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Introduction Antibody–drug conjugates (ADCs) are now widely used in breast cancer across multiple disease subtypes. With increasing clinical use, treatment-related toxicities have become an important factor in therapeutic decision-making. However, comparative safety data among ADCs are limited because direct head-to-head trials are lacking. Methods We conducted a systematic review and Bayesian network meta-analysis of randomized controlled trials evaluating ADCs in breast cancer, with the literature search updated through March 31, 2025. Treatment-related adverse events (TRAEs) and serious adverse events (SAEs) were classified according to the Common Terminology Criteria for Adverse Events. A Bayesian random-effects network meta-analysis was used to compare toxicity risks across regimens. Odds ratios (ORs) with 95% credible intervals (CrIs) were estimated, and surface under the cumulative ranking curve (SUCRA) values were used as descriptive ranking summaries interpreted alongside the corresponding comparative estimates. The protocol was registered in PROSPERO (CRD42024606194). Results Seventeen randomized trials including 8,946 patients and five ADCs—trastuzumab emtansine (T-DM1), sacituzumab govitecan (SG), trastuzumab deruxtecan (T-DXd), datopotamab deruxtecan (Dato-DXd), and ARX788—were analyzed. SG and T-DXd showed the least favorable hematologic safety profiles, with SUCRA values for anemia/neutropenia of 22.6%/17.4% and 12.0%/27.0%, respectively. T-DM1-based regimens had the highest risk of thrombocytopenia. For gastrointestinal toxicities, SG ranked worst for diarrhea (SUCRA 6.0%), whereas T-DXd was associated with higher risks of nausea (2.2%), vomiting (7.9%), and decreased appetite (8.6%). In analyses of SAEs, SG had the highest rates of anemia (11.7%) and neutropenia (8.5%), while gastrointestinal SAEs occurred more frequently with T-DXd. Conclusions This network meta-analysis highlights clinically meaningful differences in ADC safety profiles and may help guide individualized toxicity management in breast cancer.

ABSTRACT Introduction Cardiogenic shock (CS) carries high morbidity and mortality. However, evidence for mechanical circulatory support (MCS) in non‐ischemic CS remains limited, as prior trials have focused on infarct‐related shock. Methods We performed a retrospective cross‐sectional study using the Nationwide Inpatient Sample (2017–2019), identifying hospitalizations with a principal diagnosis of CS and excluding acute myocardial infarction. Patients were categorized into mutually exclusive groups: no MCS, intra‐aortic balloon pump (IABP) only, Impella only, or extracorporeal membrane oxygenation (ECMO) only. Primary outcomes were in‐hospital mortality, length of stay (LOS), and total hospital cost. Survey‐weighted multivariable models adjusted for demographics, hospital characteristics, and comorbidity burden using Elixhauser and Charlson indices. Results Among 104 045 hospitalizations, 6040 received MCS. IABP was most common, followed by Impella and ECMO. Among patients receiving a single MCS strategy, overall in‐hospital mortality was 29.5%., with lower mortality in the IABP group (19.9%) compared with Impella (37.4%) and ECMO (44.3%). After adjustment, IABP was associated with lower observed in‐hospital mortality versus no MCS (aOR 0.58, 95% CI 0.53–0.64; p < 0.01), whereas Impella and ECMO showed increased aOR. IABP and ECMO were associated with longer LOS, while Impella was not. All MCS strategies were associated with higher costs, with ECMO showing the greatest increase (cost ratio 3.30, 95% CI 3.12–3.50). Conclusion In non‐ischemic CS, MCS strategies demonstrate distinct profiles of mortality and resource utilization. IABP was associated with lower observed in‐hospital mortality, whereas all MCS were linked to higher costs, highlighting heterogeneity in outcomes and the need for further research.

e23510 Background: There is minimal data comparing the prognostic models (PM) developed to predict recurrence of Gastrointestinal stromal tumors (GIST). Current PM include tumor size, location, and mitotic index (MI) but lack genotypic data. This analysis compares the performance of the National Institute of Health, Armed Forces Institute of Pathology, and Memorial Sloan Kettering Cancer Center models for predicting tumor recurrence with and without adjustment for c-kit Exon 11 mutation status (MS). Methods: This is an IRB-approved single-institution retrospective analysis. Adults with GIST mentioned in their medical records from 2014 – 2025 at the authors’ institution were identified. 330 patients with documented biopsy-proven GIST were included. Data was collected by manual review. PM were evaluated using multivariate analysis, adjusting for surgical resection, systemic therapy, and c-Kit exon 11 MS. Predictive performance was measured by area under the curve (AUC) and receiver operating characteristics (ROC) analysis. Results: Mean age at diagnosis was 64.6 years (SD =13.1 years). 46.7 % of patients were male. Primary GIST was most common in the stomach (63.9%) and small bowel (23.6%). Primary tumors were an average of 6.5 cm (SD 5.6 cm) with a MI of 6.6/50 HFU (SD = 13.4 50/HFU). Patients received resection (80.9%) and first-line systemic therapy with imatinib (43.3%). Recurrence occurred in 17.9% of patients. 19.4% of patients completed genetic testing, of which 51.6% were c-Kit exon 11 mutation positive. The predictive performance of all PM was not statistically different with (p value = 0.16) or without (p value = 0.18) adjustment for c-Kit exon 11 MS (Table 1). However, adjusting for c-Kit exon 11 MS improved AUC for all PM. Conclusions: Adjusting PM for c-KIT exon 11 status improved the predictive performance of all PM, although the results were not significant. This study found no significant difference in the performance of PM that predict GIST recurrence, which aligns with the one other study on this topic. Lack of significance may be due to a small sample size, which also limited the multivariate analysis. Including genotypes in PM that predict GIST recurrence should be further explored with larger multi-center datasets that can adjust for multiple mutations. Multivariate analysis comparing prognostic models for gastrointestinal stromal tumors at predicting recurrence with and without adjustment for c-KIT exon 11 genotypes using receiver operating characteristic. Without c-Kit exon 11 AUC (95% Confidence Interval) P value NIH 0.86 (0.81,0.90) 0.18 AFIP 0.87 (0.82,0.91) MSKCC 0.89 (0.84,0.94) With c-Kit exon 11 genotype NIH 0.80 (0.67,0.93) 0.16 AFIP 0.84 (0.73,0.95) MSKCC 0.90 (0.80,0.99) NIH: National Institute of Health, AFIP: Armed Forces Institute of Pathology, MSKCC: Memorial Sloan Kettering Cancer Centre. AUC: Area under the curve.

e16335 Background: Splanchnic vein thrombosis (SVT) is a rare but serious complication in gastrointestinal (GI) cancer patients. This study investigated its features and outcomes in U.S. hospitalizations. Methods: Hospitalizations (≥18 years old) with a GI malignancy (ICD-10-CM C15–C26) and portal vein thrombosis (PVT) (I81), hepatic vein thrombosis (HVT) (I82.0), or mesenteric/splenic vein thrombosis (MSVT) (I82.890 + K55) in any of the 40 diagnosis fields were found in 2022 National Inpatient Sample. Exclusions: non-GI cancers (C00-C14, C27-C96), thrombophilia (D68.5, D68.6, D45, D47.1, D59.5), or cirrhosis (K74, K70.3). Demographics and outcomes (in-hospital mortality, length of stay [LOS], and total hospital charges [TOTCHG]) were compared between SVT and non-SVT groups using unweighted descriptive statistics (medians, IQRs), χ², and Mann-Whitney U tests. Multivariable regression of mortality, LOS, and TOTCHG were adjusted for age, sex, race, income quartile, insurance type, hospital region and bed size, and comorbidity burden (elixsum). Stata 17.0 was used. Results: Among 51,940 GI cancer hospitalizations (mean age 68.37 ± 12.79 years; 44.46% female), 833 (1.60%) had SVT. SVT group was younger (66.60 ± 11.76 vs. 68.40 ± 12.80 years; p < 0.001). Sex distribution was similar (58.34% vs. 55.50% male; p = 0.101). Common cancers in SVT: liver (n=427; 51.26%), pancreas (n=310; 37.21%), and colorectal (n=42; 5.04%). SVT prevalence was highest in liver (7.10%), pancreatic (3.14%), gallbladder (2.65%), and biliary (2.13%) cancers. PVT was the predominant subtype (n=788; 94.60%), followed by HVT (n=21; 2.52%), MSVT (n=4; 0.48%), and multiple SVT sites (n=20; 2.40%). SVT subtype distribution was: PVT (n=788; 94.60%), HVT (n=21; 2.52%), MSVT (n=4; 0.48%), and multiple sites (n=20; 2.40%). SVT had longer median LOS (5 [3–10] vs. 4 [3–8] days; z = -6.666, p < 0.001), similar median charges ($65,053 [I33,560–130,026] vs. $66,433 [35,897–121,038]; z = -0.76, p = 0.447), and higher mortality (9.48% vs. 4.41%; χ² = 49.042, p < 0.001). In adjusted analyses, SVT had higher mortality (aOR = 2.23, 95% CI 1.74–2.85, p < 0.001), longer LOS by 1.50 days (95% CI 0.99–2.02, p < 0.001), and 14.63% higher TOTCHG (coefficient 0.136, 95% CI 0.051–0.222, p = 0.002). Conclusions: SVT affects ~1.6% of GI cancer hospitalizations, is most prevalent in liver cancer, and associated with longer stays and higher mortality, underscoring the need for proactive monitoring. Multivariable regression analyses of SVT on clinical outcomes (unweighted sample). Outcome SVT Effect 95% CI P-value Interpretation In-hospital mortality OR 2.23 1.74–2.85 <0.001 SVT doubles odds of in-hospital death Length of stay (days) +1.50 0.99–2.02 <0.001 SVT associated with ~1.5 extra hospital days Total hospital charges ($) +0.136 (log scale) 0.051–0.222 0.002 SVT increases hospital costs by ~14.63%

e16334 Background: Liver cancer remains a leading cause of cancer-related mortality in the United States. Although overall mortality has increased over the past two decades, age-specific patterns and demographic disparities are not fully characterized. Here, we analyzed U.S. liver cancer mortality trends from 1999 to 2023, with a focus on age-related, racial and ethnic, and geographic differences. Methods: Mortality data for U.S. adults aged ≥25 years from 1999 to 2023 were obtained from the CDC WONDER database (ICD-10 codes C22.0–C22.9). Age-adjusted mortality rates (AAMRs) were standardized to the 2000 U.S. population. Joinpoint regression was used to estimate annual percent change (APC) and average annual percent change (AAPC) across age groups, sex, race/ethnicity, U.S. census regions, and levels of urbanization. Results: From 1999 to 2023, overall liver cancer AAMRs increased from 6.95 to 10.1 per 100,000. Marked age-specific differences were observed. Mortality among adults aged 45–54 and 55–64 years peaked in the mid-2010s and subsequently experienced significant declines through 2023 (recent APCs < −4.5% for both groups; P < 0.001). Specifically, while the overall AAPC for the 55–64 age group was +1.89%, mortality significantly reversed after 2016 (APC, −4.59%). In contrast, mortality continued to rise among older adults, including those aged 65–74 years (AAPC, +2.26%), 75–84 years (AAPC, +1.69%), and aged ≥85 years (AAPC, +1.77%) (all P < 0.001). Differences in mortality trends were also identified across racial and ethnic groups. Sustained increases were observed among Non-Hispanic White individuals (AAPC, +1.89%; P < 0.001), while Hispanic/Latino populations experienced declines after 2013 (APC, −0.67%; P = 0.003). Non-Hispanic Black individuals demonstrated a significant decrease in mortality between 2017 and 2020 (APC, −3.82%; P = 0.03). Regionally, liver cancer mortality declined in the Northeast after 2013 (APC, −0.66%; P < 0.001), whereas no significant post-peak declines were observed in the South or West. Conclusions: U.S. liver cancer mortality trends exhibit substantial heterogeneity by age, race and ethnicity, and geographic region. The recent, sharp declines among middle-aged adults (APCs < −4.5%) likely reflect the success of large-scale viral hepatitis screenings and highly effective antiviral therapies, whereas continued increases among older adults may relate to the cumulative impact of metabolic risk factors and delayed diagnosis. Persistent racial and regional disparities highlight the need for targeted, age-specific prevention and surveillance strategies.

e16102 Background: Esophageal cancer remains a significant cause of cancer mortality in the United States. While overall trends have been reported, less is known regarding contemporary long-term patterns and specific disparities across demographic and geographic strata. We aimed to characterize esophageal cancer mortality trajectories and identify populations with divergent outcomes by age, race/ethnicity, geographic region, and urbanization status. Methods: We analyzed esophageal cancer mortality data from 1999 to 2023 using the Centers for Disease Control and Prevention (CDC) WONDER database. Age-adjusted mortality rates (AAMRs) were standardized to the 2000 U.S. standard population. Joinpoint regression analysis was utilized to estimate the annual percent change (APC) and average annual percent change (AAPC) with 95% confidence intervals (CIs). Analyses were stratified by age, sex, race/ethnicity, census region, state, and urbanization level. Results: Overall esophageal cancer mortality in the U.S. declined significantly from 1999 to 2023 (AAPC, -0.81%; 95% CI, -0.97% to -0.65%; P < 0.001). Declines were observed across most age groups, with the steepest reductions among adults aged 45-54 years (AAPC, -1.68%; 95% CI, -2.01% to -1.35%) and 55-64 years (AAPC, -1.25%; 95% CI, -1.51% to -0.99%). Marked racial and ethnic disparities were identified. Non-Hispanic (NH) Black individuals experienced the most rapid decline (AAPC, -4.07%; 95% CI, -4.42% to -3.72%). Conversely, trends among NH White individuals remained stable overall (AAPC, -0.05%; P = 0.59), characterized by an initial increase from 1999 to 2005 (APC, 1.14%; P = 0.002) followed by a modest decline from 2005 to 2023 (APC, -0.44%; P < 0.001). Regionally, the West demonstrated the largest decline (AAPC, -1.13%; 95% CI, -1.26% to -0.99%), followed by the Northeast (AAPC, -1.05%). West Virginia was the only state to exhibit a significant increase in mortality (AAPC, 1.01%; 95% CI, 0.38% to 1.64%; P = 0.003). Mortality declined significantly in metropolitan areas (AAPC, -1.09%; P < 0.001), while trends in nonmetropolitan areas remained stagnant (AAPC, 0.48%; P = 0.12). Conclusions: Despite a national decline in esophageal cancer mortality, substantial disparities persist across racial, geographic, and urbanization groups. The dramatic improvement among NH Black populations contrasts with the relative stability in NH White populations and the lack of progress in nonmetropolitan areas. The significant rise in mortality in West Virginia warrants targeted investigation. These findings underscore the critical need for population-tailored strategies to ensure equitable progress in cancer prevention and care.

e13663 Background: Artificial intelligence (AI) in oncology, including computer vision and radiomics, has expanded rapidly with substantial federal support. However, the extent to which growth in funding and publications is accompanied by growth in prospective interventional evaluation is not well quantified. We conducted an ecosystem-level analysis to compare temporal trends in federal awards, peer-reviewed publications, and interventional clinical trial registrations in AI-oncology. Methods: We performed a cross-sectional landscape analysis spanning 2015–2025 using three public data sources: (1) federally funded extramural awards indexed in NIH RePORTER (including NIH/NCI and other agencies where available); (2) interventional studies registered on ClinicalTrials.gov; and (3) PubMed-indexed publications. Records were identified using a consistent Boolean query requiring AI-related terms (“artificial intelligence,” “machine learning,” “deep learning,” “neural network,” “large language model,” “computer vision,” “natural language processing,” “radiomics”) AND oncology-related terms (“cancer,” “oncology,” “tumor,” “neoplasm,” “carcinoma,” “melanoma,” “leukemia,” “lymphoma”). Annual absolute counts were extracted (without normalization) to preserve volume differences across the ecosystem. We summarized growth over time and calculated publication-to-trial and grant-to-output ratios. Results: Between 2015 and 2025, AI-oncology federal awards totaled 11,503. PubMed-indexed AI-oncology publications increased 38-fold from 558 (2015) to 21,414 (2025), totaling 81,782. Interventional AI-oncology trial registrations increased from 2 (2015) to 84 (2025), totaling 372 across the period. Overall, the publication-to-interventional-trial ratio was 220:1; interventional trial registrations represented 0.45% of publication volume. For every federal award, approximately 7 publications were produced, but only 0.03 interventional trials were registered. Conclusions: Across 2015–2025, AI-oncology funding and publications increased dramatically, while interventional trial registrations remained comparatively sparse. This persistent publication-to-trial gap highlights a translational bottleneck and supports targeted initiatives to accelerate prospective validation, such as dedicated funding mechanisms, multi-center evaluation networks, and trial-ready implementation infrastructure, to realize clinical benefit from AI in cancer care.

e12691 Background: Breast cancer prognosis is influenced not only by tumor-specific factors but also by systemic inflammatory responses. Elevated circulating neutrophils after surgery may reflect one such systemic inflammatory response that promotes a pro-tumorigenic environment. Neutrophil extracellular traps (NETs) have been recognized as promoters of cancer activity. However, there have been no studies looking at neutrophil count alone as an independent inflammatory marker in cancer. Clinical prognostic significance of post-operative neutrophil counts remains incompletely understood. If proven significant, neutrophil count is a simple, widely available measure to predict post-operative outcomes in patients with breast cancer. Methods: Data from the Mayo Clinic Platform were retrospectively analyzed, including only breast cancer patients with normal BMI and neutrophil counts recorded within one week after breast surgery. Patients who underwent radical mastectomy were excluded. Patients were stratified into two groups based on neutrophil count: high (>7×10⁹/L) and normal (1.7–7×10⁹/L). Clinical outcomes assessed included age distribution and 5-year overall survival. Age differences were evaluated using Cohen’s D, and Kaplan-Meier survival curves and hazard ratio estimation were performed using the Mayo Clinic Platform's tool. Results: A total of 899 patients were included in the study cohort: 303 in the high neutrophil group (Cohort A) and 596 in the normal neutrophil group (Cohort B). Age distributions were comparable between groups (Cohort A mean 59.2 ± 13.1 years; Cohort B mean 59.1 ± 14.3 years; Cohen's d = 0.0079). High neutrophil counts were significantly associated with reduced 5-year survival (77.8% vs. 84.5%) and an increased risk of mortality (HR 1.63; 95% CI: 1.16–2.29; P = 0.0041). Conclusions: Recent literature supports that surgery-induced NET formation may drive metastatic progression. Findings from this retrospective analysis at Mayo Clinic suggest that elevated neutrophil counts after breast cancer surgery are associated with poorer survival and may reflect systemic inflammation. While markers like NLR and NPAR have prognostic value, neutrophil count alone is a practical, accessible biomarker for clinical use. Limitations of this study include potential confounding factors such as lifestyle and comorbidities, and lack of subgroup analysis by breast cancer type, stage, and other treatments in addition to surgery. Further prospective studies are still needed for validation. Five-year survival rate comparison. Statistic High Neutrophil counts Normal Neutrophil counts Patient Count 303 596 1 Month Survival 99% 99.8% 5 Month Survival 97% 99.3% 10 Month Survival 94.4% 97.3% 30 Month Survival 85.4% 91.9% 60 Month Survival 77.8% 84.5% Hazard Ratio* 1.63 (95% CI: 1.16–2.29) P = 0.0041.