Citations

e16017 Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and remains one of the deadliest malignancies worldwide, largely due to late-stage diagnosis. Early-stage PDAC is frequently asymptomatic, limiting opportunities for timely detection and intervention. We aimed to develop prognostic models using routinely acquired pre-diagnostic clinical data to identify individuals at high risk for PDAC prior to clinical manifestation. Methods: We conducted a retrospective study of patients treated at Mayo Clinic sites in Rochester, MN; Phoenix, AZ; and Jacksonville, FL between January 2011 and February 2023. The cohort included 1,151 patients with PDAC and 3,745 noncancer controls. CT scans were standardized to axial abdominal acquisitions with slice thickness between 0.8–3.0 mm. Radiology reports were de-identified, normalized, and parsed into structured sections (Indications, Findings, Impression), with an additional pancreas-specific section aggregating pancreas-related sentences. Demographic and clinical variables, e.g., age, sex, race, etc., were encoded using a templated sentence representation. All textual inputs were embedded using a Sentence-BERT model. A multimodal deep learning survival model was developed to integrate CT imaging, radiology report embeddings, and demographic features. The model was trained using a negative log-likelihood loss to estimate patient-specific PDAC risk scores. Model performance was assessed using the concordance index (C-index). Internal validation included patients from MN and AZ sites, while external validation used the FL cohort. Results: We evaluated unimodal (imaging, text, and demographics) and multimodal inputs. Fusion of imaging, radiology report, and demographic features achieved the highest performance, with internal and external validation C-indices of 0.885 (95% CI, 0.856–0.908) and 0.751 (95% CI, 0.719–0.783), respectively. Subgroup analysis by tumor location demonstrated differential performance. For pancreatic head tumors, internal and external C-indices were 0.916 (95% CI, 0.841–0.960) and 0.778 (95% CI, 0.730–0.823). For pancreatic body tumors, C-indices were 0.954 (95% CI, 0.897–0.997) internally and 0.929 (95% CI, 0.897–0.953) externally. For pancreatic tail tumors, internal and external C-indices were 0.903 (95% CI, 0.830–0.974) and 0.789 (95% CI, 0.689–0.874). Conclusions: A multimodal deep survival model leveraging pre-diagnostic CT imaging, radiology report embeddings, and demographic data can effectively identify individuals at high risk for PDAC prior to clinical diagnosis. Differences in performance were observed across pancreatic head, body, and tail tumors. This approach highlights the potential of opportunistic cancer risk stratification from routine imaging to enable earlier detection and targeted intervention strategies.

Abstract Inherited susceptibility plays a critical role in prostate cancer (PCa) risk. Using large biobank and case-control datasets, we evaluated the contribution of both common variants and rare germline pathogenic variants (PVs) to overall PCa risk. The Prostate Cancer Exome Sequencing Consortium currently includes 427,388 male participants (51,452 PCa cases and 375,936 controls) with whole-exome sequencing data from ten biobanks and studies: UK Biobank (14,669 cases/195,600 controls), All of Us Research Program (7,577/75,226), African Ancestry Prostate Cancer Consortium (7,176/4,675), Mayo Clinic Biobank (6,031/15,084), Mass General Brigham Biobank (3,393/14,095), Geisinger’s MyCode Community Health Initiative (3,026/15,130), UCLA ATLAS Precision Medicine Biobank (2,850/16,904), Penn Medicine Biobank (2,598/16,255), Colorado Center for Personalized Medicine (2,269/13,399), and Malmo Diet and Cancer (1,863/9,568). Based on self-reported race/ethnicity and estimated genetic ancestry, the cases comprise approximately 79% European, 18% African, and 3% other ancestry populations. Single-variant association analyses tested all variants on chromosomes 1-22 and X with a minor allele count ≥ 5. In gene-based analyses, PVs were defined as rare variants (minor allele frequency [MAF] < 1% in controls) that had either a Variant Effect Predictor (VEP) impact score of “high” or a pathogenic or likely pathogenic ClinVar classification. Associations were estimated using Firth logistic regression, adjusting for age and the top ten genetic principal components. Results from individual studies were combined using fixed-effect meta-analysis. In single-variant association analyses, 496 variants reached genome-wide significance (p<5×10-8; MAF>0.02%). Among these, 458 (92%) variants mapped to previously known risk regions, including three rare PVs in HOXB13 (rs138213197), CHEK2 (rs555607708), and FAM111A (rs533676902). Characterization of the remaining 38 variants is ongoing. Gene-based analyses identified significant associations (p<2.4×10-6) for eight genes: HOXB13 (OR=3.7, 95% CI=3.3-4.2), BRCA2 (OR=2.0, 95% CI=1.7-2.3), CHEK2 (OR=1.6, 95% CI=1.5-1.8), ATM (OR=1.6, 95% CI=1.4-1.9), FAM111A (OR=1.4, 95% CI=1.3-1.5), BIK (OR=1.4, 95% CI=1.2-1.6), SAMHD1 (OR=2.1, 95% CI=1.6-2.7), and SMOC2 (OR=3.2, 95% CI=2.0-5.1). All genes except SMOC2 have been previously implicated in PCa susceptibility. Among cancer predisposition and DNA repair genes, nominal associations were also observed for XRCC2 (OR=1.6, 95% CIs=1.2-2.3) and BRCA1 (OR=1.2, 95% CI=1.0-1.4), whereas the association was not significant for PALB2 (OR=1.2, 95% CI=0.9-1.5). These findings reinforce the role of rare germline PVs, particularly in cancer predisposition and DNA repair genes, in PCa susceptibility. As additional studies are incorporated into the Consortium, we expect this work to provide a more comprehensive characterization of the genetic architecture of PCa. Citation Format: Yifan Zhang, Shuyan Cheng, Nicholas Boddicker, Matthew Lebo, Alexander S. Berry, Roni Haas, Ryan Hausler, Tokhir Dadaev, Heena Desai, Alex A. Rodriguez, Ravi K. Madduri, Andrew Hill, Xin Sheng, Susan M. Gundell, Mine Cicek, Penn Medicine Biobank, Olle Melander, Chris R. Gignoux, Isla P. Garraway, Bogdan Pasaniuc, Paul C. Boutros, Matt Oetjens, Adam S. Kibel, Robert J. Klein, Zsofia Kote-Jarai, Fergus J. Couch, Kara N. Maxwell, Burcu F. Darst, David V. Conti, Christopher A. Haiman, Fei Chen. Genetic risk of prostate cancer: Insights from the Prostate Cancer Sequencing Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB390.

ABSTRACT Patients with chronic thromboembolic pulmonary disease (CTEPD) without resting pulmonary hypertension (PH) frequently experience dyspnea which is not explained by stationary assessment and therefore requires cardiopulmonary exercise testing for further diagnostic insight. Ventilatory inefficiency in CTEPD is commonly identified; however, consensus definitions are lacking. Upright iCPET and careful definition of ventilatory inefficiency (i.e., lack of reduction in VD/VT with exercise) may allow optimal characterization of patients with chronic thromboembolic disease and specifically helps correctly identify patients with preload insufficiency who may otherwise be misclassified or undiagnosed.

Abstract Introduction: First-line therapy for mantle cell lymphoma (MCL) represents a critical opportunity for clinical benefit via sustained complete response. Multitargeted combinations, including optimized chemotherapy with novel agents targeted to MCL, may best address the biologic heterogeneity of this disease. Building on prior experience using modified VR-CAP and R-cytarabine, we enhanced this regimen by adding acalabrutinib while reducing cytarabine, aiming for improved efficacy and safety without impeding stem cell collection. To evaluate this regimen, we conducted a phase 2 multicenter trial at four centers in the Academic and Community Cancer Research United (ACCRU) network. Methods: CARiBOU (ACCRU-LY-1804) employs alternating VR-CAP and R-cytarabine with continuous acalabrutinib, in six 21-day cycles, for previously untreated MCL. Eligible patients (pts) were at least 18 years old with adequate organ function, measurable disease, no CNS involvement; and were ASCT-eligible per investigator. VR-CAP (cycles 1, 3, and 5) employed bortezomib 1.3 mg/m² subcutaneously days 1,8, and 15; rituximab 375 mg/m² day 1; cyclophosphamide 750 mg/m² day 1; doxorubicin 50 mg/m2 day 1; and prednisone 100 mg PO days 1-5. Cycles 2,4, and 6 used outpatient rituximab with cytarabine at 3 g/m² x 2 daily doses in the first 5 pts, after which the dose was reduced to 2 gm/m2 x 2 daily doses due to thrombocytopenia. Acalabrutinib 100 mg p.o. twice daily was given continuously but held for grade 4 thrombocytopenia or clinically significant toxicity. Growth factors and prophylactic antimicrobials were required. Responses were assessed after six cycles using FDG PET/CT, bone marrow aspirate/biopsy (BM Bx), and endoscopy when indicated (Lugano 2014). The primary endpoint was complete metabolic response (CMR) rate at end of treatment (EOT). Secondary endpoints included safety, feasibility of stem cell collection, proportion proceeding to ASCT, and progression-free (PFS) and overall survival (OS). Peripheral blood measurable residual disease (MRD) was evaluated using the ClonoSEQ assay (Adaptive Biotechnologies). A Simon two-stage design was used, targeting a CMR rate ≥ 70% versus a null rate of 50%, with a total sample size of 39 evaluable pts. Results: 41 pts were accrued from October 2021 through May 2025, 37 of whom completed all 6 cycles and are evaluable for response; two are still on therapy, and two discontinued therapy early for toxicity. Median age was 63 years (range 41-73), 22% were female, and 85% were White. MIPI risk groups were low (44%), intermediate (39%), and high (17%), and Ki-67 was ≥ 30% in 54% of pts. Three pts (7%) had known TP53 mutations by sequencing. At EOT, the CMR rate was 85.3% among all pts (35/41; 95% CI 67.9%-92.9%), or 94.5% among evaluable pts (35/37). BM Bx was performed in 31 pts at EOT, 30 of which (96.8%) were negative for MCL. Undetectable peripheral blood MRD using a 10^6 threshold (uMRD6) was achieved at EOT in 78.1% (25/32) of tested pts, and 93.8% (30/32) achieved uMRD5. Nine of 41 pts underwent ASCT; no patient failed stem cell collection. Maintenance therapy was not protocol-defined but included rituximab in 91.9% of pts and a BTK inhibitor in 21.6% of pts. With a median follow-up of 16.7 months, PFS and OS rates at 18 months were 76.7% (95% CI: 60.7%-96.9%) and 95.5% (95% CI: 87.1%-100%). Toxicities were primarily hematologic. Grade 3+ toxicities included thrombocytopenia (66.6%), neutropenia (28.2%), and anemia (25.6%). Grade 3+ febrile neutropenia was observed in 10.2% of pts. Peripheral neuropathy was seen in 20.5% of pts and all grade 1-2. Bleeding events included purpura in 2 pts (5.2%) and epistaxis in 1 patient (2.6%). One case of atrial fibrillation was observed. Eighteen pts required temporary dose hold of acalabrutinib for toxicity (mainly thrombocytopenia), and 2 had acalabrutinib dose reductions. Serious adverse events occurred in 6 pts, and no treatment-related deaths occurred. Conclusions: CARiBOU is an efficient, highly effective outpatient regimen for 1st line MCL, employing intermediate dose cytarabine with continuous acalabrutinib. With supportive care and platelet monitoring, this approach safely yielded high rates of CMR and uMRD6, supporting multitargeted induction therapy for appropriate pts with untreated MCL.

Human factors/ergonomics (HF/E) is increasingly recognized as critical to improve patient safety, quality of care and health care professional wellbeing; in particular, much HF/E work has focused on surgery and surgical teams. However, conducting research and practicing in health care broadly, and the operating room (OR) specifically, requires specialized knowledge, skills and experience. HF/E professionals trained in other application areas may face challenges in transitioning due to the specialized knowledge required, regulatory considerations, and complex sociotechnical systems in the OR. This panel is designed for HF/E students, practitioners and researchers who are interested in expanding their work to the OR, procedural suites or modeling these for usability studies. The panel will provide attendees with insights from established HF/E researchers and professionals who study the OR as well as a newcomer to the domain, offering perspectives on career transition challenges, opportunities, and strategies for success.

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Objective: Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network. Methods: We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit. Results: The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility. Conclusion: This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Background The Woven EndoBridge (WEB) device is a prevalent treatment for intracranial aneurysms. While many studies have assessed the obliteration rate post-WEB embolization, few have focused on long-term outcomes in partially thrombosed aneurysms. Objective To assess whether partially thrombosed aneurysms are at higher risk of recurrence or retreatment following WEB embolization compared with non-thrombosed aneurysms. Methods We evaluated data from 22 academic institutions, focusing on previously untreated cerebral aneurysms treated with the WEB device. Logistic regression was utilized to analyze factors predicting long-term aneurysm obliteration and retreatment necessity. Results Among 1303 patients, 26 presented with a partially thrombosed aneurysm. In the partially thrombosed group, the mean aneurysm maximal diameter was 10.7±4 mm with a neck ratio of 1.99±1.19 mm, larger than in the control group where the mean aneurysm maximal diameter was 6.81±2.37 mm with a neck ratio of 1.64±0.51 mm (P<0.001 for both maximal diameter and neck ratio). At the final follow-up, partially thrombosed aneurysms treated by the WEB device had a 38.5% retreatment rate, compared with 7.0% for non-thrombosed aneurysms (P<0.001). Among partially thrombosed aneurysms, the Raymond-Roy type IIIa/b occlusion rate was higher (38.5% vs 9.9%, P<0.001). On multivariate analysis, partially thrombosed aneurysms compared with non-thrombosed aneurysms had an increased rate of retreatment (OR 3.64, 95% CI 1.28 to 10.1). Conclusion Partially thrombosed aneurysms are associated with a poorer occlusion rate and a higher rate of retreatment following WEB embolization. For partially thrombosed aneurysms, the WEB device appears suboptimal as a first-line treatment, and therefore alternative techniques should be prioritized.

Abstract Melanoma is characterized by its rapid progression and high mortality rates, making early and accurate detection essential for improving patient outcomes. This paper presents a comprehensive review of significant advancements in early melanoma detection, with a focus on integrating computer vision and deep learning techniques. This study investigates cutting-edge neural networks such as YOLO, GAN, Mask R-CNN, ResNet, and DenseNet to explore their application in enhancing early melanoma detection and diagnosis. These models were critically evaluated for their capacity to enhance dermatological imaging and diagnostic accuracy, crucial for effective melanoma treatment. Our research demonstrates that these AI technologies refine image analysis and feature extraction, and enhance processing capabilities in various clinical settings. Additionally, we emphasize the importance of comprehensive dermatological datasets such as PH2, ISIC, DERMQUEST, and MED-NODE, which are crucial for training and validating these sophisticated models. Integrating these datasets ensures that the AI systems are robust, versatile, and perform well under diverse conditions. The results of this study suggest that the integration of AI into melanoma detection marks a significant advancement in the field of medical diagnostics and is expected to have the potential to improve patient outcomes through more accurate and earlier detection methods. Future research should focus on enhancing these technologies further, integrating multimodal data, and improving AI decision interpretability to facilitate clinical adoption, thus transforming melanoma diagnostics into a more precise, personalized, and preventive healthcare service.