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TPS8113 Background: There are currently three approved approaches for patients with resectable NSCLC including neoadjuvant chemoimmunotherapy, adjuvant chemoimmunotherapy and perioperative treatment with neoadjuvant chemoimmunotherapy followed by adjuvant immunotherapy. All regimens were approved after showing event-free survival (EFS) or disease-free survival (DFS) benefit with the addition of immunotherapy to chemotherapy compared to chemotherapy alone. Each approach has its benefits and risks. Starting immunotherapy prior to surgery may improve treatment compliance and efficacy of immunotherapy. Nevertheless, neoadjuvant chemoimmunotherapy may result in missing an opportunity for curative surgery and increase the complexity of tumor resection. PROSPECT-LUNG (NCT06632327) is a randomized study evaluating whether starting chemoimmunotherapy before or after surgery leads to better outcomes. Methods: This is a randomized phase 3 trial in which patients will be randomized 1:1 to surgery followed by chemoimmunotherapy (adjuvant arm) or neoadjuvant chemoimmunotherapy followed by surgery and adjuvant therapy (perioperative arm). Patients with histologic or cytologic confirmation of surgically resectable stage IIA-IIIB NSCLC (per AJCC 9th edition) or stage IIA to IIIB per AJCC 8 th edition up to single ipsilateral mediastinal station (N2a), ECOG PS ≤ 2 (or Karnofsky ≥ 60%), no prior treatment for NSCLC and no previous malignancy within 3 years are eligible. The dual primary endpoints are real-world event free survival (rwEFS) defined as date from randomization to date of the first of the following events: failure to undergo resection for any reason, progression prior to surgery that precludes resection, recurrence or progression at any time after surgery or death from any cause, and overall survival (OS) defined as time from randomization to death from any cause. The target accrual is 1,100 patients assuming one-sided type I error of 0.03 for OS endpoint and 0.02 for rwEFS endpoint. This sample size will enable the detection of a 3-year rwEFS improvement from 55% in the adjuvant therapy arm to 64% in the perioperative arm with an 84% power. This sample size would detect an HR of 0.73 (improvement in median OS from 8.1 to 11 years in favor of the perioperative arm, 5-year OS from 65% in the adjuvant arm to 73% in the perioperative arm, assuming exponential survival) with 83.6% power. The study has a pragmatic design with minimal data collection, reporting of adverse events that lead to discontinuation of therapy, hospitalization or death only. It allows providers to choose therapy per standard of care (FDA approved or on NCCN), includes patients with ECOG performance status 2, permits use of local laboratory testing and imaging studies and determination of recurrence/progression will be done by local treating physicians with no use of RECIST. Clinical trial information: NCT06632327 .

2030 Background: Brain metastases (BM) are common and arise in 30% of lung adenocarcinoma (LUAD) patients. Patients with LUAD that develop BM have significantly poorer outcomes, with a 10-16 month median overall survival. Unfortunately, current clinical practice for BM prediction is limited and so BM are typically detected after they develop and grow to cause neurological symptoms. Once BM are detected, currently neurosurgical tumor biopsies are performed to enable BM diagnosis via neuropathological evaluation. The aims of this study were to develop DNA methylation-based models that predict LUAD BM and non-invasively detect BM in blood to enable early diagnosis and treatment. Methods: DNA methylomes were acquired from 402 tumor tissue and plasma samples in a cohort of 346 LUAD and BM patients. Machine learning models were built using DNA methylation signatures that stratify BM risk in tissue and detect BM in plasma. Models were evaluated in independent validation datasets. A predictive nomogram was developed using the BM prediction model together with clinical factors to provide composite patient-specific scores reflecting BM risk. Results: The methylation-based BM predictor accurately stratified BM risk in a univariable Cox model using validation set data (HR = 5.65, 95%CI 1.85–17.2, p = 0.0023). Model utility was independent of the predictive value of clinical factors in a multivariable Cox model using validation set data (Table 1: HR = 8.92, 95%CI 1.97–40.5, p = 0.0046). The 5-year model accuracy was 0.81 and significantly higher than a similarly built cancer stage-based model (0.65), demonstrating utility over current practice. The combinatorial clinical-methylomic predictive nomogram had enhanced utility with an accuracy of 0.82 univariable Cox HR of 17.2 (95%CI 4.13–71.3, p < 0.0001), demonstrating comprehensive patient-specificity. The plasma-based model accurately classified BM from gliomas and lymphomas (AUROC=0.80), as typical clinical differential diagnoses, in validation set data. The models were validated further in additional external data. Conclusions: DNA methylation-based modeling of BM can accurately predict LUAD patients at risk for BM development and can non-invasively detect BM that develop. Future treatment approaches may tailor initial LUAD treatment and ongoing cancer surveillance to a patient’s BM risk, allowing for the potential to prevent and treat BM early. DNA methylation-based BM prediction is independent of clinical factors in a multivariable Cox proportional hazards model. Variable HR 95% CI p Methylome risk score 8.92 1.97–40.5 0.005 Age Years 0.96 0.92–1.02 0.177 Smoking Pack-years 0.99 0.95–1.03 0.496 EGFR Mutant vs wildtype 0.92 0.25–3.34 0.895 T T2 vs T1 1.58 0.41–6.04 0.505 T3−4 vs T1 1.49 0.28–7.98 0.642 N N1 vs N0 1.05 0.31–3.58 0.943 N2−3 vs N0 1.00 0.27–3.69 0.995 M M1 vs M0 145 12.2–1730 <0.001

e14008 Background: Lung cancer is the most common cancer to metastasize to the brain. Identifying the EGFR mutation is critical for determining treatment strategies and prognosis. However, 17.26% of patients show EGFR mutation discordance between the primary tumor and CNS metastasis. On the other hand, it is not always feasible to obtain brain samples. Many studies have developed deep learning (DL) models to predict the existence of EGFR mutations using radiomics. In this paper, we aim to measure the accuracy of diagnostic tests in those studies. Methods: PubMed, Scopus, Cochrane Library and Web of Science databases were searched from inception to January 2025, to identify studies measuring the ability of DL models in differentiating between the types of EGFR mutation in patients with lung cancer brain metastases. The quality assessment of the included studies was performed using QUIPS tool. The diagnostic accuracy meta-analysis was conducted using Meta-DiSc software. Results: Nine studies were included in our systematic review and meta-analysis, encompassing 260 patients with EGFR mutant type and 264 patients with EGFR wild type. Our analysis revealed that DL approaches exhibit a high ability to differentiate between EGFR mutation types, with a sensitivity of 0.82 (95% CI: 0.73–0.88) and a specificity of 0.88 (95% CI: 0.78–0.94). Additionally, the positive likelihood ratio and negative likelihood ratio were 6.87 (95% CI: 3.5–13.3) and 0.20 (95% CI: 0.13–0.31), respectively. The diagnostic odds ratio was 33.4 (95% CI: 13.5–82.5), which is indicative of excellent diagnostic accuracy. Conclusions: DL models demonstrate exceptional diagnostic accuracy in detecting EGFR mutations in brain metastases from lung cancer. These findings highlight the potential of AI-driven approaches to enhance personalized treatment strategies and improve outcomes for patients with EGFR-mutant NSCLC brain metastases.

10556 Background: The coexistence of interstitial lung diseases (ILD) and lung cancer (LC) presents a clinical challenge. ILD, including idiopathic pulmonary fibrosis (IPF) and non-IPF entities, is associated with an elevated risk of LC. However, the clinical features, treatment patterns, and survival outcomes of ILD-LC, especially IPF-LC, remain underexplored. We aimed to comprehensively evaluate survival outcomes and risk factors for mortality of ILD-LC patients based on a real-world long-term clinical cohort. Methods: A total of 1,341 eligible patients for this study were drawn from the Epidemiology and Genetics of Lung Cancer research program database (Mayo Clinic) including 96 IPF-LC, 111 Non-IPF ILD-LC, 99 ILD-only, and 1,035 propensity-matched LC-only patients (Controls). Data were collected on demographics, comorbidities, tumor features, treatment, and patient outcomes. Survival analyses used Kaplan-Meier methods; risk factors were assessed via Cox Proportional Hazards models. All comparisons reported below were statistically significant with p-values≤0.001 unless specified. Results: Among the 20,470 primary LC patients who were diagnosed from 1997 through 2016 and followed to 2021, 207 (1.0%) had ILD-LC, of which 96 (46.4%) had IPF. Comparing to Controls, ILD-LC patients were 4 years older at the time of LC diagnosis, 63.8% (vs. 52.8%) male, had a higher rate of former smokers (60.4% vs. 47.3%), a lower proportion of adenocarcinoma (37.7% vs. 57.5%), and a higher proportion of squamous cell (28.0% vs. 20.3%) and small cell carcinoma (13.5% vs. 8.4%). Although no significant differences in either LC stage or grade, ILD-LC patients had a significantly higher mortality rate (90.3% vs. 78.8%), particularly among IPF-LC patients (97.9% vs. 83.8%). ILD-LC patients were less likely to undergo surgery or receiving pharmacological treatments (73.9% vs. 93.5%). The median 5-year overall survival rate (OS, months) were 49.5 for Controls, 35.2 for Non-IPF ILD-LC patients, and 15.9 for IPF-LC patients. Analysis of six most common comorbidities revealed that pulmonary hypertension was associated with worse OS in ILD-LC patients compared to those without it (11.9% vs. 20.1%, P < 0.05); however unexpectedly, hypertension in ILD-LC patients was associated with better OS (21.3% vs. 14.4%) and emphysema in IPF-LC patients with better OS (7.9% vs. 3.4%, P < 0.05) when compared to those without the two comorbidities, respectively. Conclusions: This study highlights the poorer survival outcomes in ILD-LC patients, particularly IPF-LC patients, emphasizing the need for further investigation into optimized therapies and comorbidity management. Our findings also call for attention to two unexpected associations of hypertension and emphysema, warranting in-depth research on the possible biological and physiological mechanisms.

8053 Background: In the US, only 27.4% of lung cancer cases are diagnosed early, with 5-year survival rates of 63% for localized and 27% for late-stage cancers. Despite recommendations since 2012 for screening high-risk individuals with low-dose CT, uptake has been limited, and most lung cancer diagnoses occur after symptoms appear. Similarly, chest x-rays, while commonly used as an initial test to investigate patients with symptomatic suspected lung cancer, have demonstrated limited sensitivities between 50-70% and specificities over 80%. Symptoms often overlap with common conditions, making detection challenging, and studies have identified median delays of 187 days from symptom onset to diagnosis. This prolonged interval presents an opportunity for improvement. This study examines the use of the AI cancer prediction platform, C the Signs, to passively screen for lung cancer by leveraging electronic medical records (EMRs) for early lung cancer detection. Methods: Utilizing the Mayo data platform, we conducted a retrospective analysis of EMR data from 894,409 patients, including 7,395 individuals diagnosed with lung cancer. We assessed the sensitivity and specificity of the AI cancer prediction platform, in identifying patients at risk of lung cancer. Additionally, we compared the timing of lung cancer risk identification by the AI cancer prediction platform with the timing of diagnoses made by physicians to determine whether the platform enabled earlier detection. Results: The AI cancer prediction platform detected 6,749 cases of lung cancer among the 7,395 individuals diagnosed, resulting in an early detection sensitivity of 91.5%. The platform identified 423,249 false positives among the 887,014 patients who did not have lung cancer, leading to a specificity of 52.3%. Additionally, it identified the risk of a lung cancer diagnosis in 26.6% of patients up to five years earlier than the diagnoses made by physicians. Conclusions: This study highlights the potential of leveraging EMR data and AI platforms like C the Signs to enhance early lung cancer detection. Chest X-rays, with their reduced sensitivity for early-stage lesions and reliance on symptom-driven use, remain limited as a screening tool. In contrast, the AI platform achieved a sensitivity of 91.5% and identified 26.6% of cases up to five years earlier than traditional diagnoses. These findings underscore the promise of AI-based platforms as supplementary tools for improving early detection and facilitating timely intervention to enhance patient outcomes.

1015 Background: T-DXd is an antibody-drug conjugate approved for advanced HER2+/low/ultra-low breast cancer and multiple other solid tumors. T-DXd carries a rare but serious risk of ILD (incidence 12-15%), requiring frequent imaging and symptom evaluation. For > grade (G) 2 ILD, guidelines recommend permanent drug discontinuation. For asymptomatic G1 ILD, drug is held with the option for rechallenge (RC) if imaging findings resolve. Limited data exist on outcomes of RC after ILD in diverse real-world patients (pts). Methods: In this multi-center retrospective study, we analyzed pts with T-DXd related ILD treated from 2017-2024. Pts with ILD were identified via chart/ICD code review. Adjudication of T-DXd related ILD was based on treating providers’ assessment and graded via CTCAE v5. We collected pt demographics, T-DXd and steroid dosing, imaging results, and outcomes after RC. Statistical analysis was performed using Wilcoxon rank sum and Fisher’s exact tests. Results: Four centers treated 712 pts with T-DXd, with a 9.1% rate of any grade ILD (n=65). One other center reported only RC data in 18 pts with ILD. In total, 47 pts were RC; 38 after G1 ILD (81%), 9 after G2. Median (med) time to initial ILD was 145 days (d) after 1st dose (interquartile range [IQR] 78-205). Demographics for pts RC are shown in the table. Among 50 pts with G1 ILD, including pts not RC, 28/50 (56%) received steroids for a med of 36d (IQR 27-79). Radiographic improvement was seen at a med of 24d (IQR 19-63) for pts treated with steroids vs 82d (IQR 48-94) without (p<0.01); and a med of 35d (IQR 22-82) for pts RC vs 81d (IQR 68-105) for pts not RC (p=0.01). Among pts with G1 ILD, 38/50 (76%) were RC at a med of 42d (IQR 36-57) from last dose; 23/38 (61%) were dose reduced. After RC, pts remained on T-DXd for a med of 215d (IQR 60-334); 10/38 (26%) developed recurrent ILD (7-G1, 2-G2, 1-G3) at a med of 211d (IQR 47-273) from RC. No statistically significant differences were seen between ILD onset, time to RC, or demographics for pts with recurrent ILD vs not. Of the 9 pts RC after G2 ILD, T-DXd was continued for a med of 129d (IQR 49-171); 2/9 (22%) developed recurrent ILD (1-G2, 1-G3). No G5 toxicity was seen with RC. Conclusions: In this multi-center study, high RC rates were seen after G1 ILD with long duration of clinical benefit. Pts treated with steroids had faster radiographic ILD improvement, highlighting the importance of early steroid use. Among pts RC after G1 ILD, recurrent ILD rates were low, with the majority G1 and no G5 events. Notably, 9 pts with G2 ILD were RC, with a similar rate of recurrent ILD; this must be interpreted cautiously. Our large cohort data further supports the safety of T-DXd RC in diverse real-world settings. RC Pt Characteristics (n=47) n (%) or Median (IQR) Cancer typeBreastGIGyn 43 (91)3 (6)1 (2) Age (yrs) 57 (52-68) Prior # therapy lines in the advanced/metastatic setting 3 (1-5) Renal impairment (CrCl < 60 mL/min) 8 (17)

e20120 Background: In May 2024, the FDA approved tarlatamab, a Delta-like ligand (DLL3)/CD3-targeted bispecific T-cell engager, for patients with extensive-stage small cell lung cancer (SCLC) with disease progression following platinum-based chemotherapy and at least one other prior line of therapy. We aim to evaluate the real-world outcomes of agent. Methods: We conducted a retrospective multi-site single center analysis of adult patients who received tarlatamab at Mayo clinic sites including Arizona, Florida, Iowa, Minnesota, and Wisconsin. Data analysis was performed with Blue Sky statistics version 10.4. Results: Twenty patients with a median age 67 years (range 43-76) were included. Patients were predominantly women (55%) and all had a history of tobacco use. Metastatic disease commonly involved liver (55%) and CNS (60%). Majority (90%) had received prior PDL-1 therapy. 25% (5/20) of patients received prior gamma knife and in addition 10% (2/20) received whole brain radiation treatment in our cohort. Median time from CNS radiation and tarlatamab initiation was 4 months (range 3-28). Other characteristics, outcomes, and toxicity are presented in Table 1. Cytokine release syndrome (CRS) Grade (G) 1 was observed in 45% patients. G 2, 3 and 4 CRS were observed in 5% patients respectively. G 1 CRS was managed with acetaminophen initially followed dexamethasone 10mg or methylprednisone 1g if no improvement in 24 hours. Tocilizumab was used 20% patients for G 1,2,3 and 4 CRS depending on clinical course. Immune effector cell-associated neurotoxicity (ICANS) G 1 occurred in 10% patients, followed by G 2 in 15% and G 3 in 5% patients. ICANS was managed in a multidisciplinary fashion. All patients had resolution of CRS and ICANS. Conclusions: In this study investigating the real-world outcomes and toxicity of tarlatamab, a higher incidence of ICANS was observed than that reported in the DeLLphi-301 trial. The association between presence of CNS metastatic disease, prior CNS radiation and incidence of ICANS needs to be explored further. Mayo Clinic report of tarlatamab real world outcomes and toxicity. Patient Characteristics, Tarlatamab Outcomes and Toxicity Mayo Clinic Report DeLLphi-301 trial- Part 1 & 2 DeLLphi-301 trial- Part 3 ECOG no. (%) 2-4 4 (20) 0 (0) 0 (0) Metastatic Disease no.(%) 19 (95) 98 (98) 32 (94) Median no. of previous lines of therapy (range) 3 (2-4) 2 (1-6) 2 (2-6) Objective Response (%) 30% 40% CRS no (%) 12 (60) 49 (49) 19 (56) ICANS no (%) 6 (30) 7 (7) 4 (12) Neutropenia Grade 3 no (%) 2 (10) 6 (6) 2 (6) Fatal CRS, ICANS or Neutropenia 0 (0) 0 (0) 0 (0)

e20011 Background: The therapeutic landscape of lung cancer is rapidly evolving, presenting oncologists with the challenge of staying updated amidst an overwhelming influx of data. Clinical decision support (CDS) tools, including artificial intelligence (AI) and large language models (LLMs), may help bridge this gap. Evaluating the accuracy of LLMs in complex, real-world oncology scenarios is crucial to understanding their potential. Methods: Twenty-five de-identified lung cancer cases from the fellows’ clinic at Karmanos Cancer Institute, Detroit, MI, were analyzed. Two LLMs, GPT-4 (OpenAI) and Claude Opus (Anthropic), were assessed using advanced prompting techniques like persona-based and chain-of-thought prompting. Five board-certified lung cancer oncologists from NCI-designated centers evaluated LLM-generated responses based on accuracy, treatment recommendation comprehensiveness, and supportive care planning, using a 1–5 scale. Novel insights, the presence of fabricated information, and harmful recommendations were flagged as binary outcomes. Oncologists were blinded to the LLM source and actual treatment decisions. Results: Table 1 presents patient characteristics. GPT-4 achieved an average accuracy score of 4.2 (95% CI, 3.9–4.4), with 3.7 for comprehensiveness of medical/surgical treatment recommendations and 3.7 for supportive care planning. Six responses (32%) were flagged as potentially harmful, and two (8%) contained inaccuracies. Sixteen GPT-4 responses (64%) were rated trustworthy as a CDS tool. Claude Opus had an average accuracy score of 3.6 (95% CI, 3.1–4.1), scoring 3.6 for treatment recommendation comprehensiveness and 3.5 for supportive care planning. Nine responses (36%) were flagged for potential harm, and five (20%) included inaccuracies. Eleven Claude responses (44%) were deemed trustworthy. Significant differences were observed in accuracy (p=0.04) and trustworthiness (p=0.03) between models using McNemar's test. Other factors showed no statistical significance. Conclusions: GPT-4 outperformed Claude Opus in accuracy and trustworthiness, but both models demonstrated limitations, including harmful recommendations and inaccuracies. These findings highlight the need for improved LLM refinement before routine use as CDS tools in lung cancer treatment. Patient demographics and clinical characteristics. Category subcategory Number Median Age (range)- Yr 65 (26-78) Female 7 Male 18 Histology Adenocarcinoma 10 Squamous Cell Carcinoma (SCC) 7 Small Cell Carcinoma 6 Poorly Differentiated 2 Total 25 Stage NSCLC Stage 3 7 NSCLC Stage 4 13 Small Cell limited stage 3 Small Cell Extensive Stage 2

e20041 Background: Paraneoplastic syndromes (PNS) are caused by underlying malignancy but not due to metastasis, or treatment related side effects. Studies are lacking which target PNS seen in non small cell lung cancer (NSCLC) Methods: Systematic search of PubMed & Scopus was conducted to include all case reports & case series of adult NSCLC patients with PNS, published globally from inception upto July 7, 2024. Patient demographics, geographical distribution, NSCLC histology, clinical manifestations, diagnosis, treatment was extracted. Quality was assessed by JBI's critical appraisal checklist. Results: 138 publications (136 case reports, 2 case series) describing 142 patients published in 30 countries were included. Majority patients were male (69.7%) with mean age 57 years. Top publishing countries were USA (28.2%), Japan (13%), China (6.5%), Germany (5%), with publications (n=16) peak seen in 2020. Histological type of NSCLC with PNS was adenocarcinoma (39.8%), SCC (26.8%), unspecified histology (13%) & large cell (8.6%). Most common PNS seen in NSCLC were hypertrophic osteoarthropathy (7.2%), bilateral diffuse uveal melanocytic proliferation (5.7%), leukemoid reaction (4.3%) & limbic encephalitis (4.3%). Conclusions: Study provides bird’s eye view of all PNS found in NSCLC, aiding in timely diagnosis & treatment of NSCLC. Organ system PNS Cases Neurology Limbic encephalitisOpsoclonus myoclonus syndromeGBSSensorimotor peripheral neuropathyCerebellar degenerationEncephalomyelitisPerson in barrel syndromeMyoclonusPandysautonomiaNeuromyelitis opticaOpsoclonusAntiHu Ab neuropathyMononeuritis multiplexMorvan syndrome 63221111111111 Cardiology & haematology Leukemoid reactionLeukocytoclastic vasculitisHSPDICITPMyelofibrosisAcral vascular cyanosisHyper eosinophiliaHeart blockTrousseau syndromeHaemophilia ASVC syndromeChurg Strauss syndrome 6332211111111 Endocrinology SIADHCushing syndromeHypercalcemia of malignancyGynaecomastiaβ HCG secretionErectile dysfunction 433221 Rheumatology Hypertrophic osteoarthropathyPolyarthritisPolymyositisDermatomyositisMyasthenia gravisLambert Eaton syndromePolymyalgia rheumaticaSclerodermaRaynaud phenomenonAnti synthetase syndrome 10433311111 Dermatology Bazex syndromeBullous pemphigoidErythema gyratumLeser Trélat Tripe palmsErythrodermaIchthyosisAcanthosis nigricansRhinophymaGranuloma annularePityriasis rubria pilarisEczemaErythema induratumHyperhidrosis 42212111111111 Ophthalmology Bilateral diffuse uveal melanocytic proliferationCancer associated retinopathyCicatrizing conjunctivitisRetinal detachmentOrbital inflammationOcular flutterProptosis 8311111 Nephrology Nephrotic syndrome 3 Gastroenterology Pseudo achalasiaVerner Morrison syndromeGranulomatous visceral neuronopathy 111 Musculoskeletal Necrotizing myopathy 2

Abstract Vaccination is increasingly acknowledged as an effective preventive measure not only against specific infections, but also for the prevention of cardiovascular disease in high-risk patients. Specifically, a growing body of evidence suggests that vaccines against influenza, SARS-CoV-2, respiratory syncytial virus, herpes zoster, and other viruses significantly reduce infection and for influenza the incidence of major adverse cardiovascular events in vaccinated individuals. This clinical consensus statement examines the existing literature and accumulated evidence and offers practical clinical advice on vaccination timing and target demographics, specifically addressing complex clinical scenarios with a focus on cardiovascular conditions. It includes guidelines for vaccinating vulnerable populations such as immunosuppressed individuals, patients with congenital heart disease, and pregnant women as well as safety and potential complications of the procedure.