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745 Background: The combination of EV-P therapy has recently been approved as first-line (1L) therapy for patients with LA/mUC based on the results of the EV-302 trial. Herein, we report the updated results from a cohort of patients with LA/mUC who received EV-P in real world clinical setting. Methods: This retrospective study included patients with LA/mUC (07/2022-08/2024) at the Mayo Clinic who completed at least one cycle of EV-P. The best overall response (BOR) was evaluated using radiographic imaging and was adjudicated as complete response (CR), partial response (PR), stable disease (SD), mixed response (MR), or progressive disease (PD). The median progression-free survival (PFS), defined as the time from treatment initiation to disease progression or death, was estimated using the Kaplan-Meier method. Patients who had not experienced disease progression by their last follow-up were censored for the PFS analysis. Results: A total of 120 patients were included; 79 (65.8%) were males, 41 (34.1%) females, 109 (91%) White, and 119 (99%) were non-Hispanic/Latino. The median age was 72 years (IQR: 65-77). Of these, only 22 (18.3%) had locally advanced disease while the remaining had mUC; 62 (51.6%) had lower tract urothelial carcinoma (LUTC), 36 (30%) had upper tract urothelial carcinoma (UTUC). The median follow-up time was 7.1 months (IQR: 4.93-9.26). In terms of BOR, 56 patients (46.6%) experienced PR, 34 (28.3%) CR, 12 (10%) PD, 7 (5.8%) MR, and 8 (6.6%) experienced SD. 3 (2.5%) patients were not evaluable. The BOR across subgroups, LA and mUC, including LUTC and UTUC, is summarized in the table. Among the responders, the median number of treatment cycles to first response was 3 (IQR: 2.25–4). Within the follow-up, 28 patients (23.3%) experienced eventual radiographic disease progression; death was observed in 25 patients (20.8%). The median PFS was 12.7 months (95% CI: 9.8-NE), and OS was 25.1 months (16.3-NE). Conclusions: In real world setting, the combination of EV-P in LA/mUC demonstrated clinical efficacy compatible with the findings from the EV-302 trial. Our results showed comparable efficacy with EV-P in both UTUC and LUTC, highlighting it's broad therapeutic potential. However, clinical data at longer follow-up is required to fully evaluate overall survival and the durability of response. mUC LA Overall LUTC UTUC Total patients 98 62 36 22 BOR CR 23 (23.4%) 13 (20.9%) 10 (28.5%) 11 (50%) PR 49 (52.6%) 31 (50%) 18 (50%) 7 (31.8%) SD 6 (6.4%) 2 (3.2%) 4 (11.4%) 2 (9.0%) PD 10 (10.7%) 9 (15.5%) 1 (2.8%) 2 (9.0%) MR 7 (7.5%) 5 (8.6%) 2 (5.7%) 0 (0%)

727 Background: Despite the availability of targeted therapies like erdafitinib for pts with aUC with susceptible genomic alterations, the rate of NGS testing remains low at 32% in the United States (Hage Chehade...Swami, JAMA Network Open, 2024). Furthermore, limited data exist regarding the timing of NGS testing in relation to the time of death. Herein, we sought to assess the rates of NGS testing at the end of life in pts with aUC in a real-world patient population. Methods: A de-identified nationwide Flatiron Health electronic health record (EHR)-derived database was used to extract patient-level data. Inclusion criteria: pts who received NGS testing (blood/tissue) and who had a date of death recorded. The analytic cohort included pts diagnosed with aUC between 1/1/2011 and 11/14/2022 who received first NGS testing results between 5/3/2012 and 12/21/2022. The time difference between each pt’s first NGS result delivered and death was calculated, and thereafter, pts were categorized into 3 categories: pts receiving results > 3 months (mo) before death, within 3 mo of death, or after death. The frequency and percentages of the 3 categories were reported, as well as by year of death. The percentages in each category were compared between 2019 (year of erdafitinib approval) and 2022 (last year in dataset) to evaluate changes in NGS testing trend. Results: Among 12,157 pts with aUC in the dataset, 1,637 had both a recorded date of death and underwent NGS testing, and were eligible and included in final analysis. The median age was 72 (IQR 66 – 79). The majority were male (73%), White non-Hispanic (76%), and treated in community practice (86%). 68.2% received NGS results > 3 mo before death (n = 1,117), 28.3% received results within 3 mo of death (n = 464), and 3.4% of NGS results were reported after death (n = 56). In pts who died in 2019 versus 2022, the percentage of pts receiving NGS results > 3 mo before death increased from 61% to 75%, while the percentage of pts receiving results within 3 months decreased from 33% to 22%, and the percentage of NGS results reported after death declined from 5.2 to 2.7%. Baseline characteristics (including race-ethnicity, insurance plan, practice type [academic vs. community]) in these categories will be presented at the meeting. Conclusions: Our findings suggest that a sizeable number of pts with aUC (∼ 32%) received their first NGS testing results towards the end of life, highlighting the need to integrate genomic testing earlier during the course of the disease to optimize benefit with targeted therapies. These results could inform clinical practice and therapeutic decisions in the clinic. CHC, YJ: Equal contribution; US, NA: Senior authors.

114 Background: Oncologists have traditionally relied upon serial assessments of PSA and periodic imaging (i.e., every 2 to 3 cycles) to monitor treatment response. Early identification of disease progression allows for faster transitions to alternative therapies that might be more effective. In this study, we evaluated the utility of post-treatment SPECT/CT for earlier detection of treatment failure in patients receiving Lutetium-177–PSMA-617 (LuPSMA). Methods: We queried an IRB-approved registry including all patients starting LuPSMA at Mayo Clinic, MN, in the interval of March 2022 to March 2023. Those receiving fewer than 2 cycles of treatment and missing SPECT/CT data were excluded. We reviewed clinical notes to document the date and reason(s) for treatment discontinuation. The assembled cohort consists of patients stopping LuPSMA for progressive disease ([PD], categorized as biochemical, radiographic, or both). Results of the prior SPECT/CT imaging reports were reviewed. At our institution SPECT/CT images are typically acquired on the day after each infusion. Metrics collected from the radiology reports included: new foci of PSMA localization since the prior PSMA PET or SPECT/CT, suspicion for new non-PSMA avid disease, and a visual determination of PSMA-avid tumor volume. Results: A total of 256 patients received an initial cycle of LuPSMA. SPECT/CT imaging data for at least 2 consecutive treatment cycles was available for 68 patients who developed PD after a median (IQR) of 3 (2-4) cycles. There were 55 patients (81%) with both biochemical and radiographic evidence of PD, 7 (10%) with biochemical only progression, and 6 (9%) with radiographic only progression. The most used imaging modality for response assessment was PSMA PET/CT (n=46, 67%), followed by conventional imaging (n=9, 13%), and choline PET/CT (n=5, 7%). The median time between most recent post-therapy SPECT/CT and determination of PD was 39 days. The prior SPECT/CT had detected new foci of PSMA avid disease in 12 (18%) cases and new non-PSMA avid disease in 5 (7%) cases. Two patients had both new PSMA-avid and non-PSMA avid lesions. The most common sites of new PSMA-avid disease on SPECT/CT were bone (n=10) and liver (n=2). A total of 20 (29%) patients had visually increased tumor volume on SPECT/CT, 9 (45%) of which had no new lesions. In total, 24 of 68 patients (35%) had early detection of treatment futility detected by SPECT/CT. At a median (IQR) follow-up time of 8 (6.4-12) mo, the median OS [95% CI] was similar for patients with PD detected on SPECT/CT (7.8 [6.6 – 9] mo) compared to those without clear evidence of PD on SPECT/CT(8.4 [6.4-10.3] mo, p=0.885). Conclusions: Post-treatment SPECT/CT imaging may complement traditional forms of response assessment and provide an earlier warning of treatment failure through identification of new PSMA-avid lesions, non-PSMA avid disease, and increased tumor volume.

112 Background: We now have more than a decade of experience using Androgen Receptor Pathway Inhibitors (ARPIs) across various disease states of prostate cancer, and the safety of their combination with Lutetium-177–PSMA-617 has been demonstrated in VISION and other large randomized clinical trials. However, real-world data on treatment patterns and outcomes involving combination therapies with 177 Lu-PSMA-617 remain limited. Methods: For this analysis, we utilized the Mayo Clinic Rochester radiopharmaceutical database, a prospectively maintained retrospective database containing all patients who received 177 Lu-PSMA-617 at our institution. We focused on patients who started treatment from March 2022 to March 2023. Patients receiving an ARPI (abiraterone acetate, enzalutamide, apalutamide, or darolutamide) during the course of 177 Lu-PSMA-617 treatment were identified, inclusive of patients who continued using an ARPI prescribed as a prior line of treatment and those who started (i.e., switched) to a new ARPI. Baseline clinicopathologic and imaging characteristics were abstracted and compared using the Mann-Whitney U and Chi-square tests. Best PSA response during treatment was reported as a percent decline from baseline. Survival was calculated from the date of the first cycle of 177 Lu-PSMA-617. PSA50 response and overall survival (OS) outcomes for the two groups (concurrent use of ARPI vs. 177 Lu-PSMA-617 alone) were compared using the Chi-square test and Kaplan-Meier method, respectively. A multivariate Cox regression analysis was performed, including established prognostic factors. Results: An ARPI was prescribed to 106 of the 256 patients (41.4%) starting 177 Lu-PSMA-617 in the interval of March 2022 to March 2023. With regards to baseline characteristics, those receiving an ARPI concurrently with 177 Lu-PSMA-617 had a lower PSA (3.4 vs 29.7 ng/mL, p < 0.001) and a lower frequency of bone (77.4% vs. 87.4%, p = 0.035) and visceral metastases (18.9 vs 34%, p = 0.008) at start of treatment. Median follow-up for the overall cohort (IQR) was 19.1 months (8.7 – 23.6). Patients receiving an ARPI plus 177 Lu-PSMA-617, as compared to 177 Lu-PSMA-617 alone, were more likely to complete all 6 planned doses of treatment (63.2% vs. 48.7%, p < 0.001), though the PSA50 response rate was similar (49.1% vs 47.3%, p = 0.786). Median OS [95% CI] for the overall cohort was 21.3 [16.7 – 25.9] months and significantly longer for those receiving ARPI with 177 Lu-PSMA-617 (NR [NE – NE]) as compared to 177 Lu-PSMA-617 alone (15.3 mo [11.6 – 19.1], p < 0.001). However, on multivariate analysis including known prognostic variables, use of ARPI was not independently associated with improved survival (HR = 1.03 [CI: 0.68 to 1.55], p = 0.891). Conclusions: Patients receiving an ARPI with 177 Lu-PSMA-617 were more likely to complete all 6 cycles of treatment, but no clear differences in survival were observed on multivariate analysis.

371 Background: Prostate cancer is the most common malignancy in men globally and remains among the top five leading causes of cancer-related deaths. Emerging evidence suggests that early identification of symptoms can detect localized disease thus enabling opportunities for curative intent treatments. This study evaluates the use of the AI-powered prediction model, C the Signs, to passively screen for prostate cancer by leveraging data from electronic medical records (EMRs), offering a novel pathway for identifying high-risk individuals. Methods: A retrospective analysis was conducted using the Mayo Data Platform, encompassing 418,477 male patient records, of which 16,835 were diagnosed with prostate cancer. C the Signs identified patients at risk based solely on their EMR data, utilizing AI-driven pattern recognition. Sensitivity and specificity analyses were performed to assess the prediction model’s accuracy. Additionally, we examined the time-to-diagnosis advantage for patients flagged by the model compared to traditional physician clinical diagnoses. Results: C the Signs demonstrated a sensitivity of 83.3% and a specificity of 52.5% for identifying patients at risk of prostate cancer. Notably, 31.8% of prostate cancer cases were identified at risk up to five years earlier by the model compared to traditional physician clinical diagnosis. Conclusions: The integration of AI-driven prediction models like C the Signs into prostate cancer screening pathways provides an opportunity to enhance early detection, particularly in symptomatic individuals. Compared to prostate-specific antigen (PSA) testing, the model achieved equivalent sensitivity and 38% higher specificity, positioning it as a valuable companion tool for improving patient outcomes.

136 Background: The ECOG performance status (PS) is a critical measure for evaluating functional capacity and determining clinical trial eligibility in patients with advanced prostate cancer. We aim to assess the capability of large language models (LLMs) to accurately infer ECOG PS from unstructured oncology notes. Methods: This retrospective study included patients with metastatic castration-resistant prostate cancer (mCRPC) receiving Lutetium-177–PSMA-617 ( 177 Lu) at the Mayo Clinic (2022-2023). Baseline ECOG PS scores were manually curated by a trained clinician from clinical notes recorded within four weeks prior to 177 Lu initiation. Structured zero-shot prompts were then used to prompt GPT-4 to infer ECOG PS scores from unstructured oncology notes. Total dataset was randomly sampled to prompt development (20%) and held-out test set (80%). Three experimental setups were employed to assess model performance: (i) utilizing unmasked clinical notes, (ii) clinical notes with only numerical ECOG PS scores masked while retaining narrative descriptive/assessment by oncologist of ECOG score, and (iii) clinical notes with both numerical scores and narrative ECOG descriptors masked. Final performance was assessed using evaluation metrics (precision, recall, F1 score) on the held-out test set. Results: A total of 240 patients (n: 50 prompt development; n: 190 test set) were included in the analysis. The median age was 70 (IQR: 66-76); a majority of patients were White (n: 229; 95%) and non-Hispanic (n: 233; 97%). The most prevalent ECOG PS score was 0 (n: 140; 58%), followed by 1 (n: 88; 37%), and 2 (n: 12; 5%). The performance was numerically higher when using unmasked notes (weighted average precision: 0.89; recall: 0.89; F1: 0.89), compared to using notes with masked scores only (weighted average precision: 0.76; recall: 0.79; F1: 0.75) and notes with both scores and narrative masked (weighted average precision: 0.57; recall: 0.53; F1: 0.48). Metrics by each ECOG PS score is outlined (Table). Conclusions: Large language models demonstrate significant potential in precisely extracting ECOG performance status from unstructured oncology notes, offering valuable support for treatment decision-making and clinical trial enrollment. However, model performance declines as reliance on implicit reporting within clinical notes increases. Future efforts focusing on prediction of ECOG using a series of notes over time will be the next step. ECOG Unmasked oncology notes ECOG Score masked only ECOG Score + ECOG narrative marked Precision Recall F1 Score Precision Recall F1 Score Precision Recall F1 Score 0 0.89 0.92 0.91 0.75 0.93 0.83 0.59 0.74 0.66 1 0.88 0.86 0.87 0.76 0.74 0.75 0.19 0.47 0.27 2 1.00 0.82 0.90 0.78 0.33 0.47 0.78 0.16 0.26

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

341 Background: Specialist clinicians (oncologists, urologists, radiation oncologists) read through free text reports of prostate MRI and biopsy reports to risk stratify patients during initial consultation of localized PCa. This laborious and time-consuming process is repeated for each day during the clinic. Herein, we propose a hybrid stratification framework utilizing a combination of large language model (LLM) and rule-based programming to automate this process. Methods: This retrospective study included patients with localized PCa (2004-2024) presenting to care at Mayo Clinic with at least one positive prostate biopsy and MRI report available. The state-of-the-art LLM – GPT4 – was utilized using a structured zeroshot prompt to extract relevant phenotypic variables (clinical T stage, Gleason patterns, grade group, extent of specimen/cores involved, prostate size/volume) from unstructured MRI and biopsy reports. Using a rule-based algorithm, the extracted variables and PSA at PCa diagnosis were used to categorize each patient into one of the six NCCN risk categories (very low [VL], low [L], favorable intermediate [FInt], unfavorable intermediate [UFInt], high risk (HR), very high risk [VHR]). Prompts were iteratively developed using ~5% of the total dataset and validated on ~10% of the dataset. Final performance was assessed against a held-out expert annotated test dataset using evaluation metrics (accuracy, F1-score, precision, recall). Additionally, manual annotation was conducted by two independent novice reviewers to compare machine annotated and novice-human annotated risk categorization. Results: A total of 397 patients were included in the evaluation. The median age at diagnosis was 64.8 (IQR: 59.7-68.5); majority of the men were White (n: 366; 92%) and non-Hispanic (n: 371; 94%). The most prevalent risk category was UFint (n: 151; 38%) followed by FInt (n: 80; 20.2%), HR (n: 76; 19.1%), VHR (n: 64; 16.1%), L (21: 5.3%), and VL (n: 5; 1.3%). Novice reviewers achieved an accuracy of 79%, precision of 80%, recall of 82% and a F1-score of 0.81. The hybrid stratification framework using GPT4 achieved an accuracy of 89%, precision of 89%, recall of 88% and a F1-score of 0.88. Among 43 errors by GPT4 risk stratification, majority of the errors were due to miscategorization of clinical T-stage (n: 21; 49%) followed by erroneous number of positive cores (n: 13; 30%). However, additional evaluation showed that GPT4 achieved numerically higher performance (accuracy: 90%; precision: 91%; recall: 90%; F1: 0.90) compared to novice reviewers (accuracy: 77%; precision: 89%; recall: 77%; F1: 0.82) for ascertaining clinical T-stage. Conclusions: Large language models exhibited superior performance than novice clinicians for risk stratification in patients with localized PCa. As the next step, we intend to validate our framework for automated risk classification in a prospective manner.

108 Background: PARPi are approved for the treatment of pts with mCRPC either as single agents or as a combination with an androgen receptor pathway inhibitor (ARPI) [PMID: 37442702]. In a US Food and Drug Administration (FDA) pooled analysis, the benefit from PARPi appeared greatest for pts harboring BRCA1/2 alterations [PMID: 38484203]. However, real-world uptake of PARPi in pts with mCRPC with BRCA1/2 alterations is unknown. Herein, our objective was to assess the usage of PARPi in real-world pts with mCRPC harboring BRCA1/2 alterations. Methods: A de-identified nationwide Flatiron Health electronic health record (EHR)-derived database was used to extract pt-level data. Eligibility criteria: pts with mCRPC harboring alterations in BRCA1 or BRCA2 or both and alive after 8/15/2020 (i.e., 3 months following the approval of the first PARPi, rucaparib, in mCRPC) with available treatment information. The data cutoff date was 5/31/2024. Pts were categorized into two cohorts based on the receipt of any PARPi or not. Baseline characteristics at the time of mCRPC diagnosis, including age, race-ethnicity, and insurance plan, were collected in both cohorts. Results: Of the overall cohort of 24,105 pts with metastatic prostate cancer, 443 pts had mCRPC with BRCA1/2 alterations and were eligible and included in this analysis. 227 (51.2%) received a PARPi, while 216 did not (48.8%). Among the 227 pts who received a PARPi, 166 received it as a single agent (73.1%), 40 received it in combination with an ARPI (17.6%), and 21 received it in combination with other agents (9.3%). The median age was 72 in both cohorts (IQR 65 – 79 in patients receiving a PARPi and 66 – 78 in patients who did not). Other baseline characteristics in both groups are shown (Table). Conclusions: Despite PARPi demonstrating survival improvement in pts with mCRPC with BRCA1/2 alterations, a significant proportion of pts do not receive them. Our findings highlight the need to improve education among clinicians of level 1 evidence and improve access to life-prolonging therapies in pts with mCRPC. Baseline characteristics of pts with mCRPC who received vs. did not receive a PARPi. Characteristic Subgroup Pts who received a PARPiN = 227 Pts who did not receive a PARPiN = 216 Race-ethnicity, n (%) Asian 9 (4) 1 (0.5) Black 22 (9.7) 30 (13.9) Hispanic-Latino 10 (4.4) 10 (4.6) White 149 (65.6) 124 (57.4) Other and unknown 37 (16.3) 51 (23.6) Insurance, n (%) Commercial health plan 192 (84.5) 182 (84.3) Medicare-other government programs 31 (13.7) 18 (8.3) Medicaid 1 (0.4) 3 (1.4) Others and unknown 3 (1.3) 13 (6)