Citations

5108 Background: We previously proposed a hybrid framework combining LLMs and rule based algorithm (RBA) for automating risk stratification in locPCa. Herein, we aim to validate the risk stratification agent (RSA) in a prospective cohort, develop & evaluate the treatment recommendation agent (TRA) based on NCCN guidelines, and develop an interactive interface to facilitate clinicians for accurate risk stratification and treatment recommendations at the point of care. Methods: This study included pts with locPCa (2004-2024) presenting at Mayo Clinic with at least 1 positive prostate biopsy and MRI report available. For RSA prospective validation, GPT4 extracted key phenotypic variables (PSA, T stage, prostate volume, number of cores, Gleason patterns, grade group) from unstructured MRI and biopsy reports using a zeroshot prompt. An RBA then classified pts into NCCN risk groups. The agent performance was compared with the treating clinician's documentation and evaluated against gold-standard labels manually annotated by two independent clinicians. For development of TRA, two experiments were performed using GPT4 with and without retrieval-augmented generation (RAG) to generate treatment plans. Generated treatment plans were evaluated using NCCN guideline-informed treatment decision tree based algorithm (DTA). Evaluation metrics, weighted – accuracy (acc) and F1, were computed. A clinician facing interface (lisr.org/risk) was developed to provide accurate risk stratification and treatment plans. Results: A total of 858 pts were included (500 for prospective validation, 358 for treatment recommendations). Prospective validation for RSA demonstrated a higher F1 score of 0.89 compared to the treating clinician (F1: 0.58). Treatment recommendation experiments showed that GPT4 with RAG achieved higher acc (64% full – all correct treatment options and 36% partial – at least 1 correct treatment option) compared to GPT4 alone (35% and 65%, respectively). Sensitivity analysis using DTA-informed GPT4 note generation achieved 94% full acc and 6% partial acc. GPT4 alone generated hallucinated treatment options in 71% of cases, while GPT4 with RAG reduced this to 32%. Conclusions: The multi-agent framework based on LLM and RBA achieves high accuracy in risk stratification and treatment recommendation for locPCa. A multi-agent framework with an interactive interface holds high promise to enable efficient, accurate decision-making, and improve locPCa management at the point of care Performance evaluation. RSA(acc; F1) % TRA(full acc; partial acc) % Clinician RSA* GPT4 RAG+GPT4 DTA+GPT4 Overall 60; 58 89; 89 29; 71 64; 36 94; 6 V. High 94; 33 96; 96 55; 45 69; 31 93; 7 High 46; 52 92; 89 - - - Int. Unf 80; 61 91; 90 5; 95 38; 62 93; 7 Int. Fav 57; 64 84; 87 0; 100 98; 2 100; 0 Low 57; 65 87; 80 100; 0 95; 5 90; 10 V. Low 8; 13 50; 67^ - - - *Statistical significance (p<0.01) except for ^V. Low.

5064 Background: FoundationOneCDx (F1CDx) supports two FDA-approved biomarkers to guide treatment decisions for ICI for patients with mCRPC: MSI status and TMB. MSI-H and TMB-H (10+ mut/MB) have strongly overlapping prevalence. We sought to better characterize ICI outcome associations of TMB-H / non-MSI-H population and relative effectiveness of taxanes & ICI among patients who received these agents in sequence. Methods: Following a prespecified analysis plan, this study used the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine mCRPC clinico-genomic database (FH-FMI CGDB), with data originating from ~280 US cancer clinics (~800 sites of care). Inclusion criteria included patients with mCRPC treated with single-agent anti-PD(L)1 therapy in the FH network between 1/1/2011 - 3/30/2024. This study used the MSI and TMB algorithms from the tissue based F1CDx. Time to next treatment (TTNT) and OS were assessed with Kaplan-Meier plots and in multivariable Cox models adjusted for ECOG performance score, socioeconomic status, prior treatment history, and baseline PSA. Among patients who received taxanes in a prior line of therapy, the effectiveness (TTNT1 vs. TTNT2) of taxane and ICI were compared. Results: Among 2995 prostate cancer tissue specimens in the database, 95 (3.1%) were MSI-H and 142 (4.7%) were TMB-H. 94 (3.1%) were MSI-H & TMB-H, 1 was MSI-H & TMB-L, and 48 (1.6%) were TMB-H & not MSI-H. Among these, 84 patients with mCRPC were treated with ICI and met inclusion criteria, including MSI-H & TMB-H (n = 30), non-MSI-H & TMB-H (n = 8), and non-MSI-H and TMB-L (n = 46). The respective median TTNT on ICI was 8.0 vs. 9.6 vs. 3 months. The respective median OS from initiation of ICI was 10.9 vs. not reached vs. 4.4 months. In multivariable models evaluating ICI only, compared to non-MSI-H & TMB-L, the MSI-H & TMB-H group had more favorable TTNT (HR: 0.20, 95%CI: 0.10 – 0.41, p < 0.001) and OS (HR: 0.33, 95%CI: 0.16 – 0.70, p = 0.004), and the non-MSI-H & TMB-H group also had more favorable TTNT (HR: 0.13, 95%CI: 0.04 – 0.45, p = 0.002) and OS (HR: 0.20, 95%CI: 0.05 – 0.75, p = 0.017). 50 of the 84 (60%) patients treated with ICI had prior mCRPC taxane treatment. Better TTNT2 on subsequent ICI vs. prior taxane was observed for MSI-H (HR: 0.49, 95%CI: 0.23 – 1.01, p = 0.051) and TMB-H (0.54, 95%CI: 0.30 – 0.98, p = 0.044), but the opposite was true for non-MSI-H and TMB-L subgroups, with significant treatment interactions for each (p = 0.0018, p = 0.00052). Conclusions: TMB-H (4.7%) is more prevalent than MSI-H (3.1%) by F1CDx in mCRPC. Non-MSI-H / TMB-H (1.6%) in the routine practice cohort have similar outcome associations on ICI to MSI-H. Both MSI-H and TMB-H by F1CDx are predictive of differential benefit for ICI vs. taxanes in later mCRPC treatment lines.

5060 Background: Rucaparib significantly improved radiographic progression-free survival (rPFS) in men with BRCA-mutated chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC) vs a control arm of physician’s choice of therapy (docetaxel or androgen-receptor pathway inhibitor [ARPI] therapy: abiraterone acetate or enzalutamide) in the randomized, multicenter, open-label, phase 3 TRITON3 (NCT02975934) study. Herein, we conducted an analysis to determine the impact of germline vs somatic mutation status on the efficacy of rucaparib. Methods: Patients were randomized 2:1 to receive rucaparib 600 mg BID or physician’s choice of docetaxel or ARPI following progression while on 1 prior second-generation ARPI in any setting. The primary endpoint was rPFS. Color Health did genetic testing. Treatment-emergent adverse events (TEAEs) were reported for the BRCA subgroup. Results: In the rucaparib arm, 201/270 patients had BRCA mutations, and in the physician’s choice arm, 101/135 patients had BRCA mutations. Of patients with BRCA mutations: in the rucaparib arm, 80/201 (40%) were germline and 116/201 (58%) were somatic with 5/201 (2%) BRCA mutation status unknown, while in the physician’s choice arm, 39/101 (39%) were germline and 48/101 (48%) were somatic with 14/101 (14%) BRCA mutation status unknown. rPFS was significantly improved with rucaparib treatment vs physician’s choice in both the germline and somatic mutation groups (germline: HR, 0.52 [95% CI, 0.32–0.84]; somatic: HR, 0.38 [95% CI, 0.25–0.59]). Incidence rates of TEAEs were similar overall between patients with germline and somatic BRCA mutations in both arms. Conclusions: Rucaparib improves progression-free survival for patients with mCRPC with either germline or somatic BRCA mutations with a manageable safety profile. These data support the use of rucaparib as a beneficial treatment option for patients with BRCA-mutated mCRPC with germline or somatic mutations. Clinical trial information: NCT02975934 .

5041 Background: Prior studies suggest that chemotherapy-naïve pts with mCRPC treated with AA have a higher CV event-related hospitalization risk than those treated with ENZA. To further explore this association, this real-world, comparative causal inference study used a large US dataset to assess CV event risk in chemotherapy-naïve pts with mCRPC who initiated treatment (Tx) with ENZA or AA and provide outcome data based on history of (h/o) CV disease (CVD). Methods: Using US Medicare fee-for-service claims (Jan 2010–Dec 2022), we identified chemotherapy-naïve pts aged ≥65 years with mCRPC who initiated ENZA or AA between Sep 2014 and May 2017. The primary endpoint was a 4-point major adverse CV event (MACE-4; composite of acute myocardial infarction [AMI], stroke, unstable angina/revascularization [UA/R], and heart failure). Atrial fibrillation (AFib), venous thromboembolism (VTE), and all-cause death were also analyzed. Groups were propensity score matched (PSM) to adjust for differences in pt characteristics, assessed using standardized mean difference (SMD). Cause-specific Cox proportional hazards models were used to comparethe risk of CV outcomes between intention-to-treat cohorts, with death as a competing event. Subgroup analyses were conducted based on h/o CVD. Sensitivity analysis was performed with a MACE-5 endpoint, defined as MACE-4 or CV-related death. Results: Of 6319 pts in the total study population (ENZA: 2934; AA: 3385), 2913 PSM pts were included from each group. The ENZA and AA cohorts had similar baseline characteristics even before PSM (SMD<0.1), with a mean (standard deviation) age of 78.8 (7.3) years; 76% of pts had prior CVD. Compared with pts on ENZA, pts on AA had a significantly higher risk of experiencing MACE-4—particularly UA/R—as well as a higher risk of AFib and VTE (Table). Similar results were found in the subgroup of pts with a h/o CVD and the sensitivity analyses. Additionally, pts on AA had a higher risk of all-cause death than pts on ENZA, regardless of CVD history (h/o CVD hazard ratio [HR]: 1.14, 95% confidence interval [CI]: 1.07–1.21, P =0.0001; no h/o CVD HR: 1.12, 95% CI: 1.01–1.26, P =0.036). Conclusions: This matched analysis of US Medicare beneficiaries showed an increased risk for MACE-4, AFib, and VTE in pts with mCRPC treated with AA compared to ENZA, in the overall pt population and pts with a h/o CVD. The risk of all-cause death was higher with AA in all pts. These findings provide insights into Tx decision-making for pts with mCRPC, especially those at high risk of CV events. Outcomes HR* (95% CI) P -value MACE-4 1.12 (1.02–1.24) 0.028 AMI 1.01 (0.76–1.32) 0.987 Stroke 0.92 (0.70–1.20) 0.505 UA/R 1.13 (1.01–1.26) 0.041 Heart failure 1.19 (0.90–1.58) 0.237 AFib 1.73 (1.31–2.29) 0.0001 VTE 1.37 (1.02–1.85) 0.037 All-cause death 1.13 (1.07–1.19) 0.0001 *ENZA = reference group.

ASCO Guidelines provide recommendations with comprehensive review and analyses of the relevant literature for each recommendation, following the guideline development process as outlined in the ASCO Guidelines Methodology Manual . ASCO Guidelines follow the ASCO Conflict of Interest Policy for Clinical Practice Guidelines . Clinical Practice Guidelines and other guidance (“Guidance”) provided by ASCO is not a comprehensive or definitive guide to treatment options. It is intended for voluntary use by clinicians and should be used in conjunction with independent professional judgment. Guidance may not be applicable to all patients, interventions, diseases or stages of diseases. Guidance is based on review and analysis of relevant literature, and is not intended as a statement of the standard of care. ASCO does not endorse third-party drugs, devices, services, or therapies and assumes no responsibility for any harm arising from or related to the use of this information. See complete disclaimer in Appendix 1 and 2 (online only) for more . PURPOSE To provide evidence-based recommendations for patients with metastatic castration-resistant prostate cancer (mCRPC). METHODS An Expert Panel including patient representation completed a systematic review of the evidence and made recommendations. RESULTS Depending upon prior treatment received, androgen receptor pathway inhibitors (ARPIs: enzalutamide, abiraterone with prednisone), poly(ADP-ribose) polymerase inhibitors (PARPi), chemotherapeutic agents (docetaxel, cabazitaxel), radiopharmaceuticals (radium 223, 177 Lu-prostate-specific membrane antigen [PSMA]-617), and sipuleucel-T have demonstrated an overall survival (OS) benefit for patients with mCRPC. For patients with BRCA1 / 2 alterations who did not receive prior ARPI, the combination of PARPi and ARPI (talazoparib + enzalutamide, olaparib and/or niraparib + abiraterone) has shown clinical benefit. For patients with BRCA1 / 2 alterations who received prior ARPI or ARPI followed by docetaxel, olaparib showed OS benefit. In select patients with microsatellite instability-high/mismatch repair-deficient, pembrolizumab showed clinical efficacy. RECOMMENDATIONS Prior systemic therapy for castration-sensitive prostate cancer will determine subsequent therapy used for mCRPC. Continue androgen-deprivation therapy for patients with mCRPC indefinitely. Early adoption of somatic genetic testing and palliative care is recommended. Patients with mCRPC and bony metastases should receive a bone-protective agent. The panel recommends the combination of ARPI with PARPi in patients with BRCA1 / 2 alterations who did not receive prior ARPI. For patients who received prior ARPI, the panel recommends docetaxel chemotherapy. The panel recommends 177 Lu-PSMA-617 or cabazitaxel chemotherapy for patients who receive prior ARPI and docetaxel chemotherapy. For patients with BRCA1 / 2 alterations who received prior ARPI, the panel recommends PARPi monotherapy. Radium 223 is recommended for patients with symptomatic bone–only disease. Evidence for optimal sequencing for mCRPC regimens is lacking. Additional information is available at www.asco.org/genitourinary-cancer-guidelines .

ABSTRACTBackgroundThis study investigates the association between thromboembolic events (TE) and castration‐resistant prostate cancer (CRPC) patients receiving oral androgen receptor pathway inhibitors (ARPi) compared to those undergoing chemotherapy, both with and without a pre‐existing history of cardiovascular disease (CVD).MethodsA total of 2779 men diagnosed with CRPC were identified using the Surveillance, Epidemiology, and End Results (SEER) Medicare Linked Database from 2012 to 2016. Patients were stratified based on their CVD history. Within each CVD stratum (pre‐existing CVD vs. no pre‐existing CVD), patients were further categorized into two treatment groups: those receiving oral ARPi and those undergoing chemotherapy. Unadjusted and inverse probability treatment weight (IPTW)‐adjusted proportional hazards models, using Fine and Gray's method, were applied to evaluate the potential association between ARPi treatment and TE.ResultsPatients with pre‐existing CVD treated with ARPi exhibited a significantly lower crude hazard ratio (HR) for TE compared to chemotherapy (HR 0.39, 95% CI 0.27–0.58, p < 0.001). However, after adjustment using IPTW, this association was no longer significant (adjusted hazard ratio [AHR] 1.00, 95% CI 0.75–1.32, p = 0.99). For patients without CVD, ARPi use was also associated with a reduced risk of TE in the crude analysis (HR 0.53, 95% CI 0.32–0.87, p = 0.01), but this effect was not statistically significant after IPTW adjustment (HR 0.99, 95% CI 0.69–1.41, p = 0.94).ConclusionARPi demonstrated no significant effect on TE risk compared to chemotherapy, regardless of pre‐existing CVD status. Similarly, when excluding patients with a prior history of TE, ARPi use remained non‐significantly associated with new TE in the IPTW‐adjusted competing risk analysis, highlighting the need for further investigation.

261 Background: Clinicians use next-generation sequencing (NGS) to tailor targeted treatments for PCa patients. However, the results are often available in unstructured formats and are saturated with technical jargon, making them inaccessible for patients. Therefore, we aimed to assess the efficacy of LLMs to accurately extract genomic characteristics from unstructured reports and generate patient-friendly summaries for ease of variant interpretation in PCa patients. Methods: This retrospective study included patients with PCa who underwent NGS from 2023-2024. The GPT4 model was utilized using structured zero-shot prompts to extract genomic characteristics from unstructured reports in a non-decomposed manner. Prompt development was conducted iteratively using a 2% random sample of the dataset. Genomic characteristics ascertained by LLM were assessed against held out vendor-curated test dataset for evaluation. Performance was assessed using weighted evaluation metrics (precision, recall, and F1-score). Additionally, the extracted variables were organized using rule-based criterion and presented as a separate prompt to GPT4 to generate summaries for interpretation at the level of each biologically relevant variant. Mean Flesch-Kincaid scores and Vocd-D statistic with standard deviations were computed to assess readability and lexical diversity of LLM-generated variant interpretation summaries. Results: A total of 331 patients (370 NGS reports) were included in the study. Evaluation on the held-out test dataset (Table) showed that the weighted average precision ranged from 80% for extracting tumor mutational burden to 100% for extracting the CtDNA fraction from unstructured reports. The weighted average recall ranged from 85% for extracting CtDNA tumor fraction to 99% for ascertaining variant/alteration. The weighted average F1-scores varied from 0.83 for extracting tumor mutational burden to 1.0 for extracting specimen site from NGS reports. The mean Flesch-Kincaid score was 8.29 ± 1 indicating average/standard readability ease. The mean Vocd-D statistic was 7.55 ± 1 indicating focused summaries, however, with limited lexical diversity. Conclusions: This study suggests that large language models can effectively extract genomic characteristics from unstructured reports and can potentially improve genomic literacy among prostate cancer patients by providing easy-to-interpret variant summaries. However, targeted prompting may be required to increase lexical diversity for improved engagement. Performance across different genomic characteristics. Category Precision Recall F1-Score Altered gene 0.86 0.97 0.89 Variant/alteration type 0.98 0.99 0.98 Variant allele fraction 0.96 0.96 0.96 Test name/type 1 0.99 1 Specimen site 1 0.99 1 Tumor mutational burden 0.8 0.93 0.83 CtDNA tumor fraction 1 0.85 0.92 MSI status 0.99 0.97 0.98

270 Background: Inconsistent reporting of subgroup data across clinical trials makes interpretation and application of the evidence from trials challenging. We conducted a living systematic review to summarize evidence for American Society of Clinical Oncology (ASCO) guidelines for management of metastatic castration resistant prostate cancer (mCRPC). Here, we provide an overview of reporting patterns of subgroup data across mCRPC trials. Methods: MEDLINE and EMBASE were systematically searched from each database's inception through September 20 th , 2024, to identify phase II/III/IV randomized clinical trials assessing the efficacy and safety of treatment options in mCRPC. For outcomes of overall survival (OS) and progression free survival (PFS), data from the subgroup analyses was collected and compared across trials to assess heterogeneity in the reporting of subgroups. Results: This analysis included 141 trials (phase II: 83; phase III: 56; phase IV: 2). OS was reported by 113 (80%) trials; 32 (28%) reported OS by any subgroups with 95 unique subgroup categories. PFS was reported by 110 (78%) trials; 23 (21%) reported PFS by any subgroups with 63 unique subgroup categories. Even among consistently reported subgroups, such as age, ECOG performance status, race, geographical area, and Gleason score, considerable heterogeneity in different subgroup levels/thresholds was observed across trials. Top ten most commonly reported subgroups are summarized (Table). Conclusions: Lack of consistent reporting for subgroup analysis in mCRPC trials preclude meaningful conclusions about clinically relevant subgroup and synthesizing evidence for clinical guidelines. A standardized framework for consistent reporting of subgroup data across trials can help inform clinical practice and clinical practice guidelines. OS PFS Number of trials 113 110 Reporting subgroup analysis – N (%) 32 (28) 23 (21) Commonly reported subgroups – (N) Age (26)ECOG-PS (26)LDH (21)PSA (18)Geographic area (15)Alkaline phosphatase (14)Visceral disease (14)Gleason score (9)Race (9)Brief pain inventory level (8) Age (21) ECOG-PS (19)PSA (18)LDH (14)Geographic area (13)Visceral disease (11)Gleason score (10)Alkaline phosphatase (9)Bone metastasis (9)Disease location (9) Age – (N) <65 (14); ≥65 (9); ≤68 (1); ≥69 (1); <70 (5); ≥70 (5); ≤74 (4); 65-75 (5); ≤ 71 (1); >71 (1); ≥75 (11); >Median (1); <Median (1) <65 (12); 65-74 (6); ≥65 (6); <70 (5); ≥70 (5); <75 (4); ≥75 (10); 76-80 (1); >80 (1) ECOG-PS – (N) 0 (15); 0-1 (11), 1 (11); 1-2 (5); 2 (12) 0 (15); 0-1 (5), 1 (12); 1-2 (2); 2 (6) Race – (N) White (9)Non-white (6)African American/black (2)Asian (2) White (8)Non-white (5)African American/black (2)Asian (4) Geographic Area – (N) Europe (8)North America (14)United States (1)Australia (1)Asia (2) Europe (11)North America (13)United States (1)Australia (1)Asia (2)North and South America (2) Gleason Score – (N) 2-6 (1); 7 (1); 8 (1); <8 (8); ≥ 8 (8); 9-10 (1) <8 (10); ≥ 8 (10)

134 Background: Prostate cancer patients with visceral metastases at diagnosis are often treated using one-size-fits-all approach. For example, CHAARTED criteria treats any visceral metastasis as high volume. Therefore, we aimed to assess overall survival (OS) by site of visceral involvement in patients with metastatic PCa. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2010-2021) was queried to obtain case listing data on metastatic PCa patients from 17 registries. The patients were categorized into three groups based on visceral metastasis at diagnosis: lung metastasis, liver metastasis, and others. Kaplan-Meier survival analysis was performed to estimate median OS with 95% confidence interval (CI). Log-rank test was conducted to assess if survival was significantly different among the groups. Cox proportional hazard regression analysis was performed to assess the magnitude of difference among the groups. P-value <0.05 established statistical significance. Results: This analysis included 21,007 metastatic PCa patients (lung: n=319; liver: n=123; brain: 107; bone: 20538). Metastatic PCa population with lung metastasis exhibited a median OS of 60 months (95% CI: 48-72) compared to 24 months (95% CI: 12-36) in population with liver metastases and to 36 months (95% CI: 36-36) in population with other metastases. The difference in OS among the groups was statistically significant (p < 0.001). Compared to population with lung metastasis, those with liver (HR: 1.91; 95% CI: 1.41-2.57) but not those with other metastases (1.16; 0.97-1.39) had significantly worse OS adjusting for Gleason score, PSA and race (Table). Conclusions: In metastatic PCa, patients with lung-only metastasis have significantly better overall survival and should be considered differently in prognostication to liver metastases. Consideration of site of visceral metastasis is critical for treatment intensification decisions. Limitations of this analysis include lack of accounting for confounding relationships at individual patient level. Variable Hazard Ratio (95% CI) Metastases (Lung) REFERENCE Metastases (Liver) 1.91 (1.41-2.57) Metastases (Other) 1.16 (0.97-1.39) Gleason score≥8 REFERENCE Gleason score <8 0.68 (0.64 -0.72) PSA - ng/dL 1.01 (1.00 – 1.01) Race (White) REFERENCE Race (Black) 0.99 (0.94 – 1.05) Race (Asian or Pacific Islander) 0.70 (0.64 – 0.76)

132 Background: Distant metastasis at the time of prostate cancer (PCa) diagnosis is associated with increased morbidity and mortality. This study aimed to assess the impact of sociodemographic characteristics on distant metastasis at the time of PCa diagnosis. Methods: The Surveillance Epidemiology and End Results (SEER) database (2000-2021), including PCa patients from 17 registries, was queried. Patients with late-stage diagnosis (LSD) were defined as those who were initially diagnosed with stage IV disease, indicating distant metastasis. Sociodemographic predictors included race, ethnicity, age, marital status, residential status, and median household income. Multicollinearity was assessed using variance inflation factors (VIF). Univariable logistic regression was performed to identify significant predictors of LSD, which were further analyzed in multivariable logistic regression using a maximum likelihood estimation approach. Odds ratios with 95% confidence intervals (CI) were computed with a p-value of <0.05 indicating a statistically significant association. Results: Of the 1,089,736 PCa patients diagnosed, 64,983 (6%) had an LSD. The majority were white (80%), non-Hispanic/Latino (HL) (91%), aged ≥65 years (60%), married (75%), residents in metropolitan areas (88%), and had an annual house income of ≥$70,000 (70%). Multivariable logistic regression identified Black (OR: 1.24; 95%CI: 1.22-1.27), Asian or Pacific Islander (1.35; 1.30-1.40), and American Indian/Alaska Native (1.70; 1.53-1.89) to have higher odds of LSD compared to White patients (Table). In addition, HL ethnicity compared to non-HL (OR: 1.45; 95% CI: 1.42-1.49), age ≥65 compared to <65 (1.68; 1.65-1.71), unmarried status compared to married (1.88; 1.85-1.91), residency in non-metropolitan areas compared to metropolitan (1.10; 1.07-1.12), and household income <$70,000 compared to ≥$70,000 (1.03; 1.01-1.05) had a significant association with LSD. Conclusions: Older, non-White (Black, Asian/Pacific Islander, American Indian/Alaska Native), Hispanic/Latino, unmarried men, residents of non-metropolitan areas, or those with lower annual household incomes have higher odds of late-stage diagnosis. Interventions targeting early detection in these at-risk groups may improve prostate cancer outcomes in the US. Variable Odds Ratio (95% CI) p-value Race (Black vs White) 1.24 (1.22-1.27) <0.01 Race (Asian or Pacific Islander vs White) 1.35 (1.31-1.40) <0.01 Race (American Indian/Alaska Native vs White) 1.70 (1.53-1.89) <0.01 Ethnicity (Hispanic/Latino vs non-/Hispanic/Latino) 1.45 (1.42-1.49) <0.01 Age (≥65 vs <65) 1.68 (1.65-1.71) <0.01 Marital status (unmarried vs married) 1.88 (1.85-1.91) <0.01 Residence status (non-metropolitan vs metropolitan) 1.10 (1.07-1.12) <0.01 Household income (<$70,000 vs ≥$70,000) 1.03 (1.01-1.05) <0.01