of MPA (OR = 0.661, 95 %CI: 0.469-0.931, p = 0.017), and haplotype GTT might perform an increased risk of MPA (OR = 1.922, 95 %CI: 1.225-3.015, p = 0.004). Rs7574865 polymorphism was associated with an increased risk of MPA in codominant model (OR:2.03; p = 0.0093), dominant model (OR: 1.88p = 0.0023), and overdominant model (OR:1.57; p = 0.027). In Han and male subgroups, rs7574865 polymorphism dramatically increased the MPA risk. GMDR suggested that STAT4 rs7574865 and PTPN22 rs3811021 composed the most risk combinations (p = 0.0010). Moreover, renal pathology, Birmingham vasculitis activity score (BVAS), and alanine aminotransferase (ALT) might be linked with STAT4 gene polymorphisms (p < 0.05). The genetic polymorphism of STAT4 may be associated with MPA susceptibility and renal pathological classification in Chinese Guangxi population; the T allele of rs7574865 may be an important risk factor for MPA.

A retrospective analysis of 338 surgically treated pituitary adenomas.

To summarize the clinical characteristics, pituitary function assessment, postoperative pathological features, and postoperative recurrence of surgically treated pituitary adenomas (PAs). We retrospectively reviewed the data of 338 patients (169 women; average age: 50.01 ± 12.47 years) who underwent surgical PA resection at our hospital during 2016-2020. Pathological PA classification was based on postoperative immunohistochemical staining for pituitary hormones. The clinical, imaging, laboratory, and follow-up data of the patients were statistically analyzed using SPSS v25.0. The main complaints of PA patients were compressive symptoms, followed by endocrine symptoms; 23 tumors were incidentalomas. Compared with the 93 patients with functioning PAs, the 245 patients with non-functioning PAs were older, and had greater tumor diameters and rates of cavernous sinus invasion and pituitary apoplexy (P < 0.05). Postoperative hormonal staining most commonly revealed ACTH (27.4%) and FSH/LH positivity (20.6%); 23.1% of non-functioning tumors stained negative for pituitary hormones. Preoperative anterior hypopituitarism commonly involved the gonadal and thyroid axes, and was correlated with male sex and tumor diameter (P < 0.05). The optimal cut-off for tumor diameter was 1.95 cm for predicting preoperative hypopituitarism in patients with non-functioning PAs. The number of operations and preoperative hypopituitarism were correlated with postoperative pituitary dysfunction (P < 0.05). In the non-functioning PA group, tumor diameter was a risk factor for postoperative recurrence (optimal cutoff: 2.75 cm). The management of patients with surgically treated PAs is limited by deficiencies in pathological classification, assessment of hypopituitarism, and detection of recurrence.

COMPUTED TOMOGRAPHIC ANGIOGRAPHY DATA SET CONTAINING ABDOMINAL AORTIC SEGMENTATION

BACKGROUND: Artificial Intelligence (AI) is widely actively in radiology. The effectiveness of AI algorithms essentially depends on the availability of relevant and representative training and test datasets. Currently, such datasets are highly demanded in open access, particularly CT angiographic (CTA) studies of the abdominal aorta containing not only pathology classification but also vessel segmentation. The disadvantages of existing solutions include small sample volume, narrow specialization, and disparate data preparation methodology. AIM – Design of a CTA dataset containing abdominal aortic segmentation for cases of normal, dilatation, thrombosis, and calcinosis. METHODS: In accordance with the methodology of AI-algorithms testing, the terms of reference for datasets preparation were developed, the necessary sample volume was calculated and approval of the independent ethical committee was obtained. Previously designed original algorithm of semi-automatic segmentation of abdominal aorta on CTA-studies using open access software Slicer 3D was used. Inclusion criteria: CTA studies, or abdominal CT studies with contrast enhancement; presence of arterial phase of the scan; slice thickness not more than 3 mm. Exclusion criteria: presence of any foreign objects in the aortic lumen, aortic dissection. The algorithm was tested on patient data obtained from the Unified Radiologic Information System. Expert evaluation of the compliance of the obtained results with the formed requirements, as well as the assessment of time costs when using the developed segmentation algorithm was carried out. RESULTS: The calculated sample size was 100 CTA studies with a slice thickness ≤ 1.2 mm. Population data: the number of unique patients was 100, the proportion of female patients was 51%; the median age was 62 years ranged from 18 to 84 years. The proportion of pathologic studies was 61%, of which (including combined pathology): 60 cases contained calcification, both by 18 cases with aortic dilatation and thrombosed lumen. Different non-overlapping masks were used for each object. The dataset is presented in the public domain. The average processing time is 0.8 hours per 100 CT-images. CONCLUSION: The public dataset containing 100 CTA studies of segmented abdominal aorta for normal, dilated, thrombotic and calcified cases was created. Results are valuable for AI-algorithms design and anthropomorphic 3D-modeling.

Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology

BackgroundDeep learning (DL) has set new standards in cancer diagnosis, significantly enhancing the accuracy of automated classification of whole slide images (WSIs) derived from biopsied tissue samples. To enable DL models to process these large images, WSIs are typically divided into thousands of smaller tiles, each containing 10–50 cells. Multiple Instance Learning (MIL) is a commonly used approach, where WSIs are treated as bags comprising numerous tiles (instances) and only bag-level labels are provided during training. The model learns from these broad labels to extract more detailed, instance-level insights. However, biopsied sections often exhibit high intra- and inter-phenotypic heterogeneity, presenting a significant challenge for classification. To address this, many graph-based methods have been proposed, where each WSI is represented as a graph with tiles as nodes and edges defined by specific spatial relationships.ResultsIn this study, we investigate how different graph configurations, varying in connectivity and neighborhood structure, affect the performance of MIL models. We developed a novel pipeline, K-MIL, to evaluate the impact of contextual information on cell classification performance. By incorporating neighboring tiles into the analysis, we examined whether contextual information improves or impairs the network’s ability to identify patterns and features critical for accurate classification. Our experiments were conducted on two datasets: COLON cancer and UCSB datasets.ConclusionsOur results indicate that while incorporating more spatial context information generally improves model accuracy at both the bag and tile levels, the improvement at the tile level is not linear. In some instances, increasing spatial context leads to misclassification, suggesting that more context is not always beneficial. This finding highlights the need for careful consideration when incorporating spatial context information in digital pathology classification tasks.

Non-coding RNAs in urinary bladder cancer microenvironment: Diagnostic, therapeutic, and prognostic perspective.

Urinary bladder cancer (UBC) is the ninth most common cancer worldwide. Despite the reliance of UBC therapy on definite pathological grading and classifications, the clinical response among patients varies widely. The molecular basis of this type of cancer appeals to considerable research; hence, new diagnostic and therapeutic options are introduced. Convenient keywords were searched in Google Scholar, PubMed, the Egyptian Knowledge Bank (EKB), and Web of Science. The recent era of UBC research is concerned with non-coding RNAs (ncRNAs), predominantly, microRNAs (miRNAs) and long non-coding RNA (lncRNAs). In addition, snoRNAs, PIWI-interacting RNAs, mitochondrial RNAs, circular, and Schistosoma haematobium-related ncRNAs appeared to contribute to the pathogenesis of the UBC. This review underscored the recently studied ncRNAs and their importance in the pathogenesis of UBC. Besides, we introduced the prospectives regarding their diagnostic, therapeutic, and prognostic significance in UBC clinical settings. Conclusion. Oncogenic and oncosuppressor ncRNAs' definite balances and interaction within the TME of UBC are key players in the fate of the tumor. Thus, profiling ncRNA in-depth inspects the TME of the UBC for better clinical insights.

Expanding the Scope of Microvascular Inflammation: Unveiling Its Presence Beyond Antibody-Mediated Rejection Into T-Cell Mediated Contexts.

Microvascular inflammation (MVI) in kidney transplant biopsies is mainly associated with antibody-mediated rejection (AMR), sparking debate within the Banff Classification of Renal Allograft Pathology regarding its exclusivity. This study reviewed the literature on MVI in T cell-mediated rejection (TCMR) and analyzed MVI in our transplant population. We searched English publications in MEDLINE, Embase, Web of Science, Cochrane, and Google Scholar until June 2024, focusing on glomerulitis (g), peritubular capillaritis (ptc), or MVI in kidney transplant biopsies classified as TCMR. Additionally, we examined g, ptc, and MVI in 69 patients with AMR, TCMR, and no rejection. Our search yielded 541 citations, with 10 studies included, covering 810 TCMR and 156 AMR biopsies. The studies showed g, ptc, and MVI were present in TCMR but were less prevalent and severe than in AMR. In our cohort, AMR had significantly higher g, ptc, and MVI scores compared to aTCMR and ATN, however, aTCMR also displayed MVI. These findings confirm that MVI occurs in aTCMR and should not be exclusively linked to AMR. These findings highlight the need to further explore MVI's significance in TCMR and investigate the inflammatory composition. This could refine the Banff Classification, improving Classification accuracy of kidney transplant pathology assessments.

Convolutional neural networks for detection and classification of corneal pathologies on scheimpflug imaging

Aims/Purpose: Corneal tomography is a standard imaging modality for the assessment of corneal pathologies, but can sometimes be difficult for non‐specialist ophthalmologists to interpret. Convolutional neural networks as a means of deep learning are used in many clinical areas due to their ability to extract information from multidimensional data sets, especially images. The present retrospective, monocentric study was conducted to assess the use of a convolutional neural network in the detection and classification of corneal pathologies on tomography.Methods: This is a retrospective monocentric study at a tertiary university‐based clinical centre. After a strict quality control, 13567 tomography records (from OCULUS Pentacam) were included. Raw data as well as anterior curvature and elevation data were exported. The raw data was then displayed using a self‐developed non‐linear heat map. Two corneal specialists used a self‐developed labelling tool for annotating the records into the following categories: Normal, regular astigmatism, irregular astigmatism, keratoconus, other ectasia, refractive surgery, pterygium and error. After the labelling process, the dataset was divided into a training set, a validation set and a test set.Results: The training and the validation sets were used to train the network. Accuracy of the detection and classification within the test set was then measured. The accuracy of detection and classification of corneal pathologies by the neural network was 87%. This was accomplished by a combination of a learning rate of 0.001, a batch size of 16 and 500 epochs.Conclusions: Here we show that training a convolutional neural network with images may help clinicians to detect and classify different corneal pathologies.

Machine Learning Models With Hyperparameter Optimization for Voice Pathology Classification on Saarbrücken Voice Database.

Early diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification. However, machine learning models and performance metrics employed in these studies vary significantly, making it challenging to determine the optimal model for voice pathology classification. In this study, healthy and pathological voices were classified with state-of-the-art machine learning models, and the performance results of the models were compared. The voice samples employed in our research were sourced from the Saarbrücken Voice Database, a reputable German database. Feature extraction from voice signals was conducted using the Mel Frequency Cepstral Coefficients method. To assess and enhance the models' performance adequately, we employed hyperparameter optimization and implemented a 10-fold cross-validation approach. The outcomes revealed that the support vector machine model exhibited the highest accuracy, achieving 99.19% and 99.50% accuracies in the classification of male and female voice pathologies, respectively.

Direct Surgery for Spinal Arteriovenous Shunt Disease: Indication and Technical Tips

Spinal arteriovenous(AV) shunt disease is rare, although many neurosurgeons may encounter patients with the disease. Recently, the pathological findings and classification of spinal AV shunt disease have been well described. The fundamental treatment of spinal AV shunt disease involves interruption of the shunt, which is achieved by endovascular treatment or direct surgery. Development in diagnostic imaging and technology for endovascular treatment has enabled us to treat many cases. Recently, several multicenter studies on spinal AV shunt diseases revealed that the treatment outcomes of direct surgery were better than those of endovascular treatment. However, in our institute, endovascular treatment is the first line because it is less invasiveness. Although the success and complication rates of endovascular treatment are inferior to those of direct surgery, endovascular treatment remains a therapeutic option for patients with spinal AV shunt disease if it could achieve low complication rates and direct surgery could be performed afterward to rescue the patient. In this article, we elaborate on this disease, surgical indications, limitations of endovascular treatment, and tips for direct surgery.

Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification

Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification

Postoperative Hearing Outcomes and Usefulness of Endoscopy-Assisted Tympanoplasty in Pars Tensa Cholesteatoma.

Introduction In recent years, transcanal endoscopic ear surgery (TEES) has gained widespread recognition as an excellent surgical field for blind spots such as the sinus tympani (ST) when compared to microscopic ear surgery (MES). Objective To investigate the postoperative hearing results for pars tensa cholesteatoma and the indications for utilizing endoscopy. Methods The medical records of 16 patients (10 men and 6 women) with pars tensa cholesteatoma, who received initial surgical treatment between 2018 and 2022, were reviewed. We performed MES, TEES, or endoscopy-assisted MES (dual approach) depending on the pathological involvement in the mastoid cavity and ST. Results The mean age of the patients was 45 years, and the surgical techniques utilized were MES in 2 cases, TEES in 7 cases, and dual approach in 7 cases. The preoperative pathological classification was stage I in 3 patients and stage II in 13 patients. The overall surgical success rates of postoperative hearing outcomes were 69% and 50% (1/2 patients) in the TEES group, 71% (5/7 patients) in the MES group, and 71% (5/7 patients) in the dual approach group. The successful cases (n = 11) were significantly younger and demonstrated better mastoid pneumatization than unsuccessful cases (n = 5). Conclusion Endoscopy-assisted MES is appropriate for treating pars tensa cholesteatoma when pathological involvement is present at the deep bottom of the ST. Early surgical intervention and good eustachian tube function are crucial for improving hearing prognosis. Transcanal endoscopic ear surgery can be particularly useful in identifying and removing residual cholesteatoma within the ST.

Analyses of the risk factors of delayed extubation after extended thymectomy in patients with myasthenia gravis

Objective: To explore the risk factors of delayed extubation after expanded thymectomy in patients with myasthenia gravis. Methods: Patients with myasthenia gravis who underwent expanded thymectomy from May 2021 to January 2024 and were admitted to Intensive Care Unit (ICU) after surgery were retrospectively analyzed. Patients were divided in to the delayed extubation and successful extubation according to the length of mechanical ventilation whether exceeding than 48 hours. Multivariable logistics regression was used to explore the risk factors for delayed extubation after expanded thymectomy in patients with myasthenia gravis. Results: A total of 95 patients were included.15 (15.8%) of whom were delayed extubation. Compared with the successful extubation patients, the length of mechanical ventilation (94.0 h vs. 15.5 h, P<0.001), ICU station (7.1 days vs. 1.7 days, P<0.001), and hospitalization station (16.0 days vs. 9.1 days, P<0.001) were significantly prolonged in the delayed extubation patients. Adjusted for age,gender, BMI, clinical classification, pathological classification of thymoma, dose of pyridostigmine preoperative, perioperative pneumonia, preoperative pulmonary dysfunction, SBT passing for the first time, neck lift time greater than 20 seconds, the multivariable logistic regression shown, the risk of delayed extubation increased by 29% (OR=1.290, 95%CI 1.090-1.714), 1% (OR=1.009, 95%CI 1.002-1.034), 3.9-fold (OR=4.977, 95%CI 1.486-10.939), and 64% (OR=1.635, 95%CI 1.396-2.682) for every 60 mg/day increase in preoperative dose of pyridostigmine, pathological classification of thymoma, perioperative pneumonia, and preoperative pulmonary dysfunction, respectively. Neck lift time greater than 20 seconds can reduce the risk of delayed extubation 48% (OR=0.524, 95%CI 0.322-0.794). Conclusion: Preoperative administration of a large dose of pyridostigmine, pulmonary dysfunction, pathological classification of thymoma, and perioperative pneumonia were risk factors for delayed extubation after thymectomy in these patients. And neck lift time greater than 20 seconds decreased the risk of delayed extubation. In the clinical practice,for high risk of MG patients with delayed extubation, the neck lifting test may be used to assess muscle strength and reduce the occurance of tracheal reintubation and its advise effects.

Mobilize the Troops! Military Involvement in the History of Testicular Cancer.

This review examines the historical evolution of testicular cancer (TCa) treatment, emphasizing the transformative impact of World War II. Prior to the war, cases of more than 50 testicular tumors were exceedingly rare. The mobilization of American troops, especially after the German Blitz, resulted in widespread military health screenings, leading to a surge in incidental TCa diagnoses during the 1940s. This pivotal moment triggered rapid advancements in TCa pathology classification, surgical techniques, tumor marker utilization, and breakthroughs in radiation and chemotherapy. Military-driven research and practices fundamentally reshaped TCa treatment, significantly improving patient outcomes and setting new standards in this disease.