Hasil untuk "Pathology"

R. Lukes, J. Butler

B. Mcsherry

S. Sternberg, D. Antonioli

K. Jellinger

Xurong Liu, Jipang Zhan, Jingwen Zou et al.

Abstract Introduction Surgical excision is the standard treatment for basal cell carcinoma (BCC). For locally advanced BCC (laBCC) not suitable for surgery or radiotherapy, Hedgehog pathway inhibitors (HHIs) such as sonidegib are important options. Clinical observations have shown that sonidegib may lead to pigmentation and scarring, which can affect treatment evaluation. We evaluated the efficacy and safety of sonidegib in Chinese patients with laBCC and examined discrepancies between clinical/dermoscopic assessments and pathological findings, including posttreatment pathological changes. Methods This single-center retrospective study included 54 patients with laBCC treated with sonidegib 200 mg/day for ≥ 3 months (October 2022–July 2025). Response assessment integrated VISIA-based planimetric lesion-area regression, standardized dermoscopy, and dermoscopy-guided multi-site biopsy as the pathological gold standard. The primary endpoint was objective response rate (ORR); secondary endpoints included disease control rate (DCR) and safety. Results At 3 months, ORR was 87% (complete response [CR] 48%; partial response [PR] 39%), and DCR was 100%. Pathology showed complete clearance in 48.1% and residual tumor in 51.9%, with six cases showing apparent histologic subtype shifts. Dermoscopy in patients with complete remission still demonstrated a high false-positive rate (branching blood vessels 53.8%, blue-gray dots 61.5%), leading to decreased diagnostic specificity. Adverse events occurred in 81.5% of patients; 70.4% reported multiple events, most commonly muscle cramps (66.7%), dysgeusia (59.3%), and alopecia (55.6%). All events were grade 1–3, and no patient discontinued treatment as a result of toxicity. Conclusion In this real-world Chinese laBCC cohort, sonidegib produced a clinically meaningful response with a favorable safety profile. However, clinical and dermoscopic assessments showed substantial false positives due to posttreatment changes; pathological biopsy remains essential to confirm tumor clearance. Advanced noninvasive imaging (e.g., reflectance confocal microscopy) may further improve monitoring. Prospective studies with longer follow-up are warranted.

Hikmat Khan, Usama Sajjad, Metin N. Gurcan et al.

Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet existing pathology foundation models often overlook organ-specific features critical for CRC prognostication. Methods: We propose MorphDistill, a two-stage framework that distills complementary knowledge from multiple pathology foundation models into a compact CRC-specific encoder. In Stage I, a student encoder is trained using dimension-agnostic multi-teacher relational distillation with supervised contrastive regularization on large-scale colorectal datasets. This preserves inter-sample relationships from ten foundation models without explicit feature alignment. In Stage II, the encoder extracts patch-level features from whole-slide images, which are aggregated via attention-based multiple instance learning to predict five-year survival. Results: On the Alliance/CALGB 89803 cohort (n=424, stage III CRC), MorphDistill achieves an AUC of 0.68 (SD 0.08), an approximately 8% relative improvement over the strongest baseline (AUC 0.63). It also attains a C-index of 0.661 and a hazard ratio of 2.52 (95% CI: 1.73-3.65), outperforming all baselines. On an external TCGA cohort (n=562), it achieves a C-index of 0.628, demonstrating strong generalization across datasets and robustness across clinical subgroups. Conclusion: MorphDistill enables task-specific representation learning by integrating knowledge from multiple foundation models into a unified encoder. This approach provides an efficient strategy for prognostic modeling in computational pathology, with potential for broader oncology applications. Further validation across additional cohorts and disease stages is warranted.

Marco Gustav, Fabian Wolf, Christina Glasner et al.

The rapid adoption of transformer-based models in computational pathology has enabled prediction of molecular and clinical biomarkers from H&E whole-slide images, yet interpretability has not kept pace with model complexity. While attribution- and generative-based methods are common, feature visualization approaches such as class visualizations (CVs) and activation atlases (AAs) have not been systematically evaluated for these models. We developed a visualization framework and assessed CVs and AAs for a transformer-based foundation model across tissue and multi-organ cancer classification tasks with increasing label granularity. Four pathologists annotated real and generated images to quantify inter-observer agreement, complemented by attribution and similarity metrics. CVs preserved recognizability for morphologically distinct tissues but showed reduced separability for overlapping cancer subclasses. In tissue classification, agreement decreased from Fleiss k = 0.75 (scans) to k = 0.31 (CVs), with similar trends in cancer subclass tasks. AAs revealed layer-dependent organization: coarse tissue-level concepts formed coherent regions, whereas finer subclasses exhibited dispersion and overlap. Agreement was moderate for tissue classification (k = 0.58), high for coarse cancer groupings (k = 0.82), and low at subclass level (k = 0.11). Atlas separability closely tracked expert agreement on real images, indicating that representational ambiguity reflects intrinsic pathological complexity. Attribution-based metrics approximated expert variability in low-complexity settings, whereas perceptual and distributional metrics showed limited alignment. Overall, concept-level feature visualization reveals structured morphological manifolds in transformer-based pathology models and provides a framework for expert-centered interrogation of learned representations across label granularities.

Tengyue Zhang, Ruiwen Ding, Luoting Zhuang et al.

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches either fail to leverage unlabeled data from the target domain or rely on image-to-image translation, which can distort tissue structures and compromise model accuracy. In this work, we propose a semi-supervised domain adaptation (SSDA) framework that utilizes a latent diffusion model trained on unlabeled data from both the source and target domains to generate morphology-preserving and target-aware synthetic images. By conditioning the diffusion model on foundation model features, cohort identity, and tissue preparation method, we preserve tissue structure in the source domain while introducing target-domain appearance characteristics. The target-aware synthetic images, combined with real, labeled images from the source cohort, are subsequently used to train a downstream classifier, which is then tested on the target cohort. The effectiveness of the proposed SSDA framework is demonstrated on the task of lung adenocarcinoma prognostication. The proposed augmentation yielded substantially better performance on the held-out test set from the target cohort, without degrading source-cohort performance. The approach improved the weighted F1 score on the target-cohort held-out test set from 0.611 to 0.706 and the macro F1 score from 0.641 to 0.716. Our results demonstrate that target-aware diffusion-based synthetic data augmentation provides a promising and effective approach for improving domain generalization in computational pathology.

Songhan Jiang, Fengchun Liu, Ziyue Wang et al.

Vision-Language Models (VLMs) are advancing computational pathology with superior visual understanding capabilities. However, current systems often reduce diagnosis to directly output conclusions without verifiable evidence-linked reasoning, which severely limits clinical trust and hinders expert error rectification. To address these barriers, we construct PathReasoner, the first large-scale dataset of whole-slide image (WSI) reasoning. Unlike previous work reliant on unverified distillation, we develop a rigorous knowledge-guided generation pipeline. By leveraging medical knowledge graphs, we explicitly align structured pathological findings and clinical reasoning with diagnoses, generating over 20K high-quality instructional samples. Based on the database, we propose PathReasoner-R1, which synergizes trajectory-masked supervised fine-tuning with reasoning-oriented reinforcement learning to instill structured chain-of-thought capabilities. To ensure medical rigor, we engineer a knowledge-aware multi-granular reward function incorporating an Entity Reward mechanism strictly aligned with knowledge graphs. This effectively guides the model to optimize for logical consistency rather than mere outcome matching, thereby enhancing robustness. Extensive experiments demonstrate that PathReasoner-R1 achieves state-of-the-art performance on both PathReasoner and public benchmarks across various image scales, equipping pathology models with transparent, clinically grounded reasoning capabilities. Dataset and code are available at https://github.com/cyclexfy/PathReasoner-R1.

P. Goldman-Rakic, L. Selemon

D.V. Dmytriiev, P.A. Borozenets, Yu.V. Oleshko et al.

Due to active hostilities, the number of patients experiencing phantom pain has sharply increased nowadays. In case of limb amputation, 50 % of patients experience phantom pain, and about 70 % report phantom sensations. This issue is extremely relevant and insufficiently studied in modern medicine. Only a few medical institutions provide adequate management of chronic pain syndrome (including phantom pain). It is also worth noting that untreated phantom pain makes the use of prosthesis impossible, which, in turn, nullifies the potential for complete socialization and adaptation of the patient, thereby increasing the burden not only on the medical system but also on social services. The use of neuraxial analgesia methods has proven to be an effective treatment for this pathology; however, the short duration of effect encourages further exploration and research. This case report highlights the combination of neuraxial methods with the use of botulinum toxin type A for the treatment of phantom pain in patients with traumatic limb amputations. Given the limited number of relevant studies and the small sample size regarding the use of botulinum toxin type A, we would like to present our own clinical case with a positive outcome.

Sang Lin, Sheng Tan, Yonglin Peng et al.

Abstract Neurotransmitter serotonin (5-hydroxytryptamine [5-HT]) has emerged to play parallel roles in both neurobiology and oncology. Apart from receptor-mediated signaling transduction pattern, serotonin can be covalently integrated into histone (the post-translational modification known as histone serotonylation) and serve as an epigenetic mark associated with permissive gene expression. However, how histone serotonylation influences tumorigenesis is yet to be understood. In this study, we observe the higher levels of histone serotonylation (H3K4me3Q5ser) and transglutaminases 2 (TGM2, the enzyme catalyzing serotonylation) in both pancreatic ductal adenocarcinoma (PDAC) tissues and cell lines in comparison with their normal counterparts, and inhibition of histone serotonylation suppresses PDAC development. Mechanistically, we demonstrate that TGM2-mediated histone serotonylation at promoter of the gene encoding stearoyl-CoA desaturase (SCD) up-regulates its expression and drives PDAC development by lipid metabolism remodeling. Collectively, this study reveals histone serotonylation as an important driver of PDAC tumorigenesis.

Jianyu Wu, Hao Yang, Xinhua Zeng et al.

The diagnosis of pathological images is often limited by expert availability and regional disparities, highlighting the importance of automated diagnosis using Vision-Language Models (VLMs). Traditional multimodal models typically emphasize outcomes over the reasoning process, compromising the reliability of clinical decisions. To address the weak reasoning abilities and lack of supervised processes in pathological VLMs, we have innovatively proposed PathVLM-R1, a visual language model designed specifically for pathological images. We have based our model on Qwen2.5-VL-7B-Instruct and enhanced its performance for pathological tasks through meticulously designed post-training strategies. Firstly, we conduct supervised fine-tuning guided by pathological data to imbue the model with foundational pathological knowledge, forming a new pathological base model. Subsequently, we introduce Group Relative Policy Optimization (GRPO) and propose a dual reward-driven reinforcement learning optimization, ensuring strict constraint on logical supervision of the reasoning process and accuracy of results via cross-modal process reward and outcome accuracy reward. In the pathological image question-answering tasks, the testing results of PathVLM-R1 demonstrate a 14% improvement in accuracy compared to baseline methods, and it demonstrated superior performance compared to the Qwen2.5-VL-32B version despite having a significantly smaller parameter size. Furthermore, in out-domain data evaluation involving four medical imaging modalities: Computed Tomography (CT), dermoscopy, fundus photography, and Optical Coherence Tomography (OCT) images: PathVLM-R1's transfer performance improved by an average of 17.3% compared to traditional SFT methods. These results clearly indicate that PathVLM-R1 not only enhances accuracy but also possesses broad applicability and expansion potential.

Bargava Subramanian, Naveen Kumarasami, Praveen Shastry et al.

Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MRI spine scans sourced from diverse healthcare facilities across India. The AI system integrates advanced architectures, including Vision Transformers, U-Net with cross-attention, MedSAM, and Cascade R-CNN, enabling comprehensive classification, segmentation, and detection of 43 distinct spinal pathologies. The dataset is balanced across age groups, genders, and scanner manufacturers to ensure robustness and adaptability. Subgroup analyses were conducted to validate the model's performance across different patient demographics, imaging conditions, and equipment types. Performance: The AI system achieved up to 97.9 percent multi-pathology detection, demonstrating consistent performance across age, gender, and manufacturer subgroups. The normal vs. abnormal classification achieved 98.0 percent accuracy, and the system was deployed across 13 major healthcare enterprises in India, encompassing diagnostic centers, large hospitals, and government facilities. During deployment, it processed approximately 100,000 plus MRI spine scans, leading to reduced reporting times and increased diagnostic efficiency by automating the identification of common spinal conditions. Conclusion: The AI system's high precision and recall validate its capability as a reliable tool for autonomous normal/abnormal classification, pathology segmentation, and detection. Its scalability and adaptability address critical diagnostic gaps, optimize radiology workflows, and improve patient care across varied healthcare environments in India.

Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan et al.

The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification that leverages both spatial and semantic features across multiple stains. At its core, BioX-CPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis and Sjogren's Disease multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores, that permit measuring model alignment with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Source code and documentation can be found at: https://github.com/AmayaGS/BioX-CPath.

Chenjun Li, Cheng Wan, Laurin Lux et al.

Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across different modalities. However, training VLMs for detailed reasoning requires large-scale image-text datasets. In many specialized domains, for example in reading Optical Coherence Tomography Angiography (OCTA) images, such precise text with grounded description of pathologies is scarce or even non-existent. To overcome this bottleneck, we introduce Synthetic Vasculature Reasoning (SVR), a framework that controllably synthesizes images and corresponding text, specifically: realistic retinal vasculature with Diabetic Retinopathy (DR) features: capillary dropout, microaneurysms, neovascularization, and tortuosity, while automatically generating granular reasoning texts. Based on this we curate OCTA-100K-SVR, an OCTA image-reasoning dataset with 100,000 pairs. Our experiments show that a general-purpose VLM (Qwen3-VL-8b) trained on the dataset achieves a zero-shot balanced classification accuracy of 89.67% on real OCTA images, outperforming supervised baselines. Through human expert evaluation we also demonstrate that it significantly enhances explanation quality and pathology localization on clinical data.

P. Hall, D. Lane

O. G. Sarkisyan, V. A. Razdorov, E. V. Andreev et al.

Diabetic foot syndrome (DFS) is a dangerous complication of diabetes mellitus. Despite numerous studies dedicated to the wound healing process in patients with diabetic foot syndrome, surgeries in this pathology are often accompanied by surgical suture failure due to insulin therapy and require repeat surgical intervention. The aim of this study is to analyze the biochemical mechanisms involved in the wound healing process in patients with diabetic foot syndrome. To achieve this goal, articles from foreign databases such as PubMed, MedLine, Google Scholar, and the Russian Index of Scientific Citation (RISC) were selected and analyzed for the period from 2017 to 2023. The search was conducted using keywords such as diabetic foot, wound healing, molecular mechanisms, and their Russian equivalents. A total of 74 publications were identified through the literature search, of which 24 literature sources from 2017 to 2023 were included in the review, corresponding to the direction and purpose of the study. In addition, 18 sources older than 2017 were used to reveal the subject of the study from the references in the literature lists. The literature review discusses various factors that influence the wound healing process: the function of the skin barrier, activity of immune system components, as well as the contribution of hypoxia and endothelial dysfunction to tissue regeneration mechanisms in patients with DFS. Despite the available literature data, it is advisable to search for new factors involved in the development mechanisms of DFS to prevent complications and increase the effectiveness of treatment.

Ruka Setoguchi, Tomoya Sengiku, Hiroki Kono et al.

Abstract The mechanisms by which the number of memory CD8 T cells is stably maintained remains incompletely understood. It has been postulated that maintaining them requires help from CD4 T cells, because adoptively transferred memory CD8 T cells persist poorly in MHC class II (MHCII)-deficient mice. Here we show that chronic interferon-γ signals, not CD4 T cell-deficiency, are responsible for their attrition in MHCII-deficient environments. Excess IFN-γ is produced primarily by endogenous colonic CD8 T cells in MHCII-deficient mice. IFN-γ neutralization restores the number of memory CD8 T cells in MHCII-deficient mice, whereas repeated IFN-γ administration or transduction of a gain-of-function STAT1 mutant reduces their number in wild-type mice. CD127high memory cells proliferate actively in response to IFN-γ signals, but are more susceptible to attrition than CD127low terminally differentiated effector memory cells. Furthermore, single-cell RNA-sequencing of memory CD8 T cells reveals proliferating cells that resemble short-lived, terminal effector cells and documents global downregulation of gene signatures of long-lived memory cells in MHCII-deficient environments. We propose that chronic IFN-γ signals deplete memory CD8 T cells by compromising their long-term survival and by diverting self-renewing CD127high cells toward terminal differentiation.

Cristina Dorina Pârvănescu, Andreea Lili Bărbulescu, Cristina Elena Biță et al.

The accurate diagnosis of gout frequently constitutes a challenge in clinical practice, as it bears a close resemblance to other rheumatologic conditions. An undelayed diagnosis and an early therapeutic intervention using uric acid lowering therapy (ULT) is of the utmost importance for preventing bone destruction, the main point of managing gout patients. Advanced and less invasive imaging techniques are employed to diagnose the pathology and ultrasonography (US) stands out as a non-invasive, widely accessible and easily reproducible method with high patient acceptability, enabling the evaluation of the full clinical spectrum in gout. The 2023 EULAR recommendations for imaging in diagnosis and management of crystal-induced arthropathies in clinical practice state that US is a fundamental imagistic modality. The guidelines underline its effectiveness in detecting crystal deposition, particularly for identifying tophi and the double contour sign (DCS). Its utility also arises in the early stages, consequent to synovitis detection. US measures of monosodium urate (MSU) deposits are valuable indicators, sensitive to change consequent to even short-term administration of ULT treatment, and can be feasibly used both in current daily practice and clinical trials. This paper aimed to provide an overview of the main US features observed in gout patients with reference to standardized imaging guidelines, as well as the clinical applicability both for diagnosis accuracy and treatment follow-up. Our research focused on summarizing the current knowledge on the topic, highlighting key data that emphasize gout as one of the few rheumatological conditions where US is recognized as a fundamental diagnostic and monitoring tool, as reflected in the most recent classification criteria.