Citations

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after neoadjuvant therapy (NT) and curative-intent resection. Although major pathologic response predicts favorable outcomes, most patients achieve only minor response with a heterogeneous recurrence risk. We asked whether the spatial organization of residual PDAC encodes clinically relevant biology beyond residual tumor burden. Experimental Design: In a retrospective cohort of 203 patients with resected PDAC treated with NT and restricted to minor pathologic response, routine hematoxylin and eosin (H&E) whole-slide images were segmented into cancer and stroma using an artificial intelligence–enabled pipeline. Spatial composition (patch density, edge density) and configuration (compactness/complexity, intermixing) were quantified and tested for associations with disease-free survival (DFS) using multivariable models adjusted for standard clinicopathologic factors. Results: Shorter DFS was associated with a fragmented, interface-rich tumor–stroma ecology featuring higher edge density and diversity and reduced homotypic aggregation, independent of clinicopathologic variables. Two spatial risk models were independently prognostic: (i) cancer mean shape index plus stromal shape index variability [adjusted hazard ratio (HR), 1.71; P = 0.003] and (ii) mean stromal patch area plus edge density (adjusted HR, 2.19; P = 0.002). Both models stratified outcomes in which pathologic response grading and residual cancer area did not. High-risk configurations were further associated with reduced intratumoral tumor-infiltrating lymphocyte (TIL) density and infiltration ratio, with relative TIL accumulation at the cancer periphery and within the stroma, consistent with an immune-excluded phenotype. Conclusions: Residual cancer–stroma topology quantified from standard H&E slides yields independent prognostic signals after NT in PDAC, provides a cellular immune correlate for spatial risk, and motivates prospective validation and spatially informed adjuvant strategies.

ABSTRACT Background Implementation of remote robotic‐assisted surgery (rRAS) has the potential to significantly improve access to high‐quality surgical care for patients worldwide. Advances in robotic systems and telecommunications now enable highly reliable, low latency, and cybersecure connectivity. These technological developments, together with the broader adoption of telemedicine, have driven an increasing interest in the implementation of rRAS. Methods The Clinical Robotic Surgery Association (CRSA) convened a conference to develop international clinical recommendations for rRAS using the Danish Model of Consensus. The society identified five key issue categories and invited a panel of experts to address specific questions for each. Panel members and a jury of 10 unbiased, non‐expert in rRAS professionals participated in a day‐long conference on November 19, 2025 (Los Angeles, USA). During the conference, participants presented, discussed, and reached consensus on specific guidelines relevant to each category. Immediately after the conference, the jury reviewed and approved the recommended guidelines. Results Multiple recommendations were approved for each of the following categories: Operating Models, Roles and Responsibilities (2 recommendations), Clinical Pathway (2 recommendations), Emergency Management (8 recommendations), Technical Considerations (5 recommendations), and Medico‐legal and Ethical Considerations (4 recommendations). Conclusions There was consensus that with recent advances in telecommunication and robotic technologies, rRAS can be offered safely and effectively if the identified safeguards are realized. Adoption of rRAS is expected to continue advancing rapidly.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti–volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting. Graphical Abstract

Background: Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. Methods: Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. Results: In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). Conclusions: Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.

ABSTRACT Pathogenic variants in AMMECR1 have been associated with a rare multisystem disorder characterized by midface hypoplasia, hearing impairment, elliptocytosis, and nephrocalcinosis (MFHIEN). To date, most reported cases involve copy number variants or presumed loss‐of‐function alterations, with only a single prior report describing a missense variant supported by functional studies. Here, we report a patient with a heterozygous de novo AMMECR1 missense variant, NM_015365.3:c.649G>A p.(Val217Met) presenting with clinical features consistent with MFHIEN, including midface hypoplasia, partial hearing impairment, nephrocalcinosis, and elliptocytosis identified on peripheral blood smear. Comparative review of the literature highlights that while previously reported missense variants in AMMECR1 demonstrated altered intranuclear protein distribution and reduced expression in functional assays, clinical evidence supporting pathogenicity of non‐truncating variants remains limited. The phenotypic overlap between our patient and prior report strengthens the association between missense variations and the MFHIEN phenotype. Our findings support the pathogenic relevance of missense variation in AMMECR1 and emphasize the importance of integrating detailed phenotyping, including hematologic evaluation, with genomic data in the diagnosis of rare multisystem disorders. Additional cases and functional studies are needed to clarify genotype–phenotype correlations and underlying disease mechanisms.

Background Immune checkpoint inhibitor-based combinations represent the standard of care for advanced renal cell carcinoma. The OMNIVORE trial investigated a response-adaptive strategy, including treatment discontinuation in early nivolumab responders (Arm A) and salvage ipilimumab addition in non-responders (Arm B). Here we report outcomes with extended follow-up. Methods OMNIVORE was a phase II response-adaptive trial in which patients received induction nivolumab monotherapy. Patients achieving a confirmed complete or partial response discontinued nivolumab and entered observation (Arm A), while patients with stable disease or progressive disease received two doses of ipilimumab added to ongoing nivolumab (Arm B). This analysis characterizes long-term overall survival across the full study cohort and durability of response among patients who discontinued nivolumab following an early objective response. Results Of 83 patients who initiated treatment, 12 (14%) were allocated to Arm A and 57 (69%) to Arm B, with a median follow-up among living patients of 59.4 months (range 29.1–85.4 months) in Arm A and 31.4 months (range 4.6–62.6 months) in Arm B. The 3-year overall survival rate from nivolumab initiation was 64% (95% CI 51% to 74%) in the overall cohort, 83% (95% CI 48% to 96%) in Arm A, and 63% (95% CI 47% to 76%) in Arm B. Of the 12 Arm A patients, 6 (50%) remained off nivolumab at 1 year following treatment discontinuation, of whom 5 maintained responses beyond 43 months off therapy, with all remaining alive at last known follow-up, with overall survival ranging from 48.8 to 85.4 months from nivolumab initiation. Of the six patients who resumed treatment, only one achieved a durable complete response remaining on treatment at 83.2 months from nivolumab initiation. All 57 Arm B patients discontinued treatment with a median treatment duration of 3.7 months (range 1–24.8 months) and a median progression-free survival from nivolumab plus ipilimumab initiation of 4.6 months (95% CI 2.7 to 6.5 months). Conclusions With extended follow-up, a meaningful subset of patients achieving an early objective response to nivolumab maintained prolonged treatment-free survival following a short course of treatment. Salvage ipilimumab in nivolumab non-responders demonstrated limited benefit, reinforcing that upfront concurrent dual checkpoint blockade remains the preferred approach. Trial registration number NCT03203473 .

ABSTRACT Introduction Chronic gastroduodenal disorders remain challenging to manage, and new diagnostic approaches are needed to better delineate underlying causes and guide therapeutic decisions. Body Surface Gastric Mapping (BSGM) technologies combine high‐resolution gastric myoelectrical activity measurements with symptom and psychological profiling to provide mechanistic insights into gastric motor and sensory dysfunction. An International Working Group convened to derive the first consensus classification of BSGM phenotypes (the “Auckland Classification”). Methods A Technical Group conducted a systematic literature and clinical database review to identify objective test biomarkers and candidate disease mechanisms. Evidence was synthesized across 50 studies (primarily in gastroparesis, chronic nausea and vomiting, and functional dyspepsia), and BSGM phenotypes were mapped to existing treatment guidelines. Subsequently, iterative review and development of consensus was performed by a Consensus Group composed of international clinical experts familiar with BSGM. Eleven statements underlying the classification were then derived and circulated as a final survey to establish agreement. Results Six BSGM phenotypes were endorsed: three defined by myoelectrical abnormalities (Dysrhythmic, High Frequency, and Low Meal Response) and three by characteristic symptom associations (Sensorimotor, Continuous, and Delayed Onset Symptoms). Published studies plausibly linked these phenotypes to mechanisms including interstitial cell of Cajal depletion, vagal impairment, hypomotility, visceral hypersensitivity, gut‐brain dysregulation, and small bowel dysfunction. Phenotypes were also mapped to existing mechanism‐based treatment guidelines. Ten out of the eleven statements had > 80% agreement. Conclusions The Auckland Classification, derived by international consensus, presents a structured framework for BSGM‐defined patient phenotypes. Evidence and mechanism‐based treatment options are suggested for each phenotype to provide a foundation for research, further validation, and a pathway for integrating BSGM into clinical care.

Abstract Extramedullary disease (EMD) is increasingly recognized in relapsed/refractory multiple myeloma (MM), with central nervous system involvement (CNS-MM) representing a rare but highly aggressive manifestation associated with dismal outcomes. Data on its incidence and optimal management, particularly in the era of cellular and bispecific therapies, remain limited. We conducted a retrospective analysis of patients with MM with pathology-confirmed EMD treated at our institution between January 2000 and December 2023. CNS-MM was defined by parenchymal or leptomeningeal involvement. Clinical features, cytogenetics, treatments, and outcomes were analyzed, with predictors of CNS involvement evaluated among patients with EMD. Among 304 patients with EMD, 20 (6.5%) had CNS-MM. CNS involvement occurred a median of 19.2 months after MM diagnosis, though 15% presented at diagnosis. CNS-MM was strongly associated with high-risk cytogenetics (odds ratio [OR], 3.7) and additional visceral EMD (OR, 4.6). Median overall survival (OS) from CNS-MM diagnosis was 4.2 months. Outcomes were significantly improved in patients receiving chimeric antigen receptor T-cell therapy or bispecific antibodies, with a median OS of 19.2 months vs 1.2 months in those who did not receive immune effector therapies. Intrathecal (IT) chemotherapy demonstrated limited and transient benefit. In conclusion, CNS-MM is an aggressive complication enriched in high-risk, multifocal EMD with extremely poor prognosis. Cellular and bispecific therapies show promising activity and may meaningfully improve survival, whereas IT therapy appears to have limited efficacy. Prospective studies are needed to define optimal treatment strategies for CNS-MM.