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PubMed2026

Digital pathology, image analysis, and artificial intelligence in liver disease.

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.

Journal Article↗

PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗

PubMed2025

Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.

With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a "lack of information." In cancer research and clinical practice, a source of information that is broadly available and highly cost-effective comes from pathological images, which are routinely collected for definitive diagnosis and staging. In this article, we consider a Bayesian approach for selecting relevant genetic variables and modeling their relationships with a cancer outcome/phenotype. We propose borrowing information from (manually curated, low-dimensional) pathological imaging features via reinforcing the same selection results for the cancer outcome and imaging features. We further develop a weighting strategy to accommodate the scenario where information borrowing may not be equally effective for all subjects. Computation is carefully examined. Simulations demonstrate competitive performance of the proposed approach. We analyze TCGA (The Cancer Genome Atlas) LUAD (lung adenocarcinoma) data, with overall survival and gene expressions being the outcome and genetic variables, respectively. Findings different from the alternatives and with sound properties are made.

Humans↗

PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗

PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗

PubMed2026

Impact of spatial distribution of M2 macrophages on prognosis and neoadjuvant chemotherapy resistance in gastric cancer.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is a crucial treatment for locally advanced gastric cancer; however, approximately 30-40% of patients experience primary resistance, the mechanisms of which urgently require elucidation. The tumor microenvironment exhibits a high degree of spatial heterogeneity. M2 macrophages, as critical immune cells within this environment, are typically associated with poor prognosis. Yet, whether their spatial distribution impacts chemotherapy efficacy remains unclear. This study aims to investigate the relationship between the in situ spatial distribution characteristics of M2 macrophages and chemoresistance in gastric cancer. METHODS: Based on The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort, the association between M2 markers (CD163, MRC1) and histological grade as well as overall survival (OS) was evaluated. Spearman correlation and functional enrichment analyses were conducted to explore the mechanistic link between M2 macrophages and stromal barrier construction. Multiplex immunofluorescence (mIF) and digital pathology image analysis were utilized to calculate the areal density of M2 macrophages in the intratumoral core and the peritumoral stroma, respectively. The tumor-to-peritumoral ratio (TPR) was constructed, followed by a rank correlation analysis between TPR and the tumor regression grade (TRG). RESULTS: TCGA-STAD results confirmed that patients with high expression of M2 markers had worse OS (P=0.03), and the expression levels of M2 markers increased with histological grade. MRC1 was highly significantly and positively correlated with the pro-fibrotic factor TGFB1 (rho=0.447, P<0.001), with the gene set significantly enriched in pathways such as positive regulation of cytokine production and myeloid leukocyte activation. Histological examination revealed that in chemoresistant patients (TRG 3), M2 macrophages were primarily retained in the peritumoral stroma, with a median TPR of 0.50; in chemosensitive patients (TRG 1-2), a massive influx of M2 macrophages into the tumor core was observed, with a median TPR of 6.67. TPR was negatively correlated with TRG (rs=-0.65, P=0.043). CONCLUSIONS: The clinical impact of M2 macrophages in the gastric cancer microenvironment is highly dependent on their spatial distribution. The peritumoral-enriched pattern (TPR <1) mediates primary chemoresistance, whereas high infiltration in the core objectively reflects the pathological footprint following effective chemotherapy. The TPR serves as a novel tool for assessing neoadjuvant chemosensitivity in gastric cancer.

Gastric cancer (GC)↗

PubMed2026

Biomarker-guided selection of intravesical therapy in high-risk non-muscle invasive bladder cancer: A contemporary review.

High-risk non-muscle invasive bladder cancer poses therapeutic challenges, with significant rates of recurrence and progression with standard intravesical bacillus Calmette-Guérin (BCG) therapy. Current surveillance strategies lack accurate risk stratification models to predict individual treatment response and personalized treatment options. Simultaneously, there are no well-validated alternatives to replace the current gold-standard approach based on clinical and pathologic features. This review examines emerging biomarkers and advanced technologies with the potential to enhance patient selection and personalize intravesical therapy in HR-NMIBC. Artificial intelligence(AI)-driven histopathologic tools, such as the computer histological AI biomarker, have demonstrated the ability to identify non-responders to standard therapy using whole-slide digital pathology images. In parallel, radiomics-enhanced imaging has shown promise in assessing tumor biology and immune microenvironment features predictive of BCG responsiveness. Liquid biopsy, especially urine tumor DNA analysis, is now available in the arsenal to detect minimal residual disease, stratify recurrence risk, and predict treatment response even before clinical or radiographic evidence of recurrence. Tissue-based genomic profiling has also revealed molecular alterations associated with treatment resistance, though additional validation is needed. Together, these next-generation biomarkers may represent a pivotal shift toward precision oncology in bladder cancer and their incorporation into NMIBC future clinical guidelines is both anticipated and necessary.

BCG-unresponsive disease↗

PubMed2026

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.

BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improving patient outcomes. Although various predictive models have been developed, most rely on complex genomic analyses and high-dimensional clinical data. The complexity of these approaches substantially limits their feasibility for routine clinical use. To address this clinical challenge, this study aims to predict postoperative recurrence using routinely available hematoxylin and eosin (H&E)-stained images and characterize the associated biological features. METHODS: A total of 329 patients who underwent curative resection at the First Affiliated Hospital of Wenzhou Medical University (FHWMU) were retrospectively enrolled and randomly assigned to training and internal validation cohorts in a 7:3 ratio. An independent external validation cohort comprising 70 patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was included. Three patch-level feature extractors (Inception_V3, ResNet18, and DenseNet121) were evaluated within a weakly supervised multiple-instance learning (MIL) framework incorporating automated region-of-interest (ROI) detection on segmented whole-slide images (WSIs). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Kaplan-Meier (KM) survival analysis, and multivariable Cox proportional hazards regression. Transcriptomic profiling and gene set enrichment analysis (GSEA) were conducted to investigate biological differences between risk groups. RESULTS: The model achieved AUCs of 0.923 in the training cohort, 0.891 in the internal validation cohort, and 0.847 in the external validation cohort. The model effectively stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) across all cohorts (all P < 0.001) and retained prognostic value within AJCC stages I-III. Transcriptomic analyses revealed consistent enrichment of cell cycle-related pathways and neutrophil extracellular trap (NET) formation in high-risk patients across both institutional and CPTAC cohorts, aligning with distinct biological profiles of the model-derived risk stratification. CONCLUSIONS: This weakly supervised deep learning framework enables accurate and externally validated prediction of postoperative recurrence in LUAD using routinely available histopathological images, and integration of histopathological features with molecular analyses enhances biological interpretability. This work provides a clinically accessible and cost-effective tool for postoperative risk assessment in LUAD patients.

Humans↗

PubMed2026

Serum Olink Proteomics Reveals Novel Biomarkers for Early Diagnosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly prevalent malignant tumor in China, and early diagnosis critically affects the prognosis. Current imaging and pathological biopsy techniques have limitations, including high invasiveness and limited accessibility, while the insufficient sensitivity of serum biomarkers (such as AFP) restricts their use in early screening. In this study, using the Olink proteomics platform based on the proximity extension assay (PEA), we screened for hepatocellular carcinoma-related differentially expressed proteins (DEPs) and constructed a multiprotein diagnostic model. In the discovery cohort, we included 15 patients with newly diagnosed HCCs and 16 healthy controls. DEPs were identified using Olink, and their diagnostic performance was analyzed to identify the candidate biomarkers. In an independent validation cohort, including 116 HCC patients (50 early stage, 66 late stage) and 83 healthy controls, we further validated the expression levels and diagnostic performance of identified proteins─C1QA and GFER. The C1QA and GFER expression levels were significantly higher in the serum of patients with early and late HCC stages compared to healthy controls. By constructing a multiprotein diagnostic model, we identified C1QA, GFER, and AFP as the optimal diagnostic combination, demonstrating a combined diagnostic AUC of 0.92 and 0.99 for early-stage and advanced-stage HCC, respectively.

Humans↗

PubMed2026

IgA Vasculitis with necrotizing arteritis: a multicenter retrospective study from the French Vasculitis Study Group and systematic review of the literature.

IgA vasculitis (IgAV) primarily affects small vessels, but rare cases with necrotizing arteritis (NA) raise questions about overlap with polyarteritis nodosa (PAN). To characterize IgAV with necrotizing arteritis (IgAV-NA) and compare its phenotype with classical IgAV and PAN. We performed a multicenter retrospective study combined with a systematic literature review (1990-2025). Patients fulfilled EULAR/PRINTO/PRES IgAV criteria, had pathological or imaging evidence of NA in small or medium arteries, and were ANCA-negative. Thirty patients were included (7 from databases, 23 from the literature). NA was confirmed by biopsy (n = 16) or vascular imaging (n = 14). Clinical features, treatments, remission, and mortality were compared with 257 adult IgAV and 196 PAN patients. Median age was 54.5 years. IgAV-NA was characterized by severe manifestations, including gastrointestinal bleeding, perforation, surgical abdomen, neuropathy, pancreatitis, and livedo. Compared with classical IgAV, IgAV-NA showed significantly higher rates of multi-organ involvement and mortality. Compared with PAN, IgAV-NA shared vascular complications but had less fever and neuropathy. Despite arterial involvement, patients did not fulfil PAN criteria. IgAV-NA represents a rare, severe IgAV phenotype with life-threatening complications rather than an IgAV-PAN overlap. Severe or atypical IgAV presentations should prompt vascular imaging and intensified immunosuppression.

Humans↗

PubMed2026

A compound heterozygous combination of SLC34A2 variants in pulmonary alveolar microlithiasis: A case report and literature review.

Pulmonary Alveolar Microlithiasis (PAM) is a rare hereditary lung disorder characterized by the intra-alveolar deposition of calcium phosphate microliths. It is primarily familial and follows an autosomal recessive inheritance pattern, with no significant gender disparity in incidence. In its early stages, PAM is often asymptomatic, and most cases are detected incidentally through abnormal imaging findings during routine health examinations. We report a case of a male patient in his mid-50 s with a 10-year history of exertional dyspnea and cough unresponsive to conventional therapy. Initially diagnosed and treated for emphysema in early 2024, the patient was readmitted two months later with progressive dyspnea and cyanosis. The diagnosis of PAM was confirmed by typical medical imaging and pathological examination. Genetic analysis identified a previously unreported compound heterozygous combination of SLC34A2 variants: a c.910A > T (p.Lys304*) nonsense variant in exon 8 and a heterozygous ∼5.5 kb copy-number deletion at 4p15.2 (encompassing exons 2-6), thereby expanding the catalogue of reported PAM-associated genetic combinations. No recurrence was observed during one-year follow-up after bilateral lung transplantation.

Humans↗

PubMed2025

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Objective. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.Approach. We propose a multimodal transformer framework for breast cancer survival prediction using HER2 stratified (SurvMBC), a foundation model-enhanced architecture that fuses three data modalities: whole-slide images, clinical narratives, and molecular features. Tumor microenvironment features are extracted using a pathology language and image pre-training (PLIP), clinical narratives are processed with BioBERT, and miRNA expression plus DNA methylation data are embedded using Gen2Vec. These representations are integrated through a cross-modal transformer with attention mechanisms for survival prediction.Main results. The model was evaluated on 1,095 HER2-positive breast cancer patients from The Cancer Genome Atlas. SurvMBC achieved a concordance index (C-index) of 0.857 (95% CI: 0.834, 0.880), a low integrated Brier score, and a strong inverse negative binomial log-likelihood. Risk stratification based on model outputs significantly separated high- and low-risk groups (log-rankp< 0.01) and showed strong associations with tumor stage, grade, and hormone receptor status (allp< 0.05).Significance. SurvMBC demonstrates the effectiveness of multimodal fusion in addressing tumor heterogeneity and improving prognostic accuracy. The attention-based integration enables context-aware learning of survival-relevant features across modalities, supporting individualized risk stratification and risk-adaptive treatment planning for HER2 stratified breast cancer patients.

Breast Neoplasms↗

PubMed2026

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗

PubMed2026

The use of 3-dimensional (3D) printing in teaching musculoskeletal oncology for medical undergraduates.

INTRODUCTION: The approach to integrating relevant anatomy in the medical curriculum has been debated for many years. Current literature has explored the broad impact of 3D printing in medical education. However, there is little evidence on 3D printing for the teaching of musculoskeletal oncology (MSO). This is a self-controlled case series (SCCS) study that aims to analyse the effectiveness of 3D printed models in MSO in enhancing the learning experience, engagement and understanding of clinical and surgical anatomy for medical students. METHOD: A cross-sectional cohort study involving 75 clinical year medical students across 3 years from 2 medical schools that rotated through a single teaching hospital's orthopaedic department. Participants first viewed a set of computed-tomography (CT) images of a large pelvic osteosarcoma from a free open-source database, the Cancer Genome Atlas Sarcoma Collection (TCGA-SARC). A standardised 10-minute pre-intervention questionnaire which comprised 15 questions categorised by: 4 questions in 'anatomical knowledge', 7 questions in 'spatial awareness', 4 questions in 'surgical planning and complications', was administered to assess the baseline knowledge in their interpretation of the pathology via CT images only. Next, a 3D-printed model of the pelvic osteosarcoma, which included colour-coded adjacent structures, was provided as an adjunct to answer the same questionnaire. This concluded with a 5-point Likert scale feedback survey to gauge their perspectives and experience. RESULTS: The mean scores comparing their pre- and post-intervention assessment questionnaire increased by +1.34 from 8.15 (SD = 1.85) to 9.49 (SD = 1.7) (p < 0.001). The final year students had the greatest improvement of +1.54 from 8.00 (SD = 1.89) to 9.54 (SD = 1.59) (p = 0.004). There was no significant difference between the scores amongst the 2 medical schools. 91% of students agreed that the 3D model had helped them further their understanding of the anatomy of the sarcoma and 87% would want 3D printing models to augment their learning in anatomy. Baseline weaker students demonstrated significantly greater improvement in scores compared with baseline stronger students (mean difference +2.04 vs +0.30, p < 0.001). CONCLUSION: 3D printing is an effective teaching adjunct for musculoskeletal oncology surgical anatomy for medical undergraduates and could be used to enhance their understanding and learning experience. 3D models could be integrated in the teaching curriculum of surgical anatomy for undergraduate students.

Humans↗

PubMed2026

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.

Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.

Artificial intelligence↗

PubMed2026

Invasive mucinous adenocarcinoma of the lung: integrating molecular landscape, imaging phenotypes, and translational therapeutic strategies.

Invasive mucinous adenocarcinoma (IMA) of the lung is an uncommon but clinically important subtype of lung adenocarcinoma with distinctive radiologic, histopathologic, and molecular features. Its indolent symptoms, mucin-rich growth pattern, and frequent pneumonia-like or multifocal presentation can obscure early diagnosis and complicate distinction from infection, synchronous primary tumors, and intrapulmonary spread. This review integrates current evidence on the clinical course, imaging phenotypes, diagnostic workflow, histopathologic features, molecular alterations, tumor immune microenvironment, and treatment response patterns of IMA. Emphasis is placed on the relationship between radiologic appearance and underlying mucinous pathology, the clinical significance of spread through air spaces (STAS), and the need for adequate tissue sampling and comprehensive molecular profiling. Compared with non-mucinous lung adenocarcinoma, IMA is enriched for KRAS mutations and selected fusion or receptor alterations, whereas canonical EGFR mutations are less frequent. These biological differences help explain why treatment strategies extrapolated from broader non-small cell lung cancer (NSCLC) populations may be insufficient, particularly for multifocal, pneumonic-type, or advanced disease. Although surgery can provide favorable outcomes in localized disease, systemic therapy remains challenging, and the role of immunotherapy requires further clarification. Future progress will depend on integrated imaging-pathology-genomic models, prospective IMA-specific cohorts, and translational studies aimed at refining classification and developing individualized therapeutic strategies.

Invasive mucinous adenocarcinoma (IMA)↗

PubMed2026

Diagnostic Challenge: Angiomatoid Fibrous Histiocytoma with EWSR1 Rearrangement.

This image report aims to illustrate the diagnostic value of integrating various diagnostic modalities to distinguish angiomatoid fibrous histiocytoma (AFH) from malignant soft tissue tumors. A 33-year-old man presented with a painful palpable mass in the medial left thigh. Magnetic resonance imaging revealed a 27 × 23 × 20 mm intramuscular lesion in the vastus medialis with a mixed T1 signal, predominantly high T2/STIR signal, internal hyperintense foci, a capsule-like rim, septations, heterogeneous enhancement, and marked peritumoral edema extending beyond the apparent tumor margins. Because these findings raised concern for a malignant soft tissue tumor, wide excision was performed after biopsy suggestive of low-grade sarcoma. Histologically, the tumor comprised spindle to epithelioid cells arranged in fascicular and storiform patterns surrounded by a lymphoid cuff. Immunohistochemistry showed positivity for CD68, CD99, and EMA, partial desmin and S-100 expression, and a Ki-67 index of 15%. Differential diagnosis included AFH and a malignant peripheral nerve sheath tumor. FISH demonstrated EWSR1 rearrangement, while PCR testing for EWSR1-CREB1 and EWSR1-ATF1 was negative. Although the specific fusion partner could not be identified, an EWSR1 rearrangement, together with the histologic and immunohistochemical findings, was considered supportive but not definitive for AFH. AFH, a rare intermediate tumor with nonspecific imaging features and variable pathology, makes diagnosis challenging. This case emphasizes the need to consider AFH in the diagnosis of intramuscular tumors with disproportionate peritumoral edema and highlights the role of molecular analysis in confirming the diagnosis.

angiomatoid fibrous histiocytoma↗

PubMed2025

The Consortium for Clarity in ADRD Research Through Imaging (CLARiTI): Overview of consortium sites and anticipated enrollment.

INTRODUCTION: The Consortium for Clarity in Alzheimer's disease related dementias (ADRD) Research Through Imaging (CLARiTI) is a study that aims to collect standardized imaging and plasma biomarkers on 2000 Clinical Core participants enrolled across all Alzheimer's Disease Research Centers (ADRC) sites. We sought to summarize the known heterogeneity across centers regarding scientific focus and initial enrollment plans for CLARiTI. METHODS: We developed and distributed a survey capturing information on the 36 CLARiTI site's theme/expertise, recruitment plans, and the intersection of CLARiTI with other ADRC imaging efforts. RESULTS: Anticipated CLARiTI enrollees spanned 11 different categories of suspected etiologies underlying impairment. A wide range of risk factors were endorsed across sites regarding the enrollment of unimpaired individuals. Variability also existed regarding site-level strategies in enrollment into CLARiTI versus other imaging efforts. DISCUSSION: We anticipate that the 2000 individuals that will enroll into CLARiTI will reflect the clinical heterogeneity already in place across the ADRC network. HIGHLIGHTS: The ADRC Consortium for Clarity in ADRD Research Through Imaging (CLARiTI) will leverage and contribute to the existing Alzheimer's Disease Research Centers (ADRC) program by supporting standardized imaging and plasma collection across all centers. We summarize the variation in scientific focus and enrollment plans across ADRC sites participating in CLARiTI. The anticipated CLARiTI cohort will reflect the clinical heterogeneity that already exists across the ADRC network. CLARiTI will contribute to scientific goals related to the detection of multi-etiological signatures relevant for Alzheimer's disease and related disorders (ADRDs).

Humans↗