Citations

75 Background: The Rx landscape of pts with mCRPC has recently evolved with the approval of lutetium-177-PSMA-617 (Lu-177) and poly(ADP) ribose polymerase inhibitors (PARPi) either as single agents or as combinations with an androgen receptor pathway inhibitor (ARPI) [PMID: 37442702]. However, real-world data on the uptake of these agents are lacking. Herein, we sought to assess the Rx patterns and attrition rates of pts with mCRPC in the era of newly approved therapies. Methods: The de-identified nationwide Flatiron Health Electronic-Health Record (EHR)-derived database was used to extract pt-level data. Eligibility criteria: diagnosis of mCRPC and Rx line (L) information. The data cut-off date was 5/31/2024. Rx patterns in each line of therapies were summarized using frequency and percentages. All analysis was done using R version 4.2.3. Results: Of the overall cohort of 24,105 pts with metastatic prostate cancer, 12,333 pts diagnosed with mCRPC between 1/1/2013 and 5/9/2024 met the eligibility criteria and were included. Rx trends are summarized (Table). Of pts receiving 1L Rx, 61% of pts received 2L therapy, and 35% received 3L therapy. Androgen receptor pathway inhibitor (ARPI) was the most common Rx in the 1L setting (73.7%), followed by taxane (10%) and sipuleucel-T (3.8%). In 2L, ARPI was again the most frequent Rx (46.8%), followed by taxane (23.6%). In 3L, taxane became the most frequent Rx (35.4%), followed by ARPI (26.2%) and radium-223 (4.9%). Rx trends per year will be presented in the meeting. Conclusions: In the current era,a high proportion of pts with mCRPC receiving 1L Rx do not receive a subsequent line of Rx (39% of pts do not receive 2L, and 65% do not receive 3L Rx). ARPIs and taxanes remain the most frequently used Rx options in most L of therapy. These findings highlight the need for better tolerated Rx, therapies with a novel mechanism of action, and improved access to care for our pts. Rx trends per year will be presented in the meeting. Rx patterns in pts with mCRPC. Rx 1L, n (%)N = 12,333 2L, n (%)N = 7475 3L, n (%)N = 4316 4L, n (%)N = 2339 5L, n (%)N = 1225 ARPI 9085 (73.7) 3503 (46.8) 1130 (26.2) 356 (15.2) 157 (12.8) PARPi-based therapies 93 (0.8) 185 (2.5) 142 (3.3) 98 (4.2) 47 (3.9) Platinum-based therapy 127 (1) 216 (2.9) 198 (4.6) 148 (6.3) 130 (10.6) Lu-177-based therapies 50 (0.4) 109 (1.5) 169 (3.9) 143 (6.3) 103 (8.4) Radium-223 183 (1.5) 241 (3.2) 213 (4.9) 125 (5.3) 79 (6.4) Sipuleucel-T 474 (3.8) 94 (1.3) 53 (1.2) 18 (0.8) 8 (0.7) Taxane 1240 (10) 1763 (23.6) 1526 (35.4) 935 (40) 393 (32) Pembrolizumab 24 (0.2) 41 (0.5) 36 (0.8) 27 (1.2) 29 (2.4) Other*/not-applicable** 665 (5.4)/392 (3.2) 1000 (13.4)/323 (4.3) 602 (14)/247 (5.7) 320 (13.7)/169 (7.2) 162 (13.3)/117 (9.5) *Includes drugs approved for mCRPC in combination with unapproved agents or clinical trial drugs. **Includes agents not approved for prostate cancer.

95 Background: Most pts with prostate cancer with bone metastasis experience symptomatic skeletal events (SSEs) [PMID: 23884473], leading to increased morbidity and mortality. BMAs have been approved since 2007 and are recommended in mCRPC to prevent SSEs in pts with bone metastases or a high risk of osteoporotic fracture. Herein, we sought to assess the real-world trends of BMA administration in pts with mCRPC. Methods: We used the de-identified nationwide Flatiron Health electronic health record (EHR)-derived database to extract pt-level data. Inclusion criteria: pts with mCRPC with available date of diagnosis of mCRPC and information on receipt of BMA. Pts were categorized into 2 cohorts based on BMA receipt (e.g., bisphosphonates, denosumab, teriparatide, romosozumab) or not. Treatment trends of BMA by year of mCRPC diagnosis were summarized using frequency and percentages. Baseline characteristics at the time of mCRPC diagnosis (age, race-ethnicity, practice type, insurance) were compared between cohorts using the Wilcoxon rank-sum and Chi-squared tests. Results: Of 24,105 pts with metastatic prostate cancer in the dataset, 14,112 with mCRPC were eligible and included. 7,990 (56.6%) received BMA while 6,122 (43.4%) did not. In BMA receipt cohort: median age was 75 (IQR 67 – 81), majority were White (61%), treated in community practice (87%) and had commercial health plan (80%). In BMA non receipt cohort: median age was 75 (IQR 68 – 82), majority were White (61%), treated in a community practice (78%), and had commercial health plan (76%). The use of BMA decreased over time from 56% in 2013 to 46% in 2024 (Table). Statistically significant differences existed between cohorts in race-ethnicity, practice type, and insurance (p < 0.001) and will be presented at the meeting. Conclusions: Despite recommendations to use BMAs in pts with mCRPC and bone metastasis or high risk of osteoporotic fracture, our findings reveal a low utilization rate of BMAs in mCRPC setting with a notable decline over time. This highlights the need to incorporate the use of BMAs in clinical practice and improve access to these agents. Trends by year of mCRPC diagnosis in receipt of the first BMA and frequency and percentage of each agent. Year 2013N = 549 2014N = 1035 2015N = 1223 2016N = 1345 2017N = 1441 2018N = 1346 2019N = 1469 2020N = 1380 2021N = 1352 2022N = 1393 2023N = 1196 2024N = 383 Receipt of BMA, n (%) 316 (56) 674 (65) 792 (65) 837 (62) 886 (61) 766 (57) 816 (56) 766 (56) 692 (51) 713 (51) 556 (46) 176 (46) Denosumab, n (%) 190 (60.1) 450 (66.8) 557 (70.3) 599 (71.6) 606 (68.4) 533 (70) 526 (64.5) 488 (63.7) 410 (59.2) 414 (58.1) 290 (52.2) 94 (53) Zoledronic acid, n (%) 120 (38) 220 (32.6) 228 (28.7) 233 (27.8) 270 (30.5) 225 (29) 281 (34.4) 273 (35.6) 277 (40) 295 (41.4) 258 (46.4) 82 (47) Teriparatide, other bisphosphonates, n (%) 6 (1.9) 4 (0.6) 7 (1) 5 (0.6) 10 (1.1) 8 (1) 9 (1.1) 5 (0.7) 5 (0.7) 4 (0.5) 8 (1.4) 0

90 Background: Poly(ADP) ribose polymerase inhibitors and pembrolizumab are approved for patients with metastatic prostate cancer (mPC) with susceptible genomic alterations based on NGS testing. We recently reported that the rate of NGS testing in mPC remains low at 29.3% in the United States (1). Herein, we investigated the rates of NGS testing at the end of life in a real-world patient population. Methods: This study utilized the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database. Eligibility: diagnosis of mPC, with information on receipt of NGS testing (blood/tissue) and recorded date of death. The time between each patient’s first NGS testing result date and date of death was measured and categorized into 3 groups: NGS test results delivered more than 3 months (mo) before death, within 3 mo of death, and delivered after death. Frequencies and percentages of these 3 categories were reported, also by the year of death. Results: Out of 24,105 patients with mPC in the database, 3,397 had both a recorded date of death and underwent NGS testing and were eligible and included. The median age was 74 (IQR 67–80), 70% were White non-Hispanic, and 83% were treated in a community-based practice. In these patients, 2,901 (85.4%) NGS results were reported more than 3 mo before death, 457 (13.5%) within 3 mo of death, and 39 (1.1%) after death. From 2015 to 2024, the percentage of patients receiving their result more than 3 mo before death increased from 27% to 91%, while those receiving it within 3 mo before death decreased from 55% to 8.5%, and the percentage of NGS results reported after death declined from 18% to 0.6%. Baseline characteristics (including race-ethnicity, insurance plan, practice type [academic vs. community]) in these categories will be presented at the meeting. Conclusions: Despite the availability of life-prolonging targeted therapies based on NGS testing results for patients with mPC, in ∼14% of patients, the first NGS testing results were reported within 3 mo before death or after death. The lack of timely NGS testing limits patients' access to life-prolonging personalized treatments in earlier settings, resulting in missed opportunities for improved outcomes. 1. Hage Chehade, Swami, JAMA Network Open, 2024.

866 Background: Emerging data suggests that Language Models (LLMs) provide an unprecedented opportunity to extract critical data from the unstructured pathology reports. However, the performance of state of art (SOTA) models on clinically meaningful tasks such as assessment of pathologic responses in patients receiving neoadjuvant chemotherapy (NAC) in muscle invasive bladder cancer(MIBC) is not well known. Methods: This retrospective cohort included patients with pathologically confirmed Muscle-Invasive Bladder Cancer from the year 2018 to 2024. Selected patients included those who underwent Neoadjuvant chemotherapy followed by definitive cystectomy at Mayo Clinic. Gold standard labels were manually curated by trained clinicians. The state of the art (SOTA) LLM – GPT4 – was utilized using a structured zero-shot prompt to extract relevant phenotypic variables such as tumor site, tumor histology and additional histological variants if any, presence of in-situ / invasive carcinoma, presence of lamina propria / muscularis propria / lympho-vascular / perineural invasion, and T-stage from unstructured pathology reports. Structured prompts were iteratively developed for each data variable and validated using ~5% of the total dataset. The extracted variables were manually compared and labelled as True positive / True negative / False positive / False negative. Final performance was assessed against a held-out expert annotated test dataset using evaluation metric (accuracy). Results: A total of 200 reports from 99 patients were extracted by the LLM. Of the 200 reports, 41 duplicate / other pathology reports (e.g. – autopsy, cholecystectomy, etc.) were removed during final assessment. Significant characteristics such as tumor site, histology, and additional histology variant had an accuracy of 89%, 87%, and 91% respectively. Notably, accuracy of detection of in-situ versus invasive carcinoma was 70% versus 91%. Specific variables such as lamina propria / muscularis propria invasion / T-stage / lympho-vascular invasion / perineural invasion were answered with the accuracy of 75%, 75%, 67%, 90%, and 94% respectively. Conclusions: LLMs have the potential to extract critical data from unstructured pathology reports and automate assessment of pathologic responses in MIBC patients receiving NAC. Iterative improvements in performance can be achieved by improving prompt design using expert clinical input. This process can be potentially scaled to automate cancer registries.

428 Background: Prostate cancer management presents a significant healthcare burden, with the need to efficiently triage patients for treatment. Our objective is to leverage large language models to predict physician-recommended treatment plans from unstructured clinical notes. By accurately predicting treatment plans, we aim to risk stratify and triage patients effectively, thereby optimizing the allocation of physician resources. Methods: 448 unstructured initial urology consultation patient notes following first positive prostate cancer biopsy were identified. The recommended and final treatments received were manually annotated to establish ground truth labels (Table 1). The dataset was split 80:20 for training and testing, preprocessed to remove plan sections and formatted into question-answer (QA) format. A domain-specific large language model (LLM) inspired by GPT and a specialized tokenizer (PCa- LLM) for prostate cancer terminology were developed. QA models were built using the PCa-LLM and compared with those using GPT-2 as the backbone to predict recommended and final treatments. Results: For the physician-recommended treatment plans, our LLM (PCa-LLM) showed superior performance with higher AUROC scores for curative vs. non-curative treatments (0.78 vs. 0.65), chemo-hormonal vs. other non-curative treatments (0.89 vs. 0.65), and surveillance vs. all other treatments (0.72 vs. 0.70), while both models achieved the same high AUROC of 0.99 for chemo-hormonal vs. all other treatments. For final treatments, PCa-LLM demonstrated better AUROC for curative vs. non-curative treatments (0.77 vs. 0.74) and chemo-hormonal vs. other non-curative treatments (0.71 vs. 0.66), while GPT2 outperformed PCa-LLM for surveillance vs. all other treatments (0.78 vs. 0.70). Both models achieved an AUROC of 0.99 for chemo-hormonal vs. all other treatments. Conclusions: PCa-LLM accurately predicted most treatment categories better than GPT2, with higher AUROC scores, and can be utilized to triage prostate cancer patients using initial consultation notes. Task Physician -Recommended Treatment Plan Final Treatment Received Curative Prostatectomy/Radiation 228 230 Non-Curative Focal Therapy 22 15 Active Surveillance 40 45 Chemo-hormonal 30 30 Model Predictions (AUROC) GPT2 PCa-LLM GPT2 PCa-LLM Curative vs. non-curative 0.65 0.78 0.74 0.77 Chemohormonal vs. other non-curative 0.65 0.89 0.66 0.71 Chemohormonal vs. all other 0.99 0.99 0.99 0.99 Surveillance vs. other non-curative 0.64 0.60 0.67 0.59 Surveillance vs. all other 0.70 0.72 0.78 0.70

109 Background: C and Lu are associated with overall survival benefit and approved for pts with mCRPC after prior treatment with docetaxel and an androgen receptor pathway inhibitor (ARPI). However, in real-world settings, administering Lu requires coordination across specialties and resources [PMID: 36637862], which could delay its administration. Herein, we sought to compare the time from medication order to administering the first dose of C versus Lu. Methods: This study used pt level data from the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database. Inclusion criteria: availability of the date of the first order of single-agent C or Lu and the date of first administration of the drugs. The time between the first medication order to the administration of single-agent C or Lu was measured. Wilcoxon rank-sum test was used to compare the median days between medication order to administration between these 2 agents and a stratified analysis by practice type and insurance was performed. Results: Of 2,329 orders, 2,045 orders were for C between 1/1/2013 and 1/26/2024, while 284 were for Lu between 8/1/2013 and 12/6/2023. The time from medication order to administration was significantly longer with Lu (median 22 days, IQR 12-43) compared to C (median 7 days, IQR 3-14, p < 0.001). Time from medication order to administration of C and Lu by practice type and insurance is summarized (Table). At the time of the first order, pts in Lu cohort, compared to C cohort, were significantly older (median age 74 vs. 72, p < 0.001), more likely to be treated at an academic practice (27% vs. 15%, p < 0.001), and more likely to be covered by a commercial health plan (80% vs. 73%, p < 0.001). Conclusions: Our results show that it takes significantly longer to administer Lu compared to C across different practice types and most insurance plans. These results could inform healthcare policies aimed at improving access to Lu and guide treatment selection in the clinic. Time from medication order to administration of C and Lu by practice type and insurance. Category Subtype (number of orders for C/number of orders for Lu) CN = 2045 LuN = 284 p-value Practice type, mean (range) (days) Academic (315/77) 1.3 (0 – 59) 9.2 (0 – 55) < 0.001 Community (1730/207) 13 (0 – 243) 44 (1 – 282) < 0.001 Insurance, mean (range) (days) Commercial (1494/226) 11 (0 – 163) 32 (0 – 215) < 0.001 Medicare/other government programs (266/46) 10 (0 – 120) 46 (0 – 282) < 0.001 Medicaid (32/2) 9 (0 – 76) 28 (0 – 55) 0.8 Others (72/6) 12 (0 – 42) 21 (0 – 41) 0.2 Unknown (181/4) 14 (0 – 243) 76 (39 – 91) < 0.001

104 Background: Androgen deprivation therapy (ADT) intensification [ADT + androgen receptor-pathway inhibitor (ARPIs) +/- docetaxel] has been shown to improve survival outcomes in pts with mHSPC and is recommended as standard of care by all major guidelines. However, prior studies have shown underutilization of ADT intensification [PMID: 39191549]. There are limited updated and large-scale studies on the use of ADT intensification, beyond January 2023. In this study, we aimed to assess the treatment patterns of ADT intensification in real-world pts with mHSPC in the US, including data on those treated since January 2023. Methods: This retrospective study utilized the nationwide Flatiron Health Electronic Health Record (EHR) derived de-identified database. Eligibility: Pts diagnosed with mHSPC and availability of treatment information (ADT and/or line of therapy in mHSPC). The data cut-off date was 5/31/2024. Treatment patterns were summarized using frequency and percentages. All analysis was done using R version 4.2.3. Results: Among 24,105 pts with metastatic prostate cancer in the database, a total of 14,084 were eligible and included. The median age at metastatic diagnosis was 72 years (IQR 65 – 79), and 60% were White non-Hispanic. In the overall cohort (1/1/2013–5/15/2024), 56.8% received ADT monotherapy, and 37.7% received ADT intensification. Table summarizes the treatment patterns by year, highlightinga consistent increase in adoption of intensified ADT regimens, and a decline in use of ADT monotherapy. Notably, since January 2023, 76.8% pts with mHSPC received ADT intensification while only 17.3% of pts received ADT monotherapy. Conclusions: To our knowledge, this is the first and largest study to evaluate the current use of ADT intensification. These data indicate a notable improvement in the adoption of level 1 evidence (ADT intensification) in the treatment of pts with mHSPC. These encouraging data emphasize the benefits of continued efforts to promote guideline-concordant care and provide the current treatment landscape to design clinical trials. Treatment patterns by year in pts with mHSPC. Treatment 2013–2017n = 5,263(%) 2018n = 1,313 (%) 2019n = 1,407(%) 2020n = 1,353(%) 2021n = 1,483(%) 2022n = 1,556(%) 2023n = 1,459(%) 2024n = 250(%) ADT monotherapy 4,095 (77.8) 856 (65.2) 819 (58.2) 664 (49) 698 (47.1) 569 (36.6) 291 (20) 5 (2) ADT + ARPI 155 (2.9) 238 (18) 354 (25.1) 452 (33) 555 (37.4) 695 (44.7) 842 (58) 154 (61.6) ADT + Docetaxel 709 (13.5) 156 (12) 175 (12.4) 156 (12) 134 (9.0) 117 (7.5) 78 (5.3) 19 (7.6) Triplet(ADT + ARPI + Docetaxel) 6 (0.1) 2 (0.2) 1 (0.1) 2 (0.1) 9 (0.6) 82 (5.2) 166 (11.1) 53 (21.2) Other therapies 298 (5.7) 61 (4.6) 58 (4.1) 79 (5.8) 87 (5.9) 93 (6) 82 (5.6) 19 (7.6)

54 Background: The incidence of colorectal cancer is increasing rapidly, particularly amongst younger people. Despite a screening program being widely available, most patients are diagnosed with cancer through symptomatic presentation. Leveraging electronic medical records (EMRs) through clinical decision support platforms offers potential for identifying high-risk individuals before clinical suspicion develops. This study aims to explore the challenges associated with early colorectal cancer diagnosis and evaluate the effectiveness of the cancer case finding feature of a cancer management platform called C the Signs. Methods: A retrospective analysis was conducted using data from the Mayo Data Platform, comprising records of 894,275 patients, including 7,348 diagnosed with colorectal cancer. C the Signs was employed to identify patients at risk of colorectal cancer based on EMR data. Sensitivity and specificity analyses were performed to assess the platform's performance in identifying high-risk patients. Additionally, the study evaluated the temporal discrepancy in colorectal cancer diagnoses between those flagged by C the Signs and those detected by primary care physicians. Results: The C the Signs screening platform had a sensitivity of 93.8% and a specificity of 19.7% in identifying patients at risk of colorectal cancer. Notably, 29.4% of patients with colorectal cancer were identified as being at risk up to 5 years earlier by C the Signs compared to diagnoses made by primary care physicians, highlighting the platform's potential for early detection. Conclusions: Early identification of high-risk individuals holds promise for improving patient outcomes and reducing the burden of colorectal cancer. Whilst the specificity of the C the Signs platform for colorectal cancer is relatively low, the sensitivity is comparable to the most favorable rates reported for colonoscopy. In addition, if C the Signs was used in conjunction with a relatively cheap screening test such as Faecal Immunochemical Testing, this would significantly drive up the specificity whilst maintaining the sensitivity. Further studies are needed to assess its efficacy in conjunction with tests available in primary care.