Citations

Ameloblastomas are locally aggressive odontogenic tumors that most commonly arise in the mandible and maxilla and often harbor BRAF V600E mutations. While rare extragnathic ameloblastomas have been reported, primary pulmonary cases have not been documented. We identified 3 pulmonary neoplasms with features of ameloblastoma that were submitted for consultation between 2022 and 2025 at 3 academic institutions and performed detailed clinicopathologic and molecular analysis. The tumors presented as 5.4 to 7.3 cm peribronchial solitary lung masses. All resected tumors exhibited a predominant stellate reticulum-like component composed of loosely arranged p40-reactive bland squamoid-to-spindled cells with long intercellular bridges, and streaming and swirling architecture, surrounded by palisaded columnar cells with focal reverse nuclear polarity at the interface with myxoid stroma. All tumors were immunoreactive for BRAF V600E IHC, and BRAF V600E mutations were confirmed by molecular assays, including broad next-generation sequencing on 2 cases. Florid hyperplasia of entrapped pneumocytes initially suggested a biphasic neoplasm, but the absence of BRAF V600E IHC labeling confirmed pneumocyte entrapment. Occult gnathic primary tumors were excluded by subsequent clinical examination, including negative panoramic dental x-rays. No recurrences or metastases have been observed to date. This first series of primary pulmonary ameloblastomas highlights a distinctive tumor with histologic and molecular features identical to gnathic counterparts. We discuss the potential histogenesis of these unusual tumors.

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information ( Appendix 1 and Appendix 2 ). Updates are published regularly and can be found at www.asco.org/genitourinary-cancer-guidelines .

Abstract INTRODUCTION: Prostate cancer (PC) shows marked heterogeneity in clinical outcomes, from indolent to highly aggressive disease. Alterations in TP53, PTEN, RB1, and DNA damage repair genes are linked to poor patient outcomes, yet how the interaction and co-occurrence of these alterations impact prognosis remains unclear. We leveraged the AACR Project GENIE prostate cancer cohort to assess the prognostic impact of individual and concomitant gene alterations, focusing on key PC drivers (BRCA2, TP53, PTEN, RB1). METHODS: the study was conducted in two stages: (i) identification of candidate alterations at individual gene level through a systematic literature review, followed by analyzing the prevalence and co-occurrence of genes in the AACR Project GENIE Public Release (PR) dataset. (ii) Prognostic analysis of the pre-selected biomarkers in the AACR Project GENIE Biopharma Collaborative (BPC) cohort. Analyses were performed in three clinical settings (entire BPC cohort, n=1114; subgroup of patients with localized disease at diagnosis, n=837; and subgroup of patients with metastatic PC with a biopsy collected before or at the time of mPC, n=455) using two statistical approaches: univariate analysis (UA) and inverse probability weighting using propensity scores (PS). Cox models were applied to estimate overall survival hazard ratios (HRs). RESULTS: after integrating the findings from the systematic review with the analysis of the GENIE PR (n=5684), 107 individual genes and 122 co-occurring alterations were selected for study. In the GENIE BPC (n=1114 patients) 75% were M0 at diagnosis, and 67% presented metastatic PC at any time. Alteration prevalence was: TP53, 25%; PTEN, 15%; BRCA2, 4%; RB1, 4%; co-alterations included, PTEN-TP53 (6.4%), RB1-TP53 (2.1%), PTEN-RB1 (1.3%), and BRCA2-TP53 (1.2%). All four key genes showed a statistically significant prognostic impact in the entire cohort with UA (BRCA2, HR:1.9; TP53, HR:2.6; PTEN, HR:2.4; RB1, HR:4.1, all p<0.001). Results were consistent in the subgroup analysis and when using PS. Additional genes such as FANCA, PIK3CA, and MYC were also associated with poor outcomes. Among co-alterations, BRCA2-TP53 (HR:2.0, p=0.05), RB1-TP53 (HR:3.6, p<0.001), PTEN-TP53 (HR:2.4, p<0.001); PTEN-RB1 (HR:5.5, p<0.001), and PTEN-TP53-RB1 (HR:9.0, p<0.001) were the strongest predictors of poor survival, with consistent effects across subgroups and analytical methods. Other combinations including MYC-TP53 (HR 2.2, p<0.001) and PIK3CA-TP53 (HR 3.2, p<0.001), also showed independent prognostic value. CONCLUSIONS: Genomic alterations in TP53, PTEN, RB1, and BRCA2, alone and in combination, independently associate with poor prognosis. Co-alteration patterns such as PTEN-TP53-RB1 define aggressive molecular subsets. This evidence supports integrating genomic interaction patterns for enhancing clinical risk models and guiding precision patient management. Furthermore, this study highlights the potential of AACR Project GENIE to enable data-driven development of biomarker-informed patient care strategies. Citation Format: Pablo Cresta Morgado, Victor Navarro, Guillermo Vilacampa, Haitham Alatoom, Manuel Ramos del Rio, Irbaz Riaz, Shawn Sweeney, Nikolaus Schultz, Ken Kehl, Gregory Riely, Wasim Abida, Katherine Panageas, Deborah Schrag, Philippe Bedard, Christine Micheel, Xindi Guo, Chelsea Nayan, Rodrigo Dientsmann, Joaquin Mateo. Genomic prognostic biomarkers in prostate cancer from the AACR GENIE cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A013.

Background: Transradial artery access (TRA) is the favored approach for percutaneous coronary intervention (PCI) and coronary angiography (CAG). However, transulnar artery access (TUA) has emerged as an alternative, particularly when TRA is not feasible. This systematic review and meta-analysis aims to compare the efficacy and safety of TUA versus TRA in patients undergoing PCI and CAG. Methods: From inception to August 2025, a systematic search of electronic databases was conducted. Data were synthesized using a random-effects model, and heterogeneity was assessed using the I 2 statistic. Results: Nine RCTs involving 6089 patients were included. Meta-analysis revealed no significant difference in procedural success between TUA and TRA (RR: 0.93, 95% CI: 0.82–1.06, P = 0.28, I 2 = 99%). Similarly, puncture success rates were comparable (RR: 0.95, 95% CI: 0.91–1.00, P = 0.06, I 2 = 83%). Secondary outcomes showed no significant differences in puncture attempts or hematoma incidence, but TRA was associated with a shorter procedure time (MD: 0.89, 95% CI: 0.27–1.50, P = 0.005). Conclusion: TUA provides a viable alternative to TRA, with similar success rates and complication profiles. However, TRA remains superior in terms of procedure time. Further large-scale RCTs are warranted to confirm these findings and refine clinical guidelines.

Abstract Objective High-frequency oscillations (HFOs) are highly specific biomarkers of epileptogenic tissue, yet their noninvasive localization remains challenging due to their brief duration, low amplitude, and poor signal-to-noise ratio. Here, we introduce TH-DeepSIF, a transformer-based deep learning framework trained on biologically realistic neural mass model simulations, to robustly perform HFO source imaging from scalp EEG. Methods TH-DeepSIF was evaluated in simulated single- and dual-source HFO scenarios, where it was tasked with recovering both the spatial location and temporal dynamics of HFO generators under increasing spatiotemporal complexity. We further validated TH-DeepSIF in 25 patients with drug-resistant epilepsy by comparing EEG source imaged HFO sources against surgical resection regions. Results TH-DeepSIF accurately recovered both the spatial location and temporal dynamics of simulated HFO generators, achieving low localization error and strong waveform correspondence with ground truth. TH-DeepSIF localization for pathological HFOs (pHFOs, or spike ripples) demonstrated strong agreement with surgical resection regions, achieving a median localization error of 11.9 mm and specificity of 0.896. Compared with all HFOs (aHFOs), pHFO-based source imaging showed significantly stronger spatial correspondence with resection regions, significantly smaller localization error, and higher precision, sensitivity, geometric mean, and F1 score. Significance These findings demonstrate that TH-DeepSIF provides a robust, data-driven framework for noninvasive HFO source imaging with improved anatomical specificity and enhanced clinical utility for presurgical evaluation using scalp EEG. Moreover, they show that pathological HFOs (spike ripples )—rather than general HFOs—serve as robust EEG biomarkers for accurate localization of the epileptogenic zone. Key Points A fully data-driven, parameter-free framework for noninvasive HFO source imaging using scalp EEG. Pathological HFO source maps exhibit strong spatial concordance with both the surgical resection region and the seizure onset zone. Source imaging based on pathological HFOs consistently outperforms imaging based on all detected HFOs.

BACKGROUND AND OBJECTIVES: Unruptured intracranial aneurysms (UIAs) are increasingly detected and require careful management to prevent rupture. No externally validated score currently predicts procedural risk to guide treatment decisions. We developed and validated 2 predictive scores for complications after endovascular treatment (EVT) or neurosurgical treatment (NT) of UIA using routinely collected clinical and aneurysmal features. METHODS: We conducted a multicenter retrospective study including patients with UIA treated with EVT or NT across 15 neurovascular centers (2014-2024). Predictive models were built using multivariable logistic regression, with variables derived from Delphi consensus. The primary outcome was a composite safety end point: new neurological deficits, modified Rankin Scale (mRS) worsening (≥1 point or mRS 2-5 within 30 days), or procedural death. Internal validation used bootstrapping, and external validation was performed temporally and institutionally. Model performance was assessed using area under the receiver operating characteristic curve (AUROC) and calibration. Final scores, named Morbidity and Mortality Associated Risk in the Treatment of UIAs (MARTA)-EVT and MARTA-NT, were compared with existing models identified through systematic review. RESULTS: Among 2647 patients (1907 EVT and 740 NT), procedural complications occurred in 6.3% (EVT) and 12.8% (NT). Independent predictors included age, baseline mRS, aneurysm location, size, morphology, and procedural factors. MARTA-EVT (AUROC = 0.68, 95% CI = 0.57-0.78) and MARTA-NT (AUROC = 0.65, 95% CI = 0.54-0.77) showed moderate discrimination and good calibration. MARTA-EVT outperformed existing models; MARTA-NT performed similarly to SAFETEA. Predictive models are available open-source: https://martascoreapp.shinyapps.io/martascoreapp/. CONCLUSION: MARTA-EVT and MARTA-NT are validated tools for predicting procedural risks in UIA treatment and may support patient counseling and clinical decision making.

Abstract Aims Heart failure (HF) continues to be a major cause of morbidity and mortality worldwide, placing a significant burden on healthcare systems. Differences in the outcomes of HF hospitalizations for adults in teaching vs. non-teaching hospitals in urban settings are uncertain. We evaluated outcomes of HF hospitalizations in patients hospitalized in urban teaching vs. non-teaching hospitals in the United States. Methods and results HF hospitalizations were abstracted from the 2016 through 2022 Nationwide Readmissions Database and stratified into urban teaching vs. urban non-teaching hospital settings. Propensity-score matching was used to control for baseline differences between teaching and non-teaching hospital cohorts. Logistic regression and lognormal models were estimated to assess differences in inpatient mortality, length of stay (LOS), total costs, complications, and 30-day and 90-day all-cause readmissions. A total of 7 558 299 weighted HF hospitalizations were included in the analysis, of which 76.3% involved urban teaching hospitalizations. Compared to urban non-teaching, HF hospitalizations in urban teaching hospitals were associated with significantly higher odds of inpatient mortality (OR 1.19, 95% confidence interval [CI]: 1.16–1.22), complications including cardiogenic shock, cardiac arrest, Intra-aortic balloon pump, cardiopulmonary resuscitation, extracorporeal membrane oxygenation and mechanical ventilation use as well as more extended hospital LOS, higher total costs, palliative care consultation and readmissions rates (all P < 0.001). 30-day and 90-day all-cause readmission rates were also statistically higher in teaching hospitals, though the difference was clinically non-significant. Conclusion HF hospitalizations in urban teaching hospitals were associated with higher inpatient mortality, complication rates, resource utilization, and palliative care consult rates than urban non-teaching hospitals.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

399 Background: The standard of care for resectable GEA adenocarcinomas is perioperative chemotherapy with the FLOT (fluorouracil, leucovorin, oxaliplatin, docetaxel) regimen. Recent studies investigating the addition of checkpoint inhibitors (CPIs) to perioperative chemotherapy have shown mixed results. An NMA can leverage both direct and indirect comparisons to evaluate the relative efficacies of available regimens in this setting. Methods: MEDLINE, EMBASE, Scopus, Web of Science and CENTRAL were searched until August 27th, 2025. Eligible studies included phase 3 randomized trials evaluating perioperative or neoadjuvant chemotherapy +/- CPIs ± radiation in resectable GEA. The primary outcomes were disease-free survival (DFS) and overall survival (OS). Hazard ratios and 95% confidence intervals (CIs) were estimated using a frequentist NMA framework. Results: Fifteen trials including 8072 patients were analyzed. For DFS, perioperative durvalumab + FLOT (dFLOT) ranked highest (P-score = 0.99), followed by FLOT (P-score = 0.84) (HR for dFLOT vs. FLOT was 0.71, 95% CI 0.58-0.86). dFLOT also ranked higher than perioperative pembrolizumab + cisplatin and fluorouracil/capecitabine (KN-585), though this comparison was not statistically significant (HR 0.65, 95% CI 0.40-1.04). Compared with CROSS, dFLOT showed superior DFS (HR of 0.48, 95% CI 0.36-0.63). For OS, dFLOT ranked the highest (P-score=0.95) and was superior to CROSS (HR 0.57, 95% CI 0.42-0.77). While dFLOT appeared favorable versus FLOT (HR 0.78, 95% CI 0.62-0.98), this finding was primarily driven by MATTERHORN, which did not meet its prespecified OS significance boundary (P = 0.03 vs threshold P < 0.0001), and the proportional hazards assumption was violated. Similarly, no significant OS difference was observed between dFLOT and the KN-585 arms (HR 0.75, 95% CI 0.45-1.23). Conclusions: In this NMA, perioperative FLOT remained the benchmark regimen for resectable GEA. dFLOT has shown promising early results, ranking the highest for DFS and showing favorable, but not statistically definitive OS trends in comparison to FLOT. Importantly, the network enabled indirect comparisons not tested in head-to-head trials, demonstrating that dFLOT ranked above pembrolizumab+CF/CX (KN-585) without reaching statistical significance, and also outperformed CROSS. However, these findings should be interpreted with caution, as they are primarily driven MATTERHORN data in which OS did not cross the prespecified threshold and proportional hazards assumptions were violated. Perioperative immunotherapy combinations appear promising, but final MATTERHORN results and further confirmatory studies are needed before dFLOT can be considered as a new standard of care.

103 Background: Clinicians spend significant time extracting and synthesizing information to risk-stratify and make adjuvant chemotherapy (AC) recommendations for CRC. By automating parts of this process, LLMs could help reduce documentation burden and variability in decision-making. Methods: We selected 144 stage III, pMMR CRC patients treated at Mayo Clinic (2017–2021). HIPAA-compliant Gemini-2.0-Flash-001 and GPT-4o extracted clinical variables (age, neuropathy grade, pathological stage) from EHRs and generated AC recommendations. Two approaches were compared: (1) a rule-based algorithm with nested if/else statements translating variables into NCCN-based recommendations, and (2) a dynamic LLM agent generating recommendations using a structured prompt informed by NCCN guidelines. Concordance with treating physicians was assessed, with disagreements reviewed by a blinded GI oncologist. Results: The dataset included 144 patients with median age 67 (IQR 66–68), 48.6% female. The LLM pipeline extracted neuropathy grade with 100% accuracy and pathological T/N stages with 99.3% accuracy. Concordance with physician recommendations was 93.1% for the rule-based algorithm and 62.5% for the LLM agent. In discordant cases, blinded adjudication by a GI oncologist favored the algorithm and LLM agent over physicians in most cases (Table 1). Conclusions: This exploratory work shows LLM agents can reliably extract variables and generate evidence-based treatment recommendations for stage III CRC. The high rate of expert preference for LLM recommendations in discordant cases is intriguing but limited by small sample size and possible adjudicator bias. Discordant physician recommendations reflect the complexity of decision-making, including patient preferences, performance status, comorbidities, and subtle findings not fully captured in EHRs. Rather than replacing judgment, we envision LLMs as decision support tools—analogous to an advanced trainee reviewing charts and presenting evidence-based recommendations to the attending physician with ultimate authority. Larger multi-institutional studies with multiple adjudicators are needed to validate these findings and clarify AI’s role in oncology. Patient characteristics, LLM extraction accuracy, and concordance with physician recommendations. Metric Result Total patients 144 Median age (IQR) 67 (66-68) Female (%) 48.6% LLM extraction accuracy – Neuropathy grade 100% LLM extraction accuracy – Pathological T and N stages 99.3% Primary Concordance – Rule-based algorithm vs. physician 93.1% Adjudicator preference of rule-based algorithm over treating physician for discordant cases 100% Concordance – LLM agent vs. physician 62.5% Adjudicator preference of LLM agent over treating physician for discordant cases 82.0%