Hasil untuk "Cytology"

M. Cora, L. Kooistra, G. Travlos

S. de Sanjosé, Mireia Díaz, X. Castellsagué et al.

D. Solomon, D. Davey, R. Kurman et al.

G. Koliopoulos, V. Nyaga, N. Santesso et al.

D. Steward, S. Carty, R. Sippel et al.

Importance Approximately 20% of fine-needle aspirations (FNA) of thyroid nodules have indeterminate cytology, most frequently Bethesda category III or IV. Diagnostic surgeries can be avoided for these patients if the nodules are reliably diagnosed as benign without surgery. Objective To determine the diagnostic accuracy of a multigene classifier (GC) test (ThyroSeq v3) for cytologically indeterminate thyroid nodules. Design, Setting, and Participants Prospective, blinded cohort study conducted at 10 medical centers, with 782 patients with 1013 nodules enrolled. Eligibility criteria were met in 256 patients with 286 nodules; central pathology review was performed on 274 nodules. Interventions A total of 286 FNA samples from thyroid nodules underwent molecular analysis using the multigene GC (ThyroSeq v3). Main Outcomes and Measures The primary outcome was diagnostic accuracy of the test for thyroid nodules with Bethesda III and IV cytology. The secondary outcome was prediction of cancer by specific genetic alterations in Bethesda III to V nodules. Results Of the 286 cytologically indeterminate nodules, 206 (72%) were benign, 69 (24%) malignant, and 11 (4%) noninvasive follicular thyroid neoplasms with papillary-like nuclei (NIFTP). A total of 257 (90%) nodules (154 Bethesda III, 93 Bethesda IV, and 10 Bethesda V) had informative GC analysis, with 61% classified as negative and 39% as positive. In Bethesda III and IV nodules combined, the test demonstrated a 94% (95% CI, 86%-98%) sensitivity and 82% (95% CI, 75%-87%) specificity. With a cancer/NIFTP prevalence of 28%, the negative predictive value (NPV) was 97% (95% CI, 93%-99%) and the positive predictive value (PPV) was 66% (95% CI, 56%-75%). The observed 3% false-negative rate was similar to that of benign cytology, and the missed cancers were all low-risk tumors. Among nodules testing positive, specific groups of genetic alterations had cancer probabilities varying from 59% to 100%. Conclusions and Relevance In this prospective, blinded, multicenter study, the multigene GC test demonstrated a high sensitivity/NPV and reasonably high specificity/PPV, which may obviate diagnostic surgery in up to 61% of patients with Bethesda III to IV indeterminate nodules, and up to 82% of all benign nodules with indeterminate cytology. Information on specific genetic alterations obtained from FNA may help inform individualized treatment of patients with a positive test result.

R. Nayar, D. Wilbur, D. Solomon

G. Barkan

G. Barkan, Eva M. Wojcik, R. Nayar et al.

The main purpose of urine cytology is to detect high-grade urothelial carcinoma (HGUC). With this principle in mind, The Paris System (TPS) Working Group, composed of cytopathologists, surgical pathologists, and urologists, has proposed and published a standardized reporting system that includes specific diagnostic categories and cytomorphologic criteria for the reliable diagnosis of HGUC. This paper outlines the essential elements of TPS and the process that led to the formation and rationale of the reporting system. The Paris System Working Group, organized at the 2013 International Congress of Cytology, conceived a standardized platform on which to base cytologic interpretation of urine samples. The widespread dissemination of this approach to cytologic examination and reporting of urologic samples and the scheme's universal acceptance by pathologists and urologists is critical for its success. For urologists, understanding the diagnostic criteria, their clinical implications, and the limitations of TPS is essential if they are to utilize urine cytology and noninvasive ancillary tests in a thoughtful and practical manner. This is the first international/inclusive attempt at standardizing urinary cytology. The success of TPS will depend on the pathology and urology communities working collectively to improve this seminal paradigm shift, and optimize the impact on patient care.

G. Vogt

This article reviews the morphogenesis, morphology, histology, ultrastructure, and structural–functional relationships of the hepatopancreas, the main metabolic organ of the Decapoda. The hepatopancreas develops in early larval stages from a pair of lateral lobes of the midgut anlage. In adults, it consists of hundreds of blindly ending tubules that are enveloped by a muscle net consisting of longitudinal and circular fibers. Stem cells at the distal ends of the tubules give rise to three ultrastructurally different epithelial cell types, the R‐, F‐, and B‐cells. Histochemistry, immunohistochemistry, in situ hybridization, and monitoring of ultrastructural changes under different experimental conditions allowed the attribution of functions to these cell types. R‐cells serve for the absorption and metabolization of nutrients, storage of energy reserves and minerals, synthesis of lipoproteins for export to other organs, detoxification of heavy metals, and excretion of uric acid. F‐cells synthesize digestive enzymes and blood proteins involved in oxygen transport and immune defense. They also detoxify some heavy metals and probably organic xenobiotics. B‐cells are assumed to produce and recycle fat emulsifiers. The hepatopancreas tubules lack nerves. The presence of scattered M‐cells with putative endocrine function in the epithelium suggests that the hepatopancreas is mainly hormonally controlled. M‐cells probably represent a self‐perpetuating cell lineage independent from E‐cells. The interstitium between the tubules contains connective tissue, arterioles, hemolymph with circulating hemocytes, and fixed phagocytes that eliminate pathogens. The hepatopancreas is histologically and ultrastructurally uniform throughout the Decapoda, despite their broad variety in body size, morphology, life style, and ecology. However, in a few cavernicolous and deep‐sea shrimps parts of the hepatopancreas are transformed into large oil storing and bioluminescent compartments. Within the malacostracan crustaceans, the hepatopancreas of the Decapoda is most similar to the digestive gland of the Euphausiacea, supporting close taxonomic relationship of these two taxa.

Yan Kong, Yuan Yin, Hongan Chen et al.

Automated analysis of Pap smear images is critical for cervical cancer screening but remains challenging due to dense cell distribution and complex morphology. In this paper, we present our winning solution for the RIVA Cervical Cytology Challenge, achieving 1st place in Track B and 2nd place in Track A. Our approach leverages a powerful baseline, integrating the Co-DINO framework with a Swin-Large backbone for robust multi-scale feature extraction. To address the dataset's unique fixed-size bounding box annotations, we formulate the detection task as a center-point prediction problem. Tailoring our approach to this formulation, we introduce a center-preserving data augmentation strategy and an analytical geometric box optimization to effectively absorb localization jitter. Finally, we apply track-specific loss tuning to adapt the loss weights for each task. Experiments demonstrate that our targeted optimizations improve detection performance, providing an effective pipeline for cytology image analysis. Our code is available at https://github.com/YanKong0408/Center-DETR.

Lautaro Kogan, María Victoria Ríos

Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is challenged by severe class imbalance and frequent nuclear overlap. We present our approach to the RIVA Cervical Cytology Challenge (ISBI 2026), which requires multi-class detection of eight Bethesda cell categories under these conditions. Using YOLOv11m as the base architecture, we systematically evaluate three strategies to improve detection performance: loss reweighting, data resampling and transfer learning. We build an ensemble by combining models trained under each strategy, promoting complementary detection behavior and combining them through Weighted Boxes Fusion (WBF). The ensemble achieves a mAP50-95 of 0.201 on the preliminary test set and 0.147 on the final test set, representing a 29% improvement over the best individual model on the final test set and demonstrating the effectiveness of combining complementary imbalance mitigation strategies.

Hao Jiang, Yanning Zhou, Yi Lin et al.

Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.

Vedrana Ivezić, Ashwath Radhachandran, Ekaterina Redekop et al.

Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is challenging due to the heterogeneity in staining and preparation methods of samples, differences across organs, and the limited availability of large, diverse, annotated datasets. Developing a task-specific model for every cytology application is impractical and non-cytology-specific foundation models struggle to generalize to tasks in this domain where the emphasis is on cell morphology. To address these challenges, we introduce CytoFM, the first cytology self-supervised foundation model. Using iBOT, a self-supervised Vision Transformer (ViT) training framework incorporating masked image modeling and self-distillation, we pretrain CytoFM on a diverse collection of cytology datasets to learn robust, transferable representations. We evaluate CytoFM on multiple downstream cytology tasks, including breast cancer classification and cell type identification, using an attention-based multiple instance learning framework. Our results demonstrate that CytoFM performs better on two out of three downstream tasks than existing foundation models pretrained on histopathology (UNI) or natural images (iBOT-Imagenet). Visualizations of learned representations demonstrate our model is able to attend to cytologically relevant features. Despite a small pre-training dataset, CytoFM's promising results highlight the ability of task-agnostic pre-training approaches to learn robust and generalizable features from cytology data.

Rupam Mukherjee, Rajkumar Daniel, Soujanya Hazra et al.

Cytology is a valuable tool for early detection of oral squamous cell carcinoma (OSCC). However, manual examination of cytology whole slide images (WSIs) is slow, subjective, and depends heavily on expert pathologists. To address this, we introduce the first weakly supervised deep learning framework for patient-level diagnosis of oral cytology whole slide images, leveraging the newly released Oral Cytology Dataset [1], which provides annotated cytology WSIs from ten medical centres across India. Each patient case is represented as a bag of cytology patches and assigned a diagnosis label (Healthy, Benign, Oral Potentially Malignant Disorders (OPMD), OSCC) by an in-house expert pathologist. These patient-level weak labels form a new extension to the dataset. We evaluate a baseline multiple-instance learning (MIL) model and a proposed Region-Affinity Attention MIL (RAA-MIL) that models spatial relationships between regions within each slide. The RAA-MIL achieves an average accuracy of 72.7%, weighted F1-score of 0.69 on an unseen test set, outperforming the baseline. This study establishes the first patient-level weakly supervised benchmark for oral cytology and moves toward reliable AI-assisted digital pathology.

Minseok Lee, Tal Lifshitz, Young Ki Lee et al.

Conventional bright-field (BF) cytology of thyroid fine-needle aspiration biopsy (FNAB) suffers from staining variability and limited subcellular contrast. Here, we present a refractive index-correlated pseudocoloring (RICP) framework that integrates quantitative refractive index (RI) maps obtained by holotomography (HT) with color BF images to enhance diagnostic interpretability. The imaging platform combines a digital micromirror device (DMD)-based HT system with an RGB LED illumination module, enabling simultaneous acquisition of RI tomograms and BF images from PAP-stained thyroid samples. The RICP algorithm adaptively embeds RI-derived structural information into the least-occupied hue channel, preserving color fidelity while enhancing nuclear and cytoplasmic contrast. Applied to benign and malignant thyroid clusters, RICP revealed diagnostically relevant features such as nucleoli, lipid droplets, and nuclear irregularities, and hue-saturation analysis quantitatively differentiated cytological categories. This perceptually grounded, label-free framework bridges conventional color cytology and quantitative optical imaging for improved diagnostic precision.

Hussien Al-Asi, Jordan P Reynolds, Shweta Agarwal et al.

Advancements in artificial intelligence (AI) are transforming pathology by integrat-ing large language models (LLMs) with retrieval-augmented generation (RAG) and domain-specific foundation models. This study explores the application of RAG-enhanced LLMs coupled with pathology foundation models for thyroid cytology diagnosis, addressing challenges in cytological interpretation, standardization, and diagnostic accuracy. By leveraging a curated knowledge base, RAG facilitates dy-namic retrieval of relevant case studies, diagnostic criteria, and expert interpreta-tion, improving the contextual understanding of LLMs. Meanwhile, pathology foun-dation models, trained on high-resolution pathology images, refine feature extrac-tion and classification capabilities. The fusion of these AI-driven approaches en-hances diagnostic consistency, reduces variability, and supports pathologists in dis-tinguishing benign from malignant thyroid lesions. Our results demonstrate that integrating RAG with pathology-specific LLMs significantly improves diagnostic efficiency and interpretability, paving the way for AI-assisted thyroid cytopathology, with foundation model UNI achieving AUC 0.73-0.93 for correct prediction of surgi-cal pathology diagnosis from thyroid cytology samples.

Garima Jain, Sanghamitra Pati, Mona Duggal et al.

Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On the other hand, oral cytology of brush biopsy offers a minimally invasive and lower cost alternative, provided that the remaining challenges, inter observer variability and unavailability of expert pathologists can be addressed using artificial intelligence. Development and validation of robust AI solutions requires access to large, labeled, and multi-source datasets to train high capacity models that generalize across domain shifts. We introduce the first large and multicenter oral cytology dataset, comprising annotated slides stained with Papanicolaou(PAP) and May-Grunwald-Giemsa(MGG) protocols, collected from ten tertiary medical centers in India. The dataset is labeled and annotated by expert pathologists for cellular anomaly classification and detection, is designed to advance AI driven diagnostic methods. By filling the gap in publicly available oral cytology datasets, this resource aims to enhance automated detection, reduce diagnostic errors, and improve early OSCC diagnosis in resource-constrained settings, ultimately contributing to reduced mortality and better patient outcomes worldwide.

Janine Wesslowski, Sadia Safi, Michelle Rottmann et al.

Upon engagement of one of the nineteen secreted Wnt signaling proteins with one of the ten Frizzled transmembrane Wnt receptors (FZD_1–10), a wide variety of cellular Wnt signaling responses can be elicited, the selectivity of which depends on the following: (1) the specific Wnt-FZD pairing, (2) the participation of Wnt co-receptors and (3) the cellular context. Co-receptors play a pivotal role in guiding the specificity of Wnt signaling, most notably between β-catenin-dependent and -independent pathways, where co-receptors such as LRP5/6 and ROR1/2/PTK7 play major roles, respectively. It remains less understood how specific Wnt/FZD combinations contribute to the selectivity of downstream Wnt signaling, and we lack accurate comparative data on their binding properties under physiological conditions. Here, using fluorescently tagged Wnt3a, Wnt5a and Wnt16 proteins and cell lines expressing HiBiT-tagged Frizzled, we build on our ongoing efforts to provide a complete overview of the biophysical properties of all Wnt/FZD interactions using full-length proteins. Our real-time NanoBRET analysis using living cells expressing low receptor levels provides more accurate quantification of binding and will help us understand how these binary engagements control Wnt signaling outputs. We also provide evidence that LRP6 regulates the binding affinity of Wnt/FZD interactions in the trimeric Wnt-FZD-LRP6 complex.

Alex Ferreira da Silva, Franciele Jesus Lima, Alyne Riani Moreira et al.

Aberrant Rho-associated kinase function could be associated with increased bone fragility. Since cigarette smoke (CS) exposure promotes the increase in bone fragility due to changes in bone tissue components, this study aimed to investigate how CS exposure could modulate the Rho kinase-associated bone structural changes. Mice were assigned to four groups: control; smoke; control with Rho kinase inhibitor administration; and smoke with a Rho kinase inhibitor. Bone samples were obtained to assess bone histomorphometry analysis, type I collagen composition, and MEPE expression in trabeculae. We observed that CS exposure induced decreased trabecular and osteoid thickness. A concomitant increase in the osteoclastic and erosion surfaces and a decrease in the mineralization surface were observed. Additionally, CS exposure decreased the type I collagen and MEPE expression. Rho kinase inhibitor administration recovered the bone mineralization and the collagen type I deposition. Conclusions: CS exposure increases Rho kinase activity in bone cells, leading to structural changes. The administration of a Rho GTPases inhibitor partially reverses these effects, likely due to the recovery in osteoblast activity.

Min Liu, Yuanyuan Li, Zhilin Deng et al.

Abstract Mutation in genes involved in DNA replication continuously disrupt DNA replication and give rise to genomic instability, a critical driver of oncogenesis. To prevent leukemia, immature T lymphocytes with genomic instability often undergo rapid cell death during development. However, the mechanism by which immature T lymphocytes undergo rapid cell death upon genomic instability has been enigmatic. Here we show that zebrafish mcm5 mutation leads to DNA damage in immature T lymphocytes and the immature T cells sensitively undergo rapid cell death. Detailed analyses demonstrated that the immature T lymphocytes undergo rapid apoptosis via upregulation of tp53 and downregulation of bcl2 transcription in mcm5 mutants. Mechanistically, Mcm5 directly binds to Stat1a and facilitates its phosphorylation to enhance bcl2a expression under the conditions of DNA replication stress. However, in mcm5 mutants, the absence of the Mcm5-Stat1 complex decreases Stat1 phosphorylation and subsequent bcl2a transcription, accelerating apoptosis of immature T lymphocytes with genomic instability. Furthermore, our study shows that the role of Mcm5 in T-cell development is conserved in mice. In conclusion, our work identifies a role of Mcm5 in regulating T cell development via Stat1-Bcl2 cascade besides its role in DNA replication, providing a kind of mechanism by which immature T cells with gene mutation-induced DNA damage are rapidly cleared during T lymphocyte development.