Histological Information Research Articles

PATH-59. HISTOLOGIC CORRELATES FOR MOLECULAR FEATURES IN MENINGIOMAS

Abstract Histologic findings are the primary determinant of meningioma prognosis and post-operative management. However, the association of specific pathologic features with molecular alterations has not yet been comprehensively investigated. Here we utilized a large dataset of well-characterized meningiomas to identify correlates between clinically relevant histological and molecular features. Detailed histologic information was collected for 809 resected meningiomas, including cases with DNA methylation (n=380), copy number array (n=252), next-generation sequencing (n=52), RNA risk score (n=394), and/or TERT promoter profiling (n=238). Analyses were conducted on prognostic pathologic features, including WHO Grade, Ki-67, mitotic index (MI), brain invasion, and atypical histologic findings (necrosis, hypercellularity, prominent nucleoli, sheeting, and small cells). Correlation with molecular variables was performed using Fisher’s Exact Tests, including DNA methylation subgroup, RNA risk score, CNV profiles, and TERT promoter mutations. All statistics were corrected for multiple hypothesis testing. Nearly 65% of meningiomas exhibited at least one atypical histologic feature. Among said features, Ki-67/MI, WHO Grade, sheeting, and necrosis were associated with recurrent lesions and known clinical features of aggressive meningiomas. Several histologic features, including hypercellularity, elevated Ki-67/mitotic index, and WHO grade, also correlated with DNA methylation and RNA risk groups. Several chromosomal arm events were related to specific pathologic features (prominent nucleoli, Ki-67, MI, and grade), including chromosomes 1, 4, 10, 14, and 18. Our analysis highlights the continued value in screening for histologic markers of aggressive behavior in meningiomas, as they correlate with the presence of high-risk molecular features and may serve as proxy markers when advanced molecular testing is not available.

The Value of Liver Biopsy and Histology in Liver Disease Diagnosis and Patient Care-a Pragmatic Prospective Clinical Practice Study.

We aimed to examine the correlation of pre-biopsy clinical diagnosis with hepatic histopathology. Liver biopsy provides histologic information and informs physicians about the underlying clinical disease. We hypothesized that expert physicians' pre-biopsy clinical diagnoses may obviate the need for histopathological diagnosis. Patients undergoing liver biopsy to investigate a liver diagnosis were prospectively identified. In the 80 patients included, an anonymous validated questionnaire inquiring about the most likely clinical diagnosis and liver disease stage was completed prospectively by hepatologists before biopsy performance. The most common pre-biopsy diagnoses were alcoholic liver disease (19 diagnoses), followed by non-alcoholic steatohepatitis and autoimmune hepatitis (18 each). Overall, the predicted histologic diagnosis was the same as the histologic diagnosis in 51/80 patients (64%), and thus a new liver disease diagnosis was made in 36% of patients. The diagnosis with the greatest pre-biopsy and post-biopsy diagnosis discrepancy was autoimmune hepatitis, with the correct diagnosis being predicted in 6/18 (33%) of patients (other diagnoses included the following: non-alcoholic steatohepatitis/non-alcoholic fatty liver disease (4), alcoholic liver disease (3), drug-induced liver injury (3), others (2)). For fibrosis staging, when grouped as no fibrosis (F0), fibrosis (F1-F3), or cirrhosis (F4), the fibrosis stage was correctly predicted in 68% of patients (54/80). Notably, 7 patients (9%) who were initially thought to have no or early-stage fibrosis had F4 fibrosis, and 6/80 (8%) patients who were considered to have a liver disease diagnosis before their biopsy had normal histology. Although hepatology experts often predict the correct underlying liver disease diagnosis, histopathological diagnoses different from expected are common.

Capturing sclera anisotropy using direct collagen fiber models. Linking microstructure to macroscopic mechanical properties.

Because of the crucial role of collagen fibers on soft tissue mechanics, there is great interest in techniques to incorporate them in computational models. Recently we introduced a direct fiber modeling approach for sclera based on representing the long-interwoven fibers. Our method differs from the conventional continuum approach to modeling sclera that homogenizes the fibers and describes them as statistical distributions for each element. At large scale our method captured gross collagen fiber bundle architecture from histology and experimental intraocular pressure-induced deformations. At small scale, a direct fiber model of a sclera sample reproduced equi-biaxial experimental behavior from the literature. In this study our goal was a much more challenging task for the direct fiber modeling: to capture specimen-specific 3D fiber architecture and anisotropic mechanics of four sclera samples tested under equibiaxial and four non-equibiaxial loadings. Samples of sclera from three eyes were isolated and tested in five biaxial loadings following an approach previously reported. Using microstructural architecture from polarized light microscopy we then created specimen-specific direct fiber models. Model fiber orientations agreed well with the histological information (adjusted R2's>0.89). Through an inverse-fitting process we determined model characteristics, including specimen-specific fiber mechanical properties to match equibiaxial loading. Interestingly, the equibiaxial properties also reproduced all the non-equibiaxial behaviors. These results indicate that the direct fiber modeling method naturally accounted for tissue anisotropy within its fiber structure. Direct fiber modeling is therefore a promising approach to understand how macroscopic behavior arises from microstructure.

Confocal laser endomicroscopy for gastric neoplasm.

Confocal laser endomicroscopy (CLE) is a novel endoscopic modality that provides real-time histological information via high-resolution magnified view of the mucosa. CLE has a higher sensitivity, specificity, and diagnostic accuracy in detecting atrophic gastritis as compared to chromoendoscopy and narrow-band imaging. It can even predict low-grade and high-grade intraepithelial neoplasia by analyzing gastric pit patterns. CLE may have some advantages over the standard biopsy protocol, such as higher diagnostic yield and fewer biopsy requirements. Its diagnostic accuracy in detecting superficial gastric cancer is higher than that of white-light endoscopy. Inherent limitations, such as a narrow field of vision, can be surpassed by technological advancements and integration with other detection methods. Artificial intelligence holds promise in automated analysis of histopathological images. Thus, CLE can be helpful in screening for early gastric cancer and may help reduce the risk of complications from repeated biopsies, such as mucosal damage, bleeding, and infection.

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text medical notes and converting them to structured, interpretable data. Patients with a lung neoplasm, NSCLC histology, and treatment information in their notes were selected from a repository of over 27 million patients. From these, 200 were randomly selected for this study with the longest and the most recent note included for each patient. An NLP model developed and validated on a large solid and blood cancer oncology cohort was applied to this NSCLC cohort. Two certified tumor registrars and a curator abstracted concepts from the notes: neoplasm, histology, stage, TNM values, and metastasis sites. This manually abstracted gold standard was compared with the NLP model output. Precision and recall scores were calculated. The NLP model extracted the NSCLC concepts with excellent precision and recall with the following scores, respectively: Lung neoplasm 100% and 100%, NSCLC histology 99% and 88%, histology correctly linked to neoplasm 98% and 79%, stage value 98.8% and 92%, stage TNM value 93% and 98%, and metastasis site 97% and 89%. High precision is related to a low number of false positives, and therefore, extracted concepts are likely accurate. High recall indicates that the model captured most of the desired concepts. This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.

The effect of Norethisterone acetate on the uterus of albino rats: histological, histochemical and ultrastructure study

BackgroundNorethisterone acetate (NETA), also known as norethindrone acetate is a progestogens medication that is widely used in birth control pills, menopausal hormone therapy, and for the treatment of gynecological disorders as abnormal uterine bleeding and endometriosis. There is a lack of detailed histological information regarding the effects of NETA on the uterine structure. So, the present study focuses on the uterine histological, histochemical and ultrastructure changes following the exposure to NETA in the albino rats. To do this aim, fourteen adult female albino rats were used. They were randomly divided into two equally groups: Control group and NETA treated group. Albino rats of control group were administered daily food, water and orally distilled water only, while rats of NETA treated group were administered daily orally 20 µg of NETA dissolved in 2 ml distilled water, food, and water. The experiment was continued for three weeks.ResultsThe findings of the present work indicated that the use of NETA has negative effects on the endometrial epithelium (proliferation, autophagy and apoptosis), glands (necrotic, apoptotic or pseudosecretory glands) and stromal and myometrial reactions (granulocytes, connective tissue remodeling, apoptosis, myocytes hypertrophy).ConclusionThis work revealed that NETA has desynchronized progestogenic effect on the uterine tissues of the albino rat and thereby prevent implantation and pregnancy.

Thy-DAMP: deep artificial neural network model for prediction of thyroid cancer mortality.

Despite the rising incidence of differentiated thyroid cancer (DTC), mortality rates have remained relatively low yet crucial for effective patient management. This study aims to develop a deep neural network capable of predicting mortality in patients with differentiated thyroid cancer. Leveraging data from the Surveillance, Epidemiology, and End Results (SEER) database, we developed Thy-DAMP (Deep Artificial Neural Network Model for Prediction of Thyroid Cancer) to forecast mortality in DTC patients. The dataset comprised demographic, histologic, and staging information. Following data normalization and feature encoding, the dataset was partitioned into training, testing, and validation subsets, with model hyperparameters fine-tuned via cross-validation. Among the 63,513 patients, the mean age was 48.22 years (SD = 14.8), with 77.32% being female. Papillary carcinoma emerged as the predominant subtype, representing 62.94% of cases. The majority presented with stage I disease (73.96%). Thy-DAMP demonstrated acceptable performance metrics on both the test and validation sets. Sensitivity was 83.24% (95% CI 76.95-88.40%), specificity was 93.53% (95% CI 93.01-94.02%), and accuracy stood at 93.33% (95% CI 92.82-93.83%). The model exhibited a positive predictive value of 19.76% (95% CI 18.20-21.42%) and a negative predictive value of 99.66% (95% CI 99.53-99.75%). Additionally, Thy-DAMP demonstrated a positive likelihood ratio of 12.86 (95% CI 11.62-14.23), a negative likelihood ratio of 0.18 (95% CI 0.13-0.25), and an area under the receiver operating characteristic curve (AUROC) of 0.95. The model was externally validated on a separate dataset with nearly identical performance. Thy-DAMP showcases considerable promise in accurately predicting mortality in DTC patients, leveraging limited set of patient data.

Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer

BackgroundMost studies on tumour progression from precursor lesion toward gallbladder adenocarcinoma investigate lesions sampled from distinct patients, providing an overarching view of pathogenic cascades. Whether this reflects the tumourigenic process in individual patients remains insufficiently explored. Genomic and epigenomic studies suggest that a subset of gallbladder cancers originate from biliary intraepithelial neoplasia (BilIN) precursor lesions, whereas others form independently from BilINs. Spatial transcriptomic data supporting these conclusions are missing. Moreover, multiple areas with precursor or adenocarcinoma lesions can be detected within the same pathological sample. Yet, knowledge about intra-patient variability of such lesions is lacking.MethodsTo characterise the spatial transcriptomics of gallbladder cancer tumourigenesis in individual patients, we selected two patients with distinct cancer aetiology and whose samples simultaneously displayed multiple areas of normal epithelium, BilINs and adenocarcinoma. Using GeoMx digital spatial profiling, we characterised the whole transcriptome of a high number of regions of interest (ROIs) per sample in the two patients (24 and 32 ROIs respectively), with each ROI covering approximately 200 cells of normal epithelium, low-grade BilIN, high-grade BilIN or adenocarcinoma. Human gallbladder organoids and cell line-derived tumours were used to investigate the tumour-promoting role of genes.ResultsSpatial transcriptomics revealed that each type of lesion displayed limited intra-patient transcriptomic variability. Our data further suggest that adenocarcinoma derived from high-grade BilIN in one patient and from low-grade BilIN in the other patient, with co-existing high-grade BilIN evolving via a distinct process in the latter case. The two patients displayed distinct sequences of signalling pathway activation during tumour progression, but Semaphorin 4 A (SEMA4A) expression was repressed in both patients. Using human gallbladder-derived organoids and cell line-derived tumours, we provide evidence that repression of SEMA4A promotes pseudostratification of the epithelium and enhances cell migration and survival.ConclusionGallbladder adenocarcinoma can develop according to patient-specific processes, and limited intra-patient variability of precursor and cancer lesions was noticed. Our data suggest that repression of SEMA4A can promote tumour progression. They also highlight the need to gain gene expression data in addition to histological information to avoid understimating the risk of low-grade preneoplastic lesions.

A Clinico-Histopathological Study of Nail Dermatoses in a Tertiary Care Hospital.

Introduction Nail disorders account for an important component of all dermatological conditions.Nail abnormalities can result from local pathology or systemic diseases. Pathologies can lead to pain and impaired fine touch and are aesthetically distressing. Clinical assessment of nail pathologies is seldom accurate; moreover, the limited available investigative modalities make it difficult to correctly diagnose the disorders. Nail biopsies provide crucial histological information, especially for nail-limited dermatoses, though they are infrequently used and technically challenging. Proper biopsy techniques are vital to avoid complications like nail dystrophy and to ensure accurate diagnoses and effective treatments. Materials and methods A cross-sectional and observational study was conducted in the Dermatology Department of a tertiary care hospital in Maharashtra from November 2022 to July 2024, involving 51 patients aged 8-80 years with undiagnosed nail dermatoses. Patients with bleeding disorders, anesthesia allergies, and peripheral vascular diseases were excluded. Ethical clearance and written consent were obtained. In the case of pediatrics, patients' parental consent was obtained. Observation and results The age of the patients ranged from eight to 74 years, with a mean age of 38.04 years. The most affected age groups were 20-29 and 30-39 years old. Nineteen (37%) were manual laborers, followed by 10 (20%) students and nine (18%) professional workers. Symptoms lasted from one month to eight years, with a mean duration of 16.65 months. The most common dermatoses diagnosed clinically were as follows: 18 (35.3%) were onychomycosis, 16 (31.4%) were psoriasis, and eight (15.7%) were lichen planus. However, on histopathology, 20 (37.2%) were onychomycosis, followed by 16 (31.4%) of psoriasis, and eight (15.7%) were lichen planus. Conclusion This study highlights the critical role of nail biopsies in diagnosing nail disorders, particularly among middle-aged males who were manual laborers by occupation. It underscores the importance of combining clinical and histopathological approaches to accurately diagnose and manage, advocating for continued research and collaboration to improve patient outcomes.

Illuminating immunotherapy response via precision T cell-targeted PET imaging.

Traditionally, immunotherapy agent selection and treatment strategies are guided by biopsy-based histological information. However, biopsies are limited in that they are invasive, provide static information regarding the tumor immune microenvironment, and only sample a small part of one tumor site. The tumor microenvironment is dynamic and heterogenous. As a result, the immune milieu at one site may be distinct from other metastatic sites. These factors make identifying which patients are likely to respond to different immunotherapies and which harbor intrinsic resistance mechanisms difficult to identify based on a biopsy alone. As such, there is significant interest in alternative methodologies that better characterize the tumor immune microenvironment and monitor immunotherapy response. PET imaging potentially offers a non-invasive way to characterize the tumor immune microenvironment at the primary tumor and metastases and allow for longitudinal characterization. Herein, we review pre-clinically and clinically tested T cell-targeted PET radiopharmaceuticals, as T cells have been the dominant immunotherapy target, and their utility in both evaluating response to immunotherapy and in understanding the systemic immune response to treatment with immunotherapeutics.

Deepening our understanding of the environmental influences on the reproductive dynamics of the main fishery stock of Argentine hake, Merluccius hubbsi, in the southwestern Atlantic Ocean

AbstractThis study analyzed the inter‐annual variability in the reproductive dynamics of the Argentine hake from Patagonian waters during the last 20 years concerning the environmental conditions and the nutritional status of females. Macroscopic and histological information on gonadal maturity, female condition factor (K), and oceanographic data at different sampling sites throughout the studied region were obtained from research surveys carried out during the main spawning season of this species. Four stages, according to the maturity ovarian phases composition, were assigned annually at each sampling site to assess reproductive dynamics: vitellogenesis, ovulation, regression, and resting. The spatial pattern of inter‐annual variability in reproductive dynamics was described, and the influence of temperature and female condition on the probability of ovulation and regression was assessed using generalized linear models. The area with the lowest inter‐annual variability in the hake reproductive dynamics was observed between 43 and 45°S (50 to 80 m depth) in relation to ovulation events. The vitellogenesis stage was more frequently found adjacent to the reproductive area, whereas the regression was mainly observed in deeper waters. Both bottom and surface temperatures explained the ovulation and regression stages, showing a positive and negative relationship, respectively. Lower values of the K factor, associated with low food availability, were further linked to the occurrence of regression. Finally, it is worth noting that the relationship observed between the ovulation/spawning dynamics of Argentine hake and water temperature suggests that the reproductive success of this species could be affected in the future scenario of climate change.

A universal pipeline to combine spatial transcriptomics, proteomics, and diagnostic H&E assays on a single tissue section to study tissue microenvironment.

e14657 Background: Spatial multi-omics approaches, including spatial transcriptomics (ST) and spatial proteomics (SP), that elucidate the complex tissue microenvironment (TME) are gaining traction in cancer research studies. However, combining these approaches onto one tissue section remains challenging. ST methods visualize, locate and quantify RNA targets, evaluating the transcriptome of the TME at the subcellular level while SP methods stain and visualize protein biomarkers at the cellular level. Here we show a pipeline that combines these ST and SP methods with Hematoxylin and Eosin (H&E) approach on a single tissue section to achieve single slide multi-omics data with high resolution tissue morphology images. Methods: 5µm formalin-fixed paraffin-embedded sections of human lung cancer samples were placed on a Xenium (10x Genomics, Pleasanton, CA) slides and treated to expose RNA molecules. Probe hybridization was performed with a human lung panel kit containing 289 barcoded DNA padlock probes. Downstream ligation and amplification generated copies of the barcode and thereafter, the Xenium Analyzer performed imaging and data collection. The same tissue section was then stained and imaged in COMET (Lunaphore Technologies SA, Switzerland) using sequential immunofluorescence (seqIF) method to detect 40 protein targets in situ. Serial sections of the specimens were used for direct H&E and seqIF staining for comparative validation. Results: Multi-omics images generated from Xenium and COMET were overlayed, jointly analyzed and interpreted. Staining specificity of the protein targets for the post-Xenium slides remain comparable with the direct COMET-stained slide. 40-plex COMET staining intensity in post-Xenium slide was minimally weaker compared to direct COMET-stained slide. Additionally, H&E staining of the post-Xenium/COMET slide and direct COMET-stained slide were comparable to the direct H&E-stained section, demonstrating discernable tissue landscape features despite a fainter hematoxylin stain. Overall, tissue integrity and cell morphology were well-preserved across the different workflows performed on each section, and the protein targets remain relatively stable for labelling after in situ RNA detection process. Conclusions: We present a pipeline that combines two spatial omics approaches with the diagnostic standard H&E staining on a single tissue section, allowing the same cells to be assessed for their histological, proteomic, and transcriptomic information. A visual comparison of images showed minimal compromise to cellular morphology and protein stability at the end of the pipeline compared to direct staining of serial sections. This pipeline could be potentially generalized to most of the ST and SP approaches, thus advancing multi-omics cancer research and potentially clinical diagnostics in the future.

A multi-modal artificial intelligence model trained on H&E tissue images and clinical patient data for prediction of BRAF mutations in colorectal cancer.

e15161 Background: Analyzing genetic mutations is essential for personalized therapy decisions in oncology and beyond. In colorectal cancer (CRC), mutations in BRAF ( BRAFmut) render patients eligible for targeted treatment with BRAF inhibitors and immunotherapy. BRAFmut is routinely analyzed via genomic sequencing, which, however, is costly and does not provide diagnostic results immediately. Reliable detection of BRAFmut status from histological images using artificial intelligence (AI) could provide a more accessible and faster diagnostic alternative. Previous approaches to image-based BRAFmut analysis, however, failed to reach a critical level of accuracy on independent test cohorts, with reported area under the receiver operating characteristic curve (AUROC) values typically below 0.8. Methods: We hypothesized that combining tissue image analysis with clinical patient data could improve diagnostic accuracy for BRAFmut. Following our hypothesis, we developed a multi-modal AI model on n = 426 CRC cases from the TCGA database using BRAFmut data, H&E-stained tissue images, and information on patient parameters that are readily available in clinical practice and that are known to be associated with BRAFmut frequency. The model was based on a vision transformer architecture incorporating pathology-specific information using a pretrained embedding model for encoding tissue image features. Model performance was validated on an independent hold-out TCGA cohort of n = 107 samples and an additional external cohort of n = 104 CRC samples from the CPTAC database. Results: Our multi-modal AI model reached an AUROC of 0.85 ± 0.02 on the TCGA hold-out test set. Notably, on the second, external CPTAC dataset the predictive accuracy level was similar (AUROC of 0.83 ± 0.02) indicating the robustness of our approach to generalize to different datasets. Additional analyses showed that the significant performance boost over previous approaches was gained by the multi-modal analysis setup, as predicting BRAFmut based on either tissue, or clinical data alone led to lower accuracy values. Explainability analysis of AI results confirmed that relevant biological features on the histological slides, mainly tumor areas, were considered by the model for decision making. Conclusions: The combination of H&E images with clinically readily available patient parameters enabled us to develop a model predicting BRAFmut in CRC that surpasses previous approaches in diagnostic accuracy on independent evaluation cohorts. This indicates that combining histological tissue information with clinical parameters can increase the ability of AI models to predict genetic variants from tissue and more generally highlights the potential of multi-modal deep learning approaches to enhance predictive AI models in digital pathology.

Prognostic performance of the 2023 FIGO staging schema for endometrial cancer

ObjectiveTo assess the prognostic performance of the 2023 International Federation of Gynecology and Obstetrics (FIGO) endometrial cancer staging schema. MethodsThis retrospective cohort study queried the Commission-on-Cancer's National Cancer Database. Study population was 129,146 patients with stage I-IV endometrial cancer per the 2009 FIGO staging schema. Stage-shifting and overall survival (OS) were assessed according to the 2023 FIGO staging schema. ResultsUpstage (IA → II, 21.4 %; IB → II, 53.0 %) and downstage (IIIA→IA3, 22.2 %) occurred in both early and advanced diseases. Inter-stage prognostic performance improved in the 2023 schema with widened 5-year OS rate difference between the earliest and highest stages (68.2 % to 76.9 %). Stage IA1-IIB and IIC had distinct 5-year OS rate differences (85.8–96.1 % vs 75.4 %). The 5-year OS rate of the 2009 stage IIIA disease was 63.9 %; this was greater segregated in the 2023 schema: 88.0 %, 62.4 %, and 55.7 % for IIIA→IA3, IIIA1, and IIIA2, respectively (inter-substage rate-difference, 32.3 %). This 5-year OS rate of stage IA3 disease was comparable to the 2023 stage IB-IIB diseases (88.0 % vs 85.8–89.5 %). In the 2023 stage IIIC schema (micrometastasis rates: 29.6 % in IIIC1 and 15.6 % in IIIC2), micrometastasis and macrometastasis had the distinct 3-year OS rates in both pelvic (IIIC1-i vs IIIC1-ii, 84.9 % vs 71.1 %; rate-difference 13.8 %) and para-aortic (IIIC2-i vs IIIC2-ii, 82.9 % vs 65.2 %; rate-difference 17.7 %) nodal metastasis cases. The 5-year OS rate of the 2009 stage IVB disease was 23.4 %; this was segregated to 25.4 % for stage IVB and 19.2 % for stage IVC in the 2023 staging schema (rate-difference, 6.2 %). ConclusionThe 2023 FIGO endometrial cancer staging schema is a major revision from the 2009 FIGO schema. Almost doubled enriched sub-stages based on detailed anatomical metastatic site and incorporation of histological information enable more robust prognostication.

Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 Expression Rates in Invasive Breast Carcinoma: A Study of 21 Institutions.

Laboratories performing predictive marker testing for breast carcinoma are encouraged to compare patient results to published benchmarks. To collect expression rates for estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2) in invasive breast carcinoma from multiple laboratories. Participants submitted data from up to 50 primary cases during the study period. Participants reported ER, PgR, and HER2 results in addition to demographic and histologic information. Participants also provided annual institution-level expression rates. A total of 21 institutions submitted data for 687 cases. Aggregate positivity rates for ER and PgR were 85.6% and 75.1%, respectively. Receptor positivity rates were higher in well-differentiated (grade 1) tumors (ER, 97.4%; PgR, 88.0%) compared with moderately differentiated (grade 2) tumors (ER, 92.4%; PgR, 84.0%) and poorly differentiated (grade 3) tumors (ER, 61.8%; PgR, 48.0%). Expression rates were higher in postmenopausal women (ER, 87.2%) than premenopausal women (ER, 79.6%) and higher in lobular carcinomas (ER, 98.7%; PgR, 85.3%) than ductal carcinomas (ER, 84.1%; PgR, 74.5%). The aggregate HER2 positivity (score 3+) rate was 9.0%. The aggregate HER2 equivocal (score 2+) rate was 14.5%. Of 81 equivocal (score 2+) cases, 70 (86.4%) were nonamplified. The data from this study provide multi-institutional benchmark data to assist laboratories performing periodic comparisons as part of a quality management program. Overall expression rates were generally similar to those of other published reports, with the exception of the ER-negative and HER2-positive rates, both of which were somewhat lower.

New CNS tumor classification: The importance in pediatric neurosurgical practice.

The management of the central nervous system (CNS) tumors in the pediatric population is crucial in neurosurgical practice. The World Health Organization (WHO) has evolved its classification of CNS tumors from the 19th century to the 5th edition, published in 2021, incorporating molecular advancements. This transition from morphology to molecular characterization is ongoing. This manuscript analyzes the modifications introduced in the 5th edition of WHO's CNS tumor classification, particularly focusing on pediatric tumor families. The paper integrates clinical, morphological, and molecular information, aiming to guide pediatric neurosurgeons in their daily practice and interdisciplinary discussions. The 5th edition of the WHO classification introduces a hybrid taxonomy that incorporates both molecular and histological components. The terminology shifts from "entity" to "type" and "subtype," aiming to standardize terminology. Tumor grading experiences changes, integrating molecular biomarkers for prognosis. The concept of integrated layered diagnosis is emphasized, where molecular and histological information is combined systematically. The 5th edition of the WHO CNS classification signifies a paradigm shift toward molecular characterization. The incorporation of molecular advances, the layered diagnostic approach, and the inclusion of clinical, morphological, and molecular information aim to provide comprehensive insights into pediatric CNS tumors. This classification offers valuable guidance for pediatric neurosurgeons, aiding in precise diagnosis and treatment planning for these complex neoplasms.

Histological analysis of the dermal and hypodermal layers of the face and correlation with high-frequency 24 MHz ultrasonography and elastosonography.

Knowledge of the structure of the face is of fundamental importance. In fact, the face is treated in many areas of medicine, from dermatology, to maxillofacial surgery, to otorhinolaryngology, to ophthalmology, etc. and anti-aging aesthetic treatments, and those for the resolution of blemishes are on the increase. For ethical reasons it is not possible to take biopsy samples for facial analysis in the aesthetic field. The main aim of this study was to demonstrate that a high-resolution bimodal ultrasound examination, combined with elastosonography, could be a valid tool for pre-treatment morphological evaluation. To achieve this goal, skin samples were taken from the forehead, zygomatic area, nasolabial fold, upper and lower lip from cadavers to histologically characterize their structure. Subsequently, these same areas were evaluated in vivo using conventional B-mode ultrasound with a 24 MHz high-frequency probe, and elastosonography. The data obtained with the different techniques were compared, in order to state that modern ultrasound techniques can provide similar histological information. The analysis showed that the superficial hypodermis presented a different shape and structure in the different areas, with the exception of the areas of the upper and lower lip, which appeared similar. With aging, the forehead and zygomatic area showed a volumetric increase in the superficial hypodermic layer, while the lip showed non-structural changes. The morphology of the nasolabial fold remained unchanged. When it is not possible to perform histological investigations on the face, to understand its characteristics and dynamics, ultrasound with a 24 MHz probe would seem to be the most suitable method, while elastosonography could be a valid method for evaluating the stiffness of the structural components.

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Clinical features and prognosis of ANCA-associated vasculitis patients who were double-seropositive for myeloperoxidase-ANCA and proteinase 3-ANCA

Anti-neutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV) patients with dual positivity for proteinase 3-ANCA (PR3-ANCA) and myeloperoxidase-ANCA (MPO-ANCA) are uncommon. We aimed to investigate these idiopathic double-positive AAV patients’ clinical features, histological characteristics, and prognosis. We reviewed all the electronic medical records of patients diagnosed with AAV to obtain clinical data and renal histological information from January 2010 to December 2020 in a large center in China. Patients were assigned to the MPO-AAV group or PR3-AAV group or idiopathic double-positive AAV group by ANCA specificity. We explored features of idiopathic double-positive AAV. Of the 340 patients who fulfilled the study inclusion criteria, 159 (46.76%) were female, with a mean age of 58.41 years at the time of AAV diagnosis. Similar to MPO-AAV, idiopathic double-positive AAV patients were older and had more severe anemia, lower Birmingham Vasculitis Activity Score (BVAS) and C-reactive protein (CRP) levels, less ear, nose, and throat (ENT) involvement, higher initial serum creatinine and a lower estimated glomerular filtration rate (eGFR) when compared with PR3-AAV (P < 0.05). The proportion of normal glomeruli of idiopathic double-positive AAV was the lowest among the three groups (P < 0.05). The idiopathic double-positive AAV patients had the worst remission rate (58.8%) among the three groups (P < 0.05). The relapse rate of double-positive AAV (40.0%) was comparable with PR3-AAV (44.8%) (P > 0.05). Although there was a trend toward a higher relapse rate of idiopathic double-positive AAV (40.0%) compared with MPO-AAV (23.5%), this did not reach statistical significance (P > 0.05). The proportion of patients who progressed to ESRD was 47.1% and 44.4% in the idiopathic double-positive AAV group and MPO-AAV group respectively, without statistical significance. Long-term patient survival also varied among the three groups (P < 0.05). Idiopathic double-positive AAV is a rare clinical entity with hybrid features of MPO-AAV and PR3-AAV. MPO-AAV is the “dominant” phenotype in idiopathic double-positive AAV.

Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images.

There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.