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Abstract Background: Recent advances in computer vision have given rise to image foundation models (FMs), large-scale neural networks pre-trained on millions of unlabeled histopathology patches, which can learn generalizable representations of tissue morphology. In many cancers, these FMs have predicted genetic mutations and prognoses directly from histology, suggesting that morphology encodes “hidden” molecular information. In lymphoma, this field is nascent but growing.General-purpose histopathology FMs have limited representation of lymphoid morphology. We developed and evaluated LymphoVision, a lymphoma-specialized image foundation model pretrained with self-supervised learning, to extract clinically meaningful features from routine hematoxylin and eosin (H&E) histology. Methods: LymphoVision was pre-trained using knowledge distillation and consistency (DINOv2) on over 37 million multi-resolution patches (5x, 10x, 20x, 40x) sampled using a density-aware strategy from 31,211 H&E whole slide images (WSIs) from 9,155 archival lymphoma and reactive lymphoproliferative cases. To ensure diverse, representative samples, our patch sampling method uses an efficient graph-based clustering algorithm and prioritizes morphologically informative regions by clustering patch embeddings and performing density sampling within each cluster. The pretrained encoder was evaluated using a clustering-constrained attention multiple instance learning (MIL) framework on three diagnostic tasks: (1) cell-of-origin (COO) classification in diffuse large B-cell lymphoma (DLBCL), distinguishing germinal center B-cell (GCB) from non-GCB subtypes based on the Hans immunohistochemistry algorithm; (2) follicular lymphoma (FL) grading, differentiating low-grade (grades 1–2) from high-grade (grade 3); and (3) multiclass lymphoma subtype classification across six categories: DLBCL, FL, marginal zone lymphoma (MZL), mantle cell lymphoma (MCL), classical Hodgkin lymphoma (CHL), and benign/reactive lymphoid conditions. WSI-level labels were derived from pathology reports, and none of the evaluation cases were included in pretraining. Models used a ViT-Giant LymphoVision backbone to generate 20x patch embeddings, followed by MIL using 50% training data and 2-fold cross-validation (25% validation, 25% test). Performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, recall (sensitivity) and F1 score. Results: For DLBCL COO classification (total n=358 cases), LymphoVision achieved a mean test AUC of 0.93 (range 0.92–0.94), accuracy of 84.5% and F1 of 0.85 across two independent test folds (n=91 per fold, 50% GCB). These results outperformed general-purpose FMs on the same task and cases, including Virchow-v2 (AUC 0.85) and UNI-v2 (AUC 0.88), despite those models being pretrained on 10–100 times more images. For FL grading (total n=660 cases), the test set in each fold included 166 cases (18% grade 3), yielding a mean test AUC of 0.88 (range 0.87–0.90), accuracy of 89.2% and F1 of 0.80. In multiclass lymphoma subtype classification (total n=1,176 cases), the test set included 296 cases per fold. LymphoVision achieved a mean test AUC of 0.98 (range 0.97-0.99), mean test accuracy of 87.1% and mean F1 of 0.84. Class-wise recall was 92.0% for DLBCL (n=112), 88.2% for FL (n=72), 77.2% for MZL (n=29), 70.8% for MCL (n=35), 87.5% for CHL (n=24), and 95.8% for benign/reactive lymphoid conditions (n=24). Conclusion: LymphoVision is a lymphoma-specialized FM trained using self-supervision that achieves high diagnostic accuracy across multiple clinically meaningful tasks using weak supervision and standard H&E images, suggesting the encoder has learned fine-grained lymphoma morphology. Based on historical benchmarks from the 1997 International Lymphoma Study Group classification project, expert hematopathologists achieved diagnostic agreement rates of 55% to 84% using H&E morphology alone for the non-Hodgkin lymphoma (NHL) entities included in our multiclass task. LymphoVision approached or exceeded these benchmarks. These findings underscore the value of our approach using disease-specialized pretraining and intelligent patch sampling for data-efficient histopathology learning. LymphoVision has the potential to augment diagnostics, enable scalable AI-powered classification in real-world workflows, and serve as a foundation for prognostic and theragnostic applications. External validation and multimodal integration are ongoing.

Abstract Background: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

Abstract Introduction The LEO Comorbidity Index (LCI), comprising ten equally weighted comorbidity categories (respiratory, cardiovascular, digestive, hepatic, renal, autoimmune, diabetes, cancer history, HIV, and stroke), has demonstrated prognostic utility in patients with non-Hodgkin lymphomas. However, its predictive accuracy may vary with increasing age, due to age-related rises in comorbidity burden and physiological vulnerability. To investigate these dynamics, we evaluated the age-stratified predictive performance of LCI in patients with large B-cell lymphoma (LBCL), with a focused analysis on individuals aged 80+ years. We also examined which specific comorbidities most strongly influenced overall survival (OS) and developed a simplified, clinically applicable index tailored to this population. Methods We first included adults ≥40 years with newly diagnosed LBCL and recorded comorbidities from the LEO registry (USA, 2015–2020; n=1,652) to assess age-stratified predictive performance of LCI using the C-index. Next, we focused on patients 80+ years (n=263), pooled from the LEO cohort (n=147) and NiHiL registry (restricted to General Hospital, Prague, Czech Republic, 2010–2023; n=116). Comorbidities were collected from patient self-reports (LEO) and/or medical records (LEO/NiHiL). Univariate and multivariate Cox models were used. The primary endpoint was OS. Results Among the total LBCL population (n=1,652), LCI predictive performance improved by age category with C-index for patients <65 of 0.584, 65–79 years 0.579, and ≥80 years 0.647, indicating stronger discrimination in the 80+ group. Subsequent analysis focused on patients aged 80+ years (n=263; median age 83). Within this cohort, 34% had ECOG performance status (PS) 2–4, 66% stage III–IV disease, 59% elevated lactate dehydrogenase, 35% >1 extranodal site involved, and 58% International Prognostic Index (IPI) score of 3–5. A total of 377 comorbidities were reported, averaging 1.43 per patient (1.22 in LEO; 1.61 in NiHiL). LCI distribution was: 0 points in 24% (n=62), 1 point in 33% (n=88), and 2–5 points in 43% (n=113). Higher LCI scores had worse ECOG PS 2–4: 19% (LCI=0), 33% (LCI=1), and 43% (LCI ≥2; P=0.01), and inferior OS (median 6.9 years LCI=0, 3.0 years LCI=1, and 2.8 years LCI≥2, resp.; P=0.02). Presence of ≥1 comorbidity was associated with shorter OS compared to no comorbidities (HR=1.67, P=0.01). The LCI was an independent predictor of OS after adjusting for age (HR=1.18, P=0.01, C-index 0.623), IPI (HR=1.16, P=0.03, C-index 0.628) as well as for recently proposed SENIOR-IPI (HR=1.14, P=0.049, C-index 0.665). When evaluating individual comorbidities, heart disease (HR=1.54, P=0.01), stroke (HR=1.89, P=0.03), and recent malignancy (HR=1.99, P=0.02) were independent predictors of OS. Based on these findings, we developed a simplified LCI (sLCI) for use in 80+ patients incorporating only the three comorbidities (1 point each). Patients were categorized as sLCI: 0 (49%, n=129), 1 (44%, n=115), and 2–3 points (7%, n=19). sLCI scores 0, 1, 2–3 were associated with ECOG PS 2–4 27%, 37%, and 63%, respectively (P=0.01) and inferior OS: median 5.3 years (0), 2.5 years (1), and 1.4 years (2–3; P<0.01). sLCI remained an independent and strong predictor of OS after adjusting for age (HR=1.71, P<0.01, C-index 0.636), IPI (HR=1.66, P<0.01, C-index 0.635), and SENIOR-IPI (HR=1.60, P<0.04, C-index 0.659). The results were consistent in both NiHiL and LEO cohorts and remained significant among patients treated with anthracyclines (sLCI adjusted for age: HR=1.58, P<0.01, C-index 0.607; for IPI: HR=1.53, P=0.01, C-index 0.623; for SENIOR-IPI: HR=1.51, P=0.01, C-index 0.633). Conclusion Comorbidity burden becomes increasingly prognostic with higher age in LBCL patients. In the 80+ individuals, comorbidities – particularly heart disease, recent cancer, and stroke – have an independent impact on OS. While the original LCI captures the impact of broadly defined comorbidities, its prognostic utility attenuates in the 80+ patients, likely reflecting limited relevance of indolent chronic conditions in this population's constrained survival horizon. The sLCI, focused on the most prognostically relevant comorbidities, offers practical risk stratification. Incorporation of sLCI into clinical decision-making – alongside other prognostic indices – could better identify high-risk individuals and guide treatment strategies. Funding: NU21-03-00411, U01 CA195568.

Abstract Background Secondary central nervous system lymphoma (SCNSL) is a rare but significant complication, affecting approximately 2–10% of patients with diffuse large B-cell lymphoma (DLBCL), often leading to poor outcomes. The CNS-International Prognostic Index (CNS-IPI) remains a widely adopted tool for estimating CNS relapse risk; however, recent advances in molecular profiling and evolving treatment paradigms have challenged the adequacy of CNS-IPI alone in guiding risk assessment. This study evaluates the prognostic relevance of CNS-IPI in conjunction with molecular and clinical factors, specifically cell-of-origin (COO), gene expression, and frontline treatment regimens. The aim is to refine risk stratification and identify patients at highest risk for SCNSL. Method Building on our previous study, we analyzed data from patients with newly diagnosed DLBCL between 2002 and 2023, enrolled in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource. Patients with primary CNS lymphoma, CNS involvement at diagnosis, or who received CNS-directed therapy were excluded. COO was determined using the Hans algorithm and/or Nanostring profiling. Multivariate cause-specific Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for predictors of CNS relapse that were measured at the time of initial DLBCL diagnosis. Cumulative incidence with competing risks and cause-specific Cox models were used to analyze relapse risk. Result We included 2137 patients in our analysis, median age at diagnosis was 63 (IQR 53-72), with 41.6% (n=890) females. The albumin level was below normal in 32.5% (n=350), 64.0% (n=1367) had CNS-IPI of intermediate/high, 37.5% (n=586) had non-germinal center B-cell (GCB) COO, and 14.5% (n=309) were double expressor positive. At a median follow-up of 83 months (IQR 42 -143), 82 (3.8%) SCNSL relapses were observed; the 3 and 5 yrs cumulative incidence for SCNSL from time of DLBCL diagnosis were 3.7% (95% CI 3.0-4.7) and 3.8% (95% CI 3.1-4.8) respectively. In models accounting for death as a competing risk, the 5-year cumulative SCNSL incidence estimate for patients with a CNS-IPI low was 1.5% (95% CI 0.8-2.7), Intermediate was 4.9% (95% CI 3.7-6.4), and high was 6.1% (95% CI 3.8-9.6). The 5-year cumulative SCNSL incidence estimate for patients with non-GCB COO was 5.2% (95% CI 3.6-7.4) compared to 2.4% (95% CI 1.6-3.6; p=0.001) for GCB patients, and the 5-year cumulative SCNSL incidence for double expressor positive patients was 8.6% (95% 5.8-12.6) compared to 1.5% (95% CI 0.8-2.7; p<0.001) for double expressor negative patients. In a multivariable model (including CNS-IPI, COO, double expressor, treatment group, extranodal involvement, and albumin group), CNS-IPI intermediate (HR 2.85 [1.58-5.14]; p<0.001) and high (HR 4.31 [2.15-8.65]; p<0.001), non-GCB COO (HR 2.36 [1.37-4.06]; p=0.002], double expressor (HR 5.56 [2.88-10.73]; p<0.001), and below normal albumin (HR 1.89 [1.02-3.50]; p=0.042) were independently associated with increased risk of SCNSL. The majority of the SCNSL relapse occurred within the first 2 years after DLBCL diagnosis; 81.7% (n=67), with only 6.1% (n=5) occurring after 5 years. For patients who developed SCNSL, the median OS from the time of SCNSL diagnosis was 24 months (95% CI 17-34), with 5 years OS of 31% (95% CI 22-43). These findings are being validated using an external, international cohort, and final analysis will be presented at the time of presentation at ASH. Conclusion In this large, prospectively followed cohort with long-term follow-up data, SCNSL remains a rare but devastating complication of DLBCL, with a 5-year cumulative incidence of 3.8% and it is associated with very poor long-term survival. While CNS-IPI remains a strong predictor of SCNSL risk, our study identifies non-GCB COO and double expressor phenotype as independent, additive molecular risk factors. These findings suggest that current risk stratification tools may underestimate the risk of CNS relapse in molecularly defined high-risk subgroups. Considering the poor outcomes linked to SCNSL and the absence of a connection with frontline treatment types, future research should aim to enhance risk prediction models through molecular profiling and explore prophylactic strategies to reduce the likelihood of SCNSL.

Abstract Marginal zone lymphoma (MZL) is an uncommon non-Hodgkin B-cell lymphoma accounting for 7-10% of lymphoma diagnoses. Extranodal MZL (EMZL) of mucosa-associated lymphoid tissue (MALT) is the most common subtype (61%) of MZL. While the mutational landscape, chromosomal aberrations and transcriptome of nodal and splenic MZL have been extensively evaluated, the mutational landscape of EMZL has only been evaluated in a small number of patients and mainly by targeted sequencing. Consequently, the comprehensive genomic landscape of EMZL remains largely uncharacterized. Herein, we performed whole exome sequencing followed by targeted sequencing of potential tumor driver genes, copy number and/or RNA transcriptome analyses in 165 patients with pretreatment EMZL tumors involving 16 anatomic locations. All samples were collected and underwent expert review as part of the Atlas of Blood Cancer Genomes consortium. The most common anatomic locations were ocular adnexa (OAMZL) (n=35), gastric (n=25, including 6 positive for Helicobacter pylori), salivary glands (n=25), and lungs (n=24). Using common criteria for variant mutation calling and significant focal copy number (CN), we identified 1218 variants (1030 missense and 188 truncation mutations) across 174 genes and 2837 CN focal alterations (1614 copy gains and 1223 copy number losses). Of these genes, 44 and 11 were mutated in >5% and >10% of specimens, respectively. We detected variants identified previously by targeted sequencing in EMZL from different locations (e.g. TNFAIP3 (A20) (12%), TBL1XR1 (13%), SPEN (10%), CARD11 (7%), TET2 (7%) and CREBBP (6%)). We detected mutations in KLF2 (10 mutations in 8 EMZL patients) and PTPRD (7 mutations in 7 EMZL patients) that were previously suggested to be specific for splenic and nodal MZL, respectively. We also detected variants not previously reported in EMZL, including a transcriptional factor ZEB2, LRP1B,and others. The most frequently mutated gene in our study (25% of tumors) was IGLL5. Some mutations were limited to specific locations (e.g. ZEB2 mutations seen exclusively in gastric and salivary gland EMZL), while most were observed across all anatomic locations. There was no association between specific mutations and clonal IGHV, in contrast to previous reports. Using Enrichr tool, we observed enrichment of mutations in genes belonging to the B cell receptor activation, NOTCH signaling, Wnt-beta catenin signaling, NF-kB signaling , PI3K AKT signaling, DNA repair and other pathways. Chromosomal and copy number changes were also commonly observed including previously reported trisomy of chromosomes 3 and 18. Combining DNA mutations and CN alterations, the number of genetic lesions per tumor sample in the 174 mutated genes ranged from 0 to 95, with an average load of 17 lesions per case. RNA sequencing was successful in 159 EMZL tumors from 16 distinct anatomic locations. Unsupervised clustering using all genes with a median log2-normalized gene expression value greater than 5 and a standard deviation greater than 1 (1323 genes) demonstrated that the majority of gastric EMZL with and without H. pylori infections clustered together on 2 dendrograms as did most of the samples of OAMZL. Since the clustering could be forced by organ-specific and not tumor-specific gene expression, we next focused on the expression of the 345 genes from the LM22 matrix that passed our transcriptomic row filtering. Unsupervised clustering resulted in clustering of most gastric EMZL irrespective of H. pylori positivity together in the same dendrogram branch, accompanied by few additional EMZL from other anatomic locations. Most of the EMZL samples from other locations did not show preferential co-clustering together. On comparison of gene expression between gastric and OAMZL, there was enrichment of the B-cell receptor signaling pathway, B-cell activation, B-cell naïve and B-cell memory cells, B-cell proliferation and NF-kB signaling in the latter. Immune microenvironment analysis showed a statistically significant increase in resting mast cells and decrease in M1 macrophages in gastric EMZL and increase in regulatory T cells in salivary EMZL. The broad genomic studies reported here underscore the biological similarity of EMZL across anatomical sites with the potential exceptions of gastric EMZL (regardless of H. Pylori status). These data provide valuable genomic and transcriptomic resources to inform future diagnostic and therapeutic strategies.

Abstract Background Myelodysplastic syndromes (MDS) develop from the acquisition of somatic mutations over years or decades. While some mutations are random stochastic events, others derive from extrinsic agents that promote genomic instability, including environmental exposures. Agent Orange (AO) is a phenoxy herbicide used by the U.S. military as a defoliant during the Vietnam War (1962-71), often diluted with gasoline to promote aerosolization, contaminated with the carcinogen dioxin during the manufacturing process, and linked to various cancers. We studied AO as a risk factor for MDS. Methods The prospective National Heart, Lung, and Blood Institute National MDS Natural History Study includes patients (pts) with suspected or recently diagnosed MDS enrolled from 6/2016-6/2024. Bone marrow samples underwent central pathology review, resulting in diagnoses including MDS, MDS/myeloproliferative neoplasm overlap (MDS/MPN), or clonal cytopenia of undetermined significance (CCUS). Pts completed detailed assessment for AO exposure and other risk factors for MDS development. Associations between AO exposure, mutation prevalence, and key covariates (demographics, diagnosis, military service, clinical history, cytogenetics and smoking) were assessed using univariate tests of association, multivariable regression and Cox regression models for survival and progression. Results Among 2115 enrolled pts, 1191 (56%) had a myeloid malignancy or precursor condition and 130 (6.1%) reported AO exposure, the majority (96%) male. Compared to unexposed pts, AO exposed pts were more likely to derive from the 1945-54 birth cohort (p<.001), of age to serve in Vietnam, and to have an ultimate diagnosis of MDS or CCUS vs. a non-clonal diagnosis (57% vs. 44%, p=.046). AO exposure was more common among Blacks (12% vs 6%, p=.02), specifically Black males (21% vs 9%, p=.006). AO exposure was marginally associated with International Prognostic Scoring System (IPSS)-R poor-risk cytogenetics (p=.066) and lower baseline platelet count (p=.072). In multiple regression analyses, risk of AO exposure was significantly higher for the 1945-1954 birth cohort (OR [95% CI]=10.3 [4.4-30.6], p<.001), among those with military service (OR=24.1 [11.9-55.8], p<.0001), and for Blacks vs Whites (OR=2.6 [1.2-5.5], p=.017). The crude risk of AO exposure trended higher for patients with MDS compared to idiopathic cytopenia of undetermined significance (ICUS; OR=2.1 [0.9-6.0],p=.13), but not in multivariable analysis (OR=1.3 [0.5-4.4, p=.60]). AO exposure trended toward an association with younger age at MDS diagnosis (p=.06) and toward having a prior primary malignancy (49% vs. 41%, p=.09): Compared to non-AO exposed pts, AO exposed pts had higher rates of prior prostate cancer (16% vs. 8%), notable given its documented association with AO exposure. AO exposure was not associated with a history of or intensity of cigarette smoking (p=.67). After accounting for age, diagnosis, sex, and military service, AO exposure was significantly associated with increased numbers of pathogenic variants in 53 genes (OR=1.2 [1.0-1.4], p=.02) and with having Very Poor/Poor risk IPSS-R cytogenetics (OR=3.7 [1.2-11.1], p=.02). AO exposed and non-exposed pts had similar overall survival (HR=1.1 [0.8-1.7], p=.50), but those exposed to AO had a higher risk of disease progression in the first two years following diagnosis (HR =1.9 [1.2-2.8], p=.004). AO exposed pts appeared more likely to have del (5q) (15% vs. 8%) and del (20q) (13% vs. 5%) karyotypic abnormalities, and TP53 (7.7% vs. 4.5%), STAG2 (5.1% vs. 2.8%), and NRAS mutations (3.4% vs. 1.7%), although sample sizes were small. Focusing on gene pathways, AO exposed pts appeared more likely to have abnormalities in RAS signaling (10.3% vs. 5.7%), cell cycle (7.7% vs. 4.5%), and cohesion complex pathways (6.0% vs. 3.1%), but less likely to have abnormalities in cytokine receptor pathways (0% vs. 4.5%). ConclusionsIn our representative national cohort of pts with at least one cytopenia, and for the first time, we demonstrated that AO exposure is associated with younger age at MDS diagnosis, ultimate MDS diagnosis, genetic complexity of MDS, increased risk of disease progression, and with Black race, despite Black individuals comprising just 16% of men who served in the Vietnam War and being less likely to have MDS than white individuals. AO should formally be recognized as a risk factor for the development of MDS.

Background Delayed aneurysm rupture (DAR) is a potentially devastating complication after flow-diverters (FD) deployment and their exact mechanisms are still unclear. The purpose of this meta-analysis is to synthesize the latest evidence on DAR after FD treatment and identify predictive factors. Methods A systematic review and meta-analysis were conducted according to established protocols. Searches were conducted in PubMed, Scopus, Web of Science, and Embase databases up to June 2024 using variations of “aneurysm “and “delayed aneurysm rupture” terms. Original studies reporting DAR and including more than five patients were included. All analyses were conducted in R software (version 4.4.2), employing the “meta” and “tableone” packages. Results 64 studies with 9820 patients were included, large aneurysm size (OR=1.08, 95% CI (1.02, 1.14), P value=0.008), intra-aneurysmal thrombus (OR=1.05, 95% CI (1.01, 1.09), P value=0.010) and posterior circulation location (OR=1.02, 95% CI (1.00, 1.04), P value=0.014) were significant risk factors for DAR. The pooled prevalence of DAR was 1.40%, with a rate of 1.18% after removing outliers. DAR occurred within 30 days in 66.7% of cases, with a mortality rate of 73.3% at discharge. Conclusion DAR following flow diversion treatment is an uncommon but devastating complication and remains a limitation of the technique. It is influenced by factors such as large aneurysm size, intra-aneurysmal thrombosis, and posterior circulation involvement. The first 30 days post-procedure represent a particularly high-risk period. Despite advancements in FD technology, significant gaps remain, highlighting the need for further research to improve risk assessment and treatment strategies.

Abstract Objectives Polymyalgia rheumatica (PMR) is the second most frequent inflammatory rheumatic disease in older adults, after rheumatoid arthritis. Recommendations for the management of PMR, developed by the German, Austrian and Swiss societies of rheumatology and other organizations were published in 2018. Methods These recommendations are now updated based on a systematic literature search that covered the evidence published between July 2016 and January 2024 on therapeutic interventions and prognostic factors for PMR. The search string followed that used in the original recommendations, with an extension for targeted synthetic disease-modifying antirheumatic drugs (DMARDs). Based on evidence and expert opinions, the task force (12 physicians, two healthcare professionals and two patients from three countries) developed a set of four overarching principles and seven specific recommendations. Results The current update reiterates previous guideline recommendations for glucocorticoids (15–25 mg prednisone equivalents per day) as initial treatment for PMR once diagnosis has been confirmed, with subsequent tapering over 4–12 months (depending on whether a glucocorticoid sparing agent is used), with monitoring of disease activity and treatment-related adverse effects. Interleukin-6 receptor blocking agents should be considered in addition to glucocorticoids in patients with relapsing disease and can be considered in patients with new-onset PMR at high risk for glucocorticoid-related adverse events. Methotrexate or rituximab may be alternatives. Older and frail patients should also be offered an individualized exercise program. Conclusion These updated recommendations should serve as an aid to evidence-based decisions ensuring high-quality care for all patients with PMR.

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.

Introduction Post‐market and multicenter studies report low complete occlusion rates (41‐49%) and higher retreatment rates (13%) following WEB deployment, compared to 3.8% with microsurgical clipping. While retreatment after WEB has been shown to be technically feasible, data on final angiographic outcomes remain limited. We performed a retrospective analysis of prospective databases from 14 cerebrovascular centers in the US to compare the safety and efficacy of retreatment versus continued observation for residual and recurrent aneurysms after initial WEB deployment. Materials/Methods Multinomial logistic regression was used to identify baseline characteristics of untreated and residual/recurrent aneurysms that may act as confounders, which then underwent propensity scored matching (PSM). Results Among 211 patients (125 observed, 86 retreated), retreatment was more common in initially ruptured aneurysms (p<0.001), which showed higher recurrence (p=0.036) and sac‐dominant residuals (p=0.004). These were primarily due to WEB compression on follow‐up (p=0.001), rather than incomplete occlusion with an appropriately sized device (p<0.001). Retreatment was associated with significantly higher rates of complete angiographic occlusion at last follow‐up compared to observation alone (p<0.001), a finding that remained consistent across subgroups defined by initial unruptured status, residual type, and recurrence patterns (all p<0.05). Clipping demonstrated higher, though statistically insignificant, rates of complete occlusion; flow diversion and stent‐assisted coil embolization yielded comparable complete occlusion rates at follow‐up. After propensity score matching, retreatment remained significantly associated with higher rates of complete angiographic occlusion (all p<0.05), with comparable outcomes observed between clipping and various endovascular approaches as above. Conclusion Retreatment improved complete occlusion rates; however, no aneurysmal hemorrhages occurred in either group. Further intervention may be unnecessary when the WEB device—whether compressed or intact (BOSS 0/1)—adequately protects the dome. In cases where WEB compression significantly exposes the aneurysm wall, stent‐assisted coiling or flow diversion can be performed safely and successfully, depending on vascular anatomy and surgeon preference. image image