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Urinary incontinence (UI) is defined as any uncontrolled urine leakage. Pelvic floor muscles (PFM) appear to be a crucial aspect of trunk and lumbo-pelvic stability, and UI is one indication of pelvic floor dysfunction. The evaluation of pelvic tilt and lumbar angle is critical in assessing the alignment and posture of the spine in the lower back region and pelvis, and both of these variables are directly related to female dysfunction in the pelvic floor. UI affects a significant number of women worldwide and can have a major impact on their quality of life. However, traditional methods of assessing these parameters involve manual measurements, which are time-consuming and prone to variability. The rehabilitation programs for pelvic floor dysfunction (FSD) in physical therapy often focus on pelvic floor muscles (PFMs), while other core muscles are overlooked. Therefore, this study aimed to predict the activity of various core muscles in multiparous women with FSD using multiple scales instead of relying on Ultrasound imaging. Decision tree, SVM, random forest, and AdaBoost models were applied to predict pelvic tilt and lumbar angle using the train set. Performance was evaluated on the test set using MSE, RMSE, MAE, and R 2 . Pelvic tilt prediction achieved R 2 values > 0.9, with AdaBoost (R 2  = 0.944) performing best. Lumbar angle prediction performed slightly lower with decision tree achieving the highest R 2 of 0.976. Developing a machine learning model to predict pelvic tilt and lumbar angle has the potential to revolutionize the assessment and management of this condition, providing faster, more accurate, and more objective assessments than traditional methods.

Diagnosing atypical melanocytic tumors can be challenging without molecular characterization, necessitating simple tools to enhance diagnostic accuracy in daily practice. This study retrospectively analyzed the utility of p16 immunohistochemistry (IHC) and 9p21 fluorescence in situ hybridization (FISH) on 206 tumors referred for expert second opinion. The performance of p16 and 9p21 was compared to histological diagnosis (both initial and final respectively without and with p16 and 9p21 status), histological subtype, and follow-up data. Negative p16 immunolabelling detected 90% of malignant cases, while only 11% of benign tumors were p16 negative. Homozygous 9p21deletion detected 42% of malignant tumors and excluded 95% of benign ones. Heterozygous deletion showed no diagnostic value. Homozygous 9p21 deletion significantly improved diagnostic confidence (P < 0.001), leading to tumor upgrading (n = 23) or melanoma confirmation (n = 22). Among 97 patients with follow-up, 17 had adverse outcomes. Kaplan–Meier analysis showed no significant difference in progression-free survival between groups (P = 0.64). Combining both techniques ultimately enhanced histological diagnostic confidence in daily practice. However, in cases where p16 is negative without homozygous deletion, or where histological malignancy is uncertain and p16 positive, other p16-inactivation mechanisms or molecular anomalies should be considered, necessitating further molecular investigations.

In this Consensus Statement, a group of experts propose that high-quality immunoassays, rather than indirect immunofluorescence, should be used as the primary screening method for detecting anti-neutrophil cytoplasmic antibodies when diagnosing patients with suspected granulomatosis with polyangiitis or microscopic polyangiitis. Anti-neutrophil cytoplasmic antibodies (ANCAs) are valuable laboratory markers used for the diagnosis of well-defined types of small-vessel vasculitis, including granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA). According to the 1999 international consensus on ANCA testing, indirect immunofluorescence (IIF) should be used to screen for ANCAs, and samples containing ANCAs should then be tested by immunoassays for proteinase 3 (PR3)-ANCAs and myeloperoxidase (MPO)-ANCAs. The distinction between PR3-ANCAs and MPO-ANCAs has important clinical and pathogenic implications. As dependable immunoassays for PR3-ANCAs and MPO-ANCAs have become broadly available, there is increasing international agreement that high-quality immunoassays are the preferred screening method for the diagnosis of ANCA-associated vasculitis. The present Consensus Statement proposes that high-quality immunoassays can be used as the primary screening method for patients suspected of having the ANCA-associated vaculitides GPA and MPA without the categorical need for IIF, and presents and discusses evidence to support this recommendation.

Disclaimer: These ACMG Standards and Guidelines were developed primarily as an educational resource for clinical laboratory geneticists to help them provide quality clinical laboratory services. Adherence to these standards and guidelines is voluntary and does not necessarily assure a successful medical outcome. These Standards and Guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. In determining the propriety of any specific procedure or test, the clinical laboratory geneticist should apply his or her own professional judgment to the specific circumstances presented by the individual patient or specimen. Clinical laboratory geneticists are encouraged to document in the patient’s record the rationale for the use of a particular procedure or test, whether or not it is in conformance with these Standards and Guidelines. They also are advised to take notice of the date any particular guideline was adopted and to consider other relevant medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures. The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants.1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next-generation sequencing. By adopting and leveraging next-generation sequencing, clinical laboratories are now performing an ever-increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes, and epigenetic assays for genetic disorders. By virtue of increased complexity, this shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context the ACMG convened a workgroup in 2013 comprising representatives from the ACMG, the Association for Molecular Pathology (AMP), and the College of American Pathologists to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP, and College of American Pathologists stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories, including genotyping, single genes, panels, exomes, and genomes. This report recommends the use of specific standard terminology—“pathogenic,” “likely pathogenic,” “uncertain significance,” “likely benign,” and “benign”—to describe variants identified in genes that cause Mendelian disorders. Moreover, this recommendation describes a process for classifying variants into these five categories based on criteria using typical types of variant evidence (e.g., population data, computational data, functional data, segregation data). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent. Genet Med17 5, 405–423.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines. In a series of clinically relevant tasks in computational pathology, AI-driven models display marked performance disparities across demographic groups, which can only partially be mitigated by self-supervision on large training datasets and existing debiasing techniques.

Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.

Key Points Definite classification of dementia is based on the underlying neuropathology Accumulation of abnormally folded proteins lies at the heart of dementia neuropathology Alzheimer disease pathology can give rise to subtypes with focal onset in functional networks outside the memory system, such as language, visuospatial and behavioural executive domains Frontotemporal lobar degeneration, associated with aggregates of tau, TDP-43 or FUS, can give rise to three core frontotemporal dementia syndromes and three associated syndromes Clinical classification of dementia syndromes is based on diagnostic criteria that rely heavily on the specificity of affected domains and the evolution of deficits in these domains In vivo biomarkers of disease include imaging findings of morphological, molecular and functional changes, both upstream and downstream of the disease processes The process of phenotyping and classification of dementia has improved over decades of careful clinicopathological correlation, and through the discovery of in vivo biomarkers of disease. Elahi and Miller review the salient features of the most common dementia subtypes, emphasizing neuropathology, epidemiology, risk factors, and signature signs and symptoms. The most definitive classification systems for dementia are based on the underlying pathology which, in turn, is categorized largely according to the observed accumulation of abnormal protein aggregates in neurons and glia. These aggregates perturb molecular processes, cellular functions and, ultimately, cell survival, with ensuing disruption of large-scale neural networks subserving cognitive, behavioural and sensorimotor functions. The functional domains affected and the evolution of deficits in these domains over time serve as footprints that the clinician can trace back with various levels of certainty to the underlying neuropathology. The process of phenotyping and syndromic classification has substantially improved over decades of careful clinicopathological correlation, and through the discovery of in vivo biomarkers of disease. Here, we present an overview of the salient features of the most common dementia subtypes — Alzheimer disease, vascular dementia, frontotemporal dementia and related syndromes, Lewy body dementias, and prion diseases — with an emphasis on neuropathology, relevant epidemiology, risk factors, and signature signs and symptoms.

Rapid diagnostic tests (RDTs) have revolutionized malaria diagnosis, playing a crucial role in improving timely treatment and supporting surveillance efforts, especially in resource-limited settings. However, the performance of RDTs can vary widely due to factors such as parasite genetic diversity, environmental conditions, and operational challenges. Understanding these variations is essential to ensuring accurate and reliable malaria diagnosis. This systematic review and meta-analysis critically evaluate the diagnostic performance of malaria RDTs across sub-Saharan Africa, identifying key gaps and proposing strategies for developing novel tests. By pooling data from 48 studies, the analysis quantifies the sensitivity and specificity of various RDT brands in different settings. The results reveal considerable variability, influenced by factors such as antigen persistence, cross-reactivity with other infections, and genetic polymorphism in the HRP2 gene, which can lead to false positives and negatives. The findings underscore the need for region-specific diagnostic strategies and the development of advanced diagnostic tools capable of detecting low-level parasitemia and differentiating between Plasmodium species. Emerging technologies and multi-platform approaches are recommended to enhance the accuracy and reliability of malaria diagnosis, ultimately contributing to more effective malaria control and elimination efforts in sub-Saharan Africa.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 1 – 3 . Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context 4 . Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet 5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data 6 . With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision–language pretraining for pathology 7 , 8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling. Prov-GigaPath, a whole-slide pathology foundation model pretrained on a large dataset containing around 1.3 billion pathology images, attains state-of-the-art performance in cancer classification and pathomics tasks.

We herein present an overview of the upcoming 5th edition of the World Health Organization Classification of Haematolymphoid Tumours focussing on lymphoid neoplasms. Myeloid and histiocytic neoplasms will be presented in a separate accompanying article. Besides listing the entities of the classification, we highlight and explain changes from the revised 4th edition. These include reorganization of entities by a hierarchical system as is adopted throughout the 5th edition of the WHO classification of tumours of all organ systems, modification of nomenclature for some entities, revision of diagnostic criteria or subtypes, deletion of certain entities, and introduction of new entities, as well as inclusion of tumour-like lesions, mesenchymal lesions specific to lymph node and spleen, and germline predisposition syndromes associated with the lymphoid neoplasms.