Sun, Chenyu

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%

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.

1673 Background: Survivorship care plans (SCPs) summarize cancer treatments and provide evidence-based recommendations for surveillance, screening, and management of treatment-related complications. Despite the availability of guidelines and templates, SCPs remain underutilized in routine practice due to the time-intensive and error-prone nature of manual creation. Empowering cancer survivors to use publicly available large language models (LLM)–based chatbots, such as ChatGPT, may offer a feasible solution to this problem. Methods: Standardized patient profiles were developed for six common cancers: breast, colorectal, non–small cell lung, small cell lung, prostate cancer, and diffuse large B-cell lymphoma. Profiles were reviewed by a primary care physician and an oncologist to ensure clinical accuracy and representativeness of common survivorship scenarios. Using lay language to simulate real-world patient input, ChatGPT (version 5.2, publicly available at the time of the study) was prompted with each profile using the question, “What follow-up care do I need?” Common survivorship symptoms for each cancer were also tested. Responses were evaluated across five domains: medical accuracy (concordance with current ASCO or NCCN guidelines), thoroughness, patient safety and risk framing, clarity and patient comprehension, and actionability. Five-point Likert scales were used (1 = not at all; 5 = very much). Scores were averaged across two independent physician raters. Readability was assessed using the Flesch Reading Ease Score. Results: ChatGPT-generated SCPs demonstrated high medical accuracy (mean score 4.25/5), with no factual errors or hallucinations identified on manual review. Patient safety and risk framing scored 3.75/5; symptom red flags and care escalation guidance were appropriately stated in most scenarios. Thoroughness was moderate (3.50/5), with key survivorship elements, such as smoking cessation counseling or genetic risk considerations; sometimes omitted unless explicitly prompted. Readability was limited, with a mean Flesch Reading Ease Score of 40.5, corresponding to a college reading level and exceeding recommended readability standards for patient education materials. Clarity (3.38/5) and actionability (3.38/5) were similarly constrained due to dense medical language. Conclusions: ChatGPT demonstrated high medical accuracy in generating survivorship care plans across six common cancers, supporting the feasibility of using publicly available LLMs to guide survivorship care. However, improvements in comprehensiveness, readability, and patient-centered actionability are necessary before such tools can be safely integrated into clinical survivorship workflows.

As an emerging tire wear-derived environmental contaminant, 6PPD-quinone (6PPDQ) has raised significant concerns regarding its neurotoxic potential, particularly for children exposed to recycled tire crumb rubber in playgrounds. However, the molecular mechanisms by which 6PPDQ influences neurological disorders such as epilepsy remain poorly understood. In this study, we employed an integrative framework combining network toxicology, bulk analysis of human epileptic brain tissues, Mendelian randomization, and molecular dynamics simulations to elucidate these mechanisms. Our findings, validated through CETSA-WB and SPR, identify 6PPDQ as a direct ligand that binds to and stabilizes neuronal TP53. Through a synergistic double-hit mechanism, 6PPDQ directly engages the TP53 pathway while simultaneously triggering microglial interleukin-6 secretion. These converging pathways lead to the suppression of the master antioxidant regulator Nrf2, resulting in glutathione depletion, excessive reactive oxygen species accumulation, and exacerbated neuronal damage under excitotoxic stress. Experimental validation using glutamate-induced HT22 cell models and microglia–neuron crosstalk systems confirmed that targeting the TP53/Nrf2 axis or scavenging ROS significantly attenuates 6PPDQ-induced neurotoxicity. Our findings highlight critical risks to pediatric neurological health posed by tire-derived contaminants and identify the TP53/Nrf2 axis as a promising therapeutic target. Furthermore, this work provides a robust scientific basis for refining risk assessment frameworks and developing regulatory strategies to mitigate environmental exposure to 6PPDQ.

Background Recent studies have disputed the association between bisphenol A (BPA) and endometriosis. This updated meta-analysis was conducted to investigate the impact of BPA exposure on endometriosis. Methods This study evaluated BPA exposure and endometriosis risk, analyzing studies from Web of Science, PubMed, Embase, Cochrane Library, and Chinese databases (CNKI, CBM, Wanfang, VIP) through August 2025. Methodological quality was assessed via the Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality Tool (AHRQ). Heterogeneity was quantified using I 2 statistics. Pooled odds ratios (ORs) and 95% CIs were calculated using fixed- or random-effects models according to heterogeneity. Subgroup analysis, sensitivity analysis, and publication bias assessment were supplemented to validate the overall finding. Results Seven studies comprising 2,488 participants were included in quantitative synthesis. The meta-analyses demonstrated that BPA exposure was associated with a statistically significant increase in endometriosis risk (OR = 1.36, 95% CI = 1.13–1.63, p = 0.008). Additionally, subgroup analysis indicated that BPA exposure may further elevate endometriosis risk in cases with different regions, populations, and measurement methods. Sensitivity analysis confirmed that the results of these meta-analyses were relatively robust. No publication bias was detected. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251019552 , identifier PROSPERO (CRD420251019552).