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Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

5076 Background: C and Lu are both life-prolonging therapies for pts with mCRPC after progression on androgen receptor pathway inhibitor (ARPI) and docetaxel (D). Herein, our objective was to assess the comparative effectiveness of C vs. Lu in pts with mCRPC with prior progression on D and ARPI in real-world pts in the USA. Methods: A de-identified nationwide Flatiron Health electronic health record (EHR)-derived database was used to extract pt-level data. Eligibility: pts with mCRPC who progressed on D and ARPI and received for the first-time single-agent C or Lu. Pts who received alternative Lu or C in a later line of therapy (LOT) were excluded. Endpoints: real-world time to next therapy (rwTTNT) and real-world overall survival (rwOS). These were summarized via Kaplan-Meier survival estimates with a 95% confidence interval (CI) and compared in the context of propensity score (PS) matching weighted analysis using the Cox proportional hazard model. PS model included the following covariates: age, race-ethnicity, socioeconomic status, treatment year, Gleason score, ECOG performance status, log2PSA, alkaline phosphatase, hemoglobin, creatinine, LOT, insurance, and practice type. All the covariates achieved balance after PS matching weighting. Results: Among 24,105 pts with metastatic prostate cancer in the dataset, 1,445 met the eligibility criteria and were included (1,227 treated with C, 218 treated with Lu). In C cohort: median age was 73 (IQR 67 – 78), 66.9% had Gleason score ≥ 8, and median LOT was 4 (IQR 3-4). In Lu cohort: median age was 75 (IQR 67.25 – 80), 68.1% had Gleason score ≥ 8, and median LOT was 4 (IQR 3-5). In PS matching weighting analysis, there was evidence that pts receiving Lu had a significantly improved rwTTNT (median 8.3 months [mo], 95% CI 6.3 – 9.8) compared to those receiving C (median 4.7 mo, 95% CI 4.2 – 5.4) (HR 0.49, 95% CI 0.37 – 0.63, p < 0.001), which persisted after adjusting for covariates (HR 0.43, 95% CI 0.32 – 0.57, p < 0.001). Additionally, there was evidence that rwOS was longer in pts receiving Lu (median 10.3, 95% CI 8.9 – 12.8) compared to those receiving C (median 8.8 mo, 95% CI 6.6 – 10.8) (HR 0.69, 95% CI 0.51 – 0.92, p = 0.01). This improvement in rwOS with Lu compared to C persisted after adjusting for covariates (HR 0.61, 95% CI 0.44 – 0.84, p < 0.001). Conclusions: This is the largest real-world data to date assessing the comparative effectiveness of Lu vs. C, and it showed significantly longer rwTTNT and rwOS with Lu compared to C in pts with mCRPC pretreated with ARPI and D. Limitations: retrospective nature, selection bias, missingness, lack of randomization, etc. and residual confounding in real-world datasets. Upon external validation, these findings could guide treatment selection in the clinic and design of clinical trials.

Rab32 is a small GTPase and molecular switch implicated in vesicular trafficking. Rab32 is also an A-Kinase Anchoring Protein (AKAP), which anchors cAMP-dependent Protein Kinase (PKA) to specific subcellular locations and specifies PKA phosphorylation of nearby substrates. Surprisingly, we found that a form of Rab32 deficient in PKA binding (Rab32 L188P) relocalized away from the Golgi apparatus and induced a marked disruption in Golgi organization, assembly, and dynamics. Although Rab32 L188P did not cause a global defect in PKA activity, our data indicate that Rab32 facilitates the phosphorylation of a specific PKA substrate. We uncovered a direct interaction between Rab32 and the adaptor protein optineurin (OPTN), which regulates Golgi dynamics. Further, our data indicate that optineurin is phosphorylated by PKA at Ser342 in a Rab32-dependent manner. Critically, blocking phosphorylation at OPTN Ser342 leads to Golgi fragmentation, and a phospho-mimetic version of OPTN rescues Golgi defects induced by Rab32 L188P. Finally, Rab32 AKAP function and OPTN phosphorylation are required for Golgi repositioning during cell migration, contributing to tumor cell invasion. Together, these data reveal a role for Rab32 in regulating Golgi dynamics through PKA-mediated phosphorylation of OPTN.

Abstract Hospital medicine (HM), a well‐established clinical specialty, requires clarity of research priorities to identify target areas for investment in HM research infrastructure and activities. The Society of Hospital Medicine's Research Committee developed a 20‐item survey and used purposeful sampling of US hospitalist leaders to prioritize primary research topic domains and subdomains. Respondents were asked to rank their HM research priorities using a scale (1 [highest] to 8 [lowest]). Of 239 surveys distributed, 81 (34%) responded. Respondents were predominantly White (53%) and from academic institutions (57%). “ Overall, 46% of respondents were male and 33% were female. ” Top‐ranked research domains were innovation in care delivery (median 2, IQR 1,3), value‐based care (median 3.5, IQR 2,6), and health disparities (median 4, IQR 2,5). This survey identified top HM research priorities including systems‐based topics and health disparities, aligning with HM's identity as a specialty focused on improving systems of care and inequities in care.

ABSTRACT Background Head and neck squamous cell carcinoma remains challenging due to high recurrence rates and poor survival outcomes. Developing precise technologies for disease burden assessment, treatment response, and minimal residual disease (MRD) surveillance is crucial for improving prognosis. Methods This review explores the potential of liquid biopsy for MRD and recurrence detection. A novel liquid TNM (LiTNM) staging system is introduced, integrating biomarkers from saliva, surgical drain lymphatic fluid (SLF), and peripheral blood. Results Proximal liquid biopsies, particularly saliva and SLF, offer advantages due to their proximity to the tumor microenvironment. Saliva demonstrates high sensitivity in HPV‐associated oropharyngeal cancers, while SLF holds potential in identifying early postoperative recurrence. Despite these advancements, standardization and validation remain challenges. Conclusions Liquid biopsy approaches show promise for postoperative disease monitoring, yet their clinical implementation remains in the early stages. The proposed LiTNM staging system could complement TNM staging by providing a molecular framework for risk stratification. However, rigorous prospective studies are necessary to validate its clinical utility and facilitate adoption.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

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)

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.