Citations

11157 Background: Metastatic cancers are the leading cause of mortality in the United States (US). In this study of death certificates, obtained from data registry spanning from 1999 to 2024, we examined mortality trends among individuals suffering from any metastatic cancer in US. Methods: Our aim was to analyze the trends in mortality among US residents by demographic characteristics and predict future years based on the existing data by Autoregressive Integrated Moving Average (ARIMA) forecasting modeling. The national mortality data from the multiple causes of death files in the CDC WONDER Database were queried by applying the ICD-10 codes as C77-C79 for secondary cancers. Trends in age-adjusted mortality rate (AAMR) were assessed and results were expressed as average annual percentage changes (AAPC) from Join point regression. Results: There were a total of 2,098,842 deaths due to metastatic cancers in US from 1999-2024 with a crude mortality rate of 25.97 per 100,000 population, about 52% of them being males and 48% were females. Mortality rates (AAMR) continue to trend down from 1999-2008 in both gender, but showed an increase from 2009 onwards with females showed significant AAPC of +0.44. ARIMA predictive model exhibits a significant decline of mortality rates up to the year 2050 in Black population (AAMR: 24.45), Northeast region (AAMR: 10.84) and metropolitan areas (AAMR: 19.93). Conclusions: These findings provide valuable insights of mortality patterns among US population with metastatic malignant diseases. Certain subgroups with specific demographic and geographic characteristics such as White Population, non-metropolitan areas and those belonged to West region had higher future prediction of AAMR. ARIMA forecasting model for predicting AAMR in metastatic cancer population in U.S. Variables AAMR in 1999 Lowest AAMR (year) AAMR in 2024 AAPC ARIMA forecast (2050) Total 27.65 18.34(2008) 29.13 +0.30(0.20 – 0.40)* 29.90 Gender Female 23.89 15.73(2008) 26.23 +0.44(0.34 – 0.54)* 26.04 Male 33.48 22.03(2008) 33.19 +0.06(-0.32 – 0.44) 32.31 Race White 27.74 18.70(2008) 30.89 +0.51(0.40 – 0.63)* 32.95 Black 35.32 21.30(2014) 30.67 -0.54(-1.43 – 0.36) 24.45 Hispanic 19.07 12.93(2008) 21.68 +0.42(0.30 – 0.55)* 24.68 Asian or Pacific Islander 16.32 11.60(2008) 19.37 +0.54(0.25 – 0.89)* 23.15 American Natives 20.35 15.19(2005) 21.68 +0.33(-0.15 – 1.15) 24.28 Census Region Northeast 27.29 15.62(2014) 20.53 -1.03(-1.57 – -0.49)* 10.84 Midwest 29.51 18.55(2010) 31.96 +0.35(0.21 – 0.47)* 30.74 South 28.23 19.18(2008) 30.77 +0.43(0.31 – 0.56)* 33.30 West 24.81 17.18(2008) 30.72 +0.97(0.77 – 1.19)* 39.18 Urbanization † Metropolitan 27.17 17.76(2008) 24.66 -0.46(-0.60 – -0.33)* 19.93 Non-metropolitan 29.87 20.73(2010) 30.39 +0.02(-0.75 – 0.80) 26.56 AAMR per 100,000 population. † For urbanization, AAMR 1999-2020. *P-value <0.05.

11114 Background: Advances in early detection and treatment of breast cancer (BC) have significantly improved survival outcomes. In this growing survivor population, addressing modifiable lifestyle factors such as alcohol intake (AI), a known risk factor for BC and BC recurrence, is critical to improving prognosis and health-related quality of life (HRQoL). This study examines AI patterns and their associations with mental and physical HRQoL in early BC survivorship. Methods: Adult patients newly diagnosed (≤1 year) with stage I–III BC at Mayo Clinic Rochester were invited to enroll in the Mayo Clinic Breast Disease Registry. Of the 3252 patients who consented, 1997 completed surveys approximately one-year post-diagnosis, capturing weekly AI, demographic factors, and PROMIS-10 scores. PROMIS-10 (a 10-item measure of health, well-being, and distress) physical and mental health T-scores (mean = 50, SD = 10) are standardized to the U.S. population. Weekly AI was categorized as minimal/none (<1 drink), mild (1–4 drinks), moderate (5–14 drinks), or high (≥15 drinks). Univariate analyses were performed with Monte Carlo-based Fisher exact tests. Multivariate multinomial logistic regression models using a glogit link function assessed AI as the outcome and PROMIS-10 scores as independent variables, adjusting for financial status, smoking, chemotherapy, and moderate exercise because those were associated with AI at year 1 in prior analyses. Results: Univariate analyses revealed statistically significant associations between AI and PROMIS-10 scores (Table 1). Adjusted multivariate analyses identified that patients with better physical health were more likely to drink 1–4 or 5–14 drinks/week and less likely to abstain (<1 drink/week) than those with poorer physical health (p=0.027). There was no statistically significant association between AI and mental health QoL after adjustment for covariates. Conclusions: Better physical HRQoL during early survivorship was associated with higher AI, suggesting that patients who are feeling physically unwell may be less likely to drink alcohol. Additional public health messaging about the relationship between AI and breast cancer risk and recurrence may be needed, especially for those who are feeling well enough to consume alcohol. Univariate analyses of year 1 PROMIS-10 scores with year 1 alcohol consumption using Monte Carlo-based Fisher exact test. Alcoholic drinks per week at year 1 < 1(N=1204) 1-4(N=600) 5-14(N=175) 15+(N=18) Total(N=1997) P-value PROMIS global mental health T score, Year 1, n (%) 0.008 < 50 466 (63.8%) 200 (27.4%) 54 (7.4%) 10 (1.4%) 730 (37.1%) 50+ 718 (57.9%) 394 (31.8%) 120 (9.7%) 7 (0.6%) 1239 (62.9%) Missing 20 6 1 1 28 PROMIS global physical health T score, Year 1, n (%) <0.001 < 50 440 (66.5%) 165 (24.9%) 50 (7.6%) 7 (1.1%) 662 (33.6%) 50+ 740 (56.7%) 432 (33.1%) 124 (9.5%) 10 (0.8%) 1306 (66.4%) Missing 24 3 1 1 29

11106 Background: Patients with cancer take different types of medication (e.g., enteral, parenteral), with varying treatment schedules, in various settings. They are also sometimes instructed to stop medication in response to toxicity. To measure medication nonadherence in this heterogenous population, we modified and pilot-tested a self-report measure originally developed to assess nonadherence to daily oral medication, the Domains of Subjective Extent of Nonadherence (DOSE-Nonadherence), in adults with cancer. Methods: Participants were ≥21 years, on active cancer treatment at Memorial Sloan Kettering, and spoke English and/or Spanish. A branching logic was added to allow participants to select the setting where they take cancer medication: 1) only at home, 2) only in clinic, or 3) partly at home/partly in clinic. Based on administration setting, participants reported nonadherence to medications their clinician expected them to take over a given reference period (1 week for home and 1 month for clinic medications). Participants then reported on setting-relevant reasons for nonadherence. Survey instructions/items were refined in an iterative process using feedback from a patient investigator committee and participant cognitive interviews. During interviews, we asked participants about the interpretation of instructions/items, recall period, and comprehensiveness of reasons for nonadherence, regardless of their own adherence. After initial testing in English, a Spanish version was developed through transcreation. Adherence was dichotomized for analysis (complete adherence vs. any nonadherence). For all participants who received clinic medications, concordance between chart abstracted and self-reported adherence was evaluated. Results: Of 55 participants who completed the surveys and interviews (67% in English; 33% in Spanish), 87% were female and 44% identified as Latino. The majority (73%) had breast cancer; 60% had metastatic disease. 12 of 45 who received medication in clinic reported nonadherence as did 8 of 35 who took medication at home. Participants felt able to respond accurately to both recall periods. All reasons for nonadherence were perceived as relevant to themselves or other patients. There was 89% concordance between self-reported and chart abstracted adherence for clinic-administered medication. To improve concordance, changes were made to the formatting and instructions. Of 12 participants who completed the final version of the survey for clinic-administered medication, there was 100% concordance between self-reported and chart abstracted nonadherence. Conclusions: Our results support the reliability and validity of modified DOSE-Nonadherence for cancer patients. This instrument can be used to better identify nonadherence to cancer medications in a population of adult patients with cancer receiving various treatment regimens.

e13741 Background: The NCCN guidelines for first-line (1L) regimens in CLL have evolved over the past decade, shifting from ibrutinib (ibr) and chemoimmunotherapy (CIT) to second-generation BTK inhibitor and venetoclax combinations. We evaluated real-world use of preferred 1L treatment (tx) since 2016 in CLL patients (pts) in routine care to identify differences in prescribing based on race/ethnicity and practice type. Methods: This retrospective observational study utilized the US nationwide Flatiron Health electronic health record-derived de-identified database. Eligible pts had confirmed CLL and initiated 1L tx between 01/01/16-07/31/24. Primary outcome was receipt of preferred 1L tx defined by NCCN guidelines in four time periods by race/ethnicity (Hispanic, White, Black, Asian/Other). Odds ratios (ORs) were estimated using logistic regression, additionally adjusting for age, sex, ECOG performance status, IGHV, del17p/ TP53 mutation status, time period, and practice type. Results: A total of 7528 pts were included. Compared with White pts (n = 5472), Black pts (n = 640) were younger (median age at 1L: 68 vs 71). More Black and Hispanic (n = 290) pts were treated at community practices (86% vs 80% White). Of those pts tested, more Black pts had unmutated IGHV than White (77% vs 56%). Presence of del17p/ TP53 mutation was similar across races/ethnicities. The proportion of pts receiving preferred 1L tx based on the NCCN Guidelines significantly differed by race/ethnicity (Table) ( P = 0.0021). The proportion of Hispanic pts treated with preferred 1L tx was significantly lower than White (OR = 0.61; 95% CI: 0.47-0.79), but Black was similar to White (OR = 1.07; 95% CI: 0.89-1.30). Updates to NCCN guidelines were significantly associated with pts receiving preferred 1L tx by practice type ( P = 0.0005). In 2016-2018, 44% of community practices and 55% of academic centers adopted targeted therapies (TTs); in 2019, ibr use increased in both practices (77% vs 68%, respectively). Adherence to preferred tx improved across practices in 2020 but decreased with the prioritization of second-generation therapies. After the approval of zanu, use of TTs was 71% in community practices and 74% in academic centers. Conclusions: Inequities in pts with CLL receiving preferred 1L tx suggests disproportionate use of CIT and ibr by race/ethnicity. Use of preferred TTs also differed by practice type and time period, with increased adoption after pivotal trials. Time period % Pts receiving preferred tx by race a White Black Hispanic Asian/Other 2016-2018:Preferred tx ibr 46% 50% 43% 45% 2019(v2.2019: Jan 2019)Preferred tx ibr 76% 76% 67% 79% 2020-Jun 2022(v4.2020: Feb 2020)Preferred tx acala, VO, ibr 84% 84% 69% 83% Jul 2022-Jul 2024(v2.2023: Aug 2022)Preferred tx acala, VO, zanu 72% 66% 56% 73% VO, venetoclax + obinutuzumab. a By listing of NCCN preference (preferred vs other) among non-del17p and age >65.

e13738 Background: Persistent racial/ethnic and regional disparities in neoplasm mortality remain significant public health challenges. This study evaluates long-term trends in neoplasm mortality by race/ethnicity at the county level in the United States and examines the relationship between hospital density and mortality. Methods: Using data from the Global Burden of Disease Study (2000-2019), we analyzed age-standardized mortality rates (ASR) for neoplasms by ethnicity/raceHispanic (all races) and non-Hispanic American Indian or Alaska Native (AIAN), Asian or Pacific Islander (API), Black, and White. A joinpoint regression analysis was used to identify significant changes in the mortality trends, summarized as average annual percent change (AAPC). Correlation analysis evaluated the relationship between county-level hospital density and neoplasm mortality. Results: Neoplasm mortality rates declined across all racial/ethnic groups from 2000 to 2019, but disparities persisted. ASR per 100,000 population decreased from 240.5 (95% UI: 214.3–269.9) to 203.0 (180.8–228.3) for AIAN, 149.5 (144.9–153.9) to 115.3 (111.7–118.7) for API, 314.5 (312.1–317.0) to 218.7 (217.1–220.3) for Black, 168.4 (164.6–172.0) to 133.8 (131.0–136.4) for Latino, and 241.2 (240.5–242.0) to 187.5 (186.9–188.2) for White. AAPC from 2000 to 2019 ranged from -0.94% (AIAN) to -1.90% (Black). Gender-specific analyses revealed similar trends, with males showing slightly greater declines. Southeast states experienced slower declines compared to the Northeast. Some counties exhibited worsening mortality trends, particularly for AIAN, API, and Latino. The county-level hospital density (table) showed weak but statistically significant correlations in 2019 for AIAN (r = -0.35; p < 0.0001), White (r = -0.15; p < 0.0001), and Black (r = -0.18; p < 0.0001). No inverse correlation was observed for API (r = 0.07; p = 0.071) and Latino (r = 0.06; p = 0.012). Conclusions: While significant progress has been made in reducing neoplasm mortality, racial/ethnic and regional disparities persist, and health care access appears to contribute to this at least in part. Future analyses should evaluate mortality by cancer site and also account for broader clinical and public health infrastructure, as well as social determinants of health to better understand and address underlying disparities. The correlation between hospital density and age-standardized mortality rate from neoplasms by race/ethnicity in 2019. Correlation coefficient with mortality and hospital density AIAN API Black Latino White 2019 -0.35(-0.43 – -0.27) 0.069(-0.0058–0.14) -0.18(-0.23 –-0.13) 0.064(0.014–0.11) -0.15(-0.19 – -0.12)

e13718 Background: Oncology has evolved in the cyber era of medicine. The 21 st Century Cures Act mandated patient access to medical data (tests, provider notes) via patient portals connected to EHRs in order to facilitate patient engagement. Furthermore, the COVID-19 pandemic rapidly transformed patients’ interactions with portals by increasing reliance on telehealth and portal messaging. This shift has been well-documented in primary care, and emerging research suggests similar trends in oncology. More information is needed regarding how this has evolved post-pandemic. We present single academic institution real-world data (RWD) describing ongoing changes in patient messages and telephone calls in oncology. Methods: We quantified the changes (2019-2024) within oncology at Mayo Clinic Rochester campus including all care team, provider, nursing, and pharmacist visits, patient portal medical advice requests, patient calls, and messages sent to patients per year using Epic's SlicerDicer™ tool. Results: Patient visits/year dipped (-0.2%) from 2019 to 2020, but increased steadily from 2021 forward (Table 1). Patient portal advice requests increased significantly faster (Table 1); from 2019 to 2024, visits /year increased by 40%, while the number of advice requests increased by 131%. Patient calls/year increased more modestly (64%), while the number of messages sent to patients increased by 163% over this time. Conclusions: The transparency allowed by the Cures Act coupled with the impact of the pandemic has empowered patients to engage in their oncology care more proactively through the EHR. Patient-initiated portal messaging is increasing, with messages sent to patients from the oncology department similarly rising. Patient call volumes are also growing despite EHR messaging. Further research is necessary to better understand patterns of EHR use in oncology and explore ways to reduce clinician burnout that may arise from responding to this increased communication volume, as well as to understand the impact on patient satisfaction and outcomes. Changes in patient volume compared to patient calls and EHR messaging over time. Year Total Patient Visits Change in Patient Visits from Prior Year (%) Total Patient Medical Advice Messages Change in Patient Medical Advice Message Volume from Prior Year (%) Total Patient Calls Change in Patient Call Volume from Prior Year (%) Messages Sent to Patients Change in Patient Messages Sent to Patients from Prior Year (%) 2019 62,440 n/a 173,177 n/a 195,143 n/a 99,974 n/a 2020 61,959 -0.7 218,976 +26.5 240,833 +23.4 135,044 +35.1 2021 70,950 +14.5 276,561 +26.3 267,395 +11.0 165,293 +22.4 2022 74,961 +5.7 325,580 +17.7 263,732 -1.4 206,043 +24.7 2023 80,672 +7.6 349,756 +7.4 274,446 +4.0 214,257 +4.0 2024 87,605 +8.6 399,740 +14.3 319,963 +16.6 263,271 +22.9

e13701 Background: The optimal therapeutic strategy for advanced epithelial ovarian cancer (EOC) — primary debulking surgery (PDS) followed by adjuvant chemotherapy vs. neoadjuvant chemotherapy (NACT) followed by interval debulking surgery — remains debated. NACT is often chosen for unresectable disease, typically identified only during surgery. This study aims to develop a deep learning (DL) model to predict tumor resectability from pre-operative computed tomography (CT) scans. Methods: We retrospectively included EOC patients, FIGO stage III-IV, who underwent PDS between 01/ 2016 and 12/2023 at the European Institute of Oncology, Milan, Italy. The dataset included anonymized portal venous phase contrast-enhanced CT scans. Residual tumor (RT) annotations were extracted from operative reports and categorized as optimal RT (< 1cm) and suboptimal RT (≥1cm). A DL segmentation model (based on open-source TotalSegmentator) was used to identify pelvic/abdominal organ volumes and to remove couch artifacts. The dataset was split into training/validation (80%) and testing (20%) sets. A pre-trained Vision Transformer (ViT) encoder was adapted for 3D imaging, converting CT volumes into features refined by an attention mechanism and classified as optimal RT or suboptimal RT . We trained two ViT configurations (vit-base-patch16-224 and dinov2-small-imagenet1k-1-layer) and compared them with DenseNet121, a widely used Convolutional Neural Network (CNN) for image classification. Performance metrics included accuracy and F1 score. Results: Out of 521 patients, 416 (80%) had optimal RT and 105 (20%) suboptimal RT , the proportion being respected througouth the training/validation and testing groups. The median age at diagnosis was 60.0 years (IQR, 53.0-67.0). ViT models showed better performance compared to CNN in terms of accuracy (0.80 and 0.77 vs. 0.62) and F1 score (0.43 and 0.33 vs. 0.12). Conclusions: A pioneering ViT-based model was capable to predict tumor resectability from pre-operative CT scans in patients with advanced EOC. This improvement in performance, compared to CNN, might be due to the ViTs' ability to capture long-range dependencies and global context, which are crucial for predicting tumor resectability. In contrast, CNNs are limited by localized feature extraction and inductive bias, hindering their ability to capture the complex spatial relationships. By addressing the limitations of current radiological assessments, the model has the potential to serve as a clinical decision-support tool, enabling personalized management strategies for patients. Future work will focus on further improving the model performance.

e23101 Background: Liver cell carcinoma (LCC) and hepatic failure (HF) are major contributors to global adult mortality. Identifying trends and disparities in LCC and HF-related deaths is critical for addressing public health challenges and guiding prevention strategies. This study examines national trends in LCC and HF-related mortality from 1999 to 2023, with an emphasis on demographic, geographic, and temporal disparities in age-adjusted mortality rates (AAMRs). Methods: Using the CDC WONDER database (1999–2023), this descriptive study identified deaths attributed to LCC (ICD-10 code C22.0) and HF (ICD-10 code K72.9) among adults aged 25 and older. Mortality data were stratified by gender, race/ethnicity, census region, urban-rural classification, and state. Age-adjusted mortality rates (AAMRs) per 1,000,000 individuals were calculated, and trends were analyzed using Joinpoint regression to determine annual percent changes (APCs) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05, and the study followed STROBE guidelines. Results: From 1999 to 2023, there were 22,963 deaths attributed to LCC and HF (Men: 17,822; Women: 5,141). Overall, AAMRs declined from 1999 to 2007 (APC: -2.4), stabilized between 2007 and 2017 (APC: 0.2), and declined again from 2017 to 2023 (APC: -3.5). Men experienced a steady AAMR decline from 1999 to 2010 (APC: -2.1), followed by stabilization (2010–2017, APC: 0.7), and further decline (2017–2023, APC: -4.4). Women showed a sharp AAMR decrease from 1999 to 2006 (APC: -3.8) and a relatively stable trend thereafter (APC: -0.1). Non-Hispanic Blacks people experienced an overall AAMR decline from 1999 to 2020 (APC: -0.9). Among Non-Hispanic Whites people, AAMRs decreased initially (1999–2006, APC: -2.6), rose slightly (2006–2018, APC: 0.5), and declined further (2018–2020, APC: -7.3). Regional AAMRs varied, with the West having the highest overall AAMR (5.6; 95% CI: 5.4–5.7) and the Midwest the lowest (3.2; 95% CI: 3.1–3.3). Hawaii reported the highest state AAMR (10.3; 95% CI: 8.9–11.6), while Arkansas had the lowest (2.4; 95% CI: 2.0–2.9). Urban-rural disparities revealed a sharper AAMR decline in metropolitan areas (1999–2005, APC: -3.9) and the non-metropolitan areas showed a slight increase (1999–2020, APC: 1.3). Conclusions: LCC and HF-related mortality rates have shown significant declines over the past two decades, with variations across gender, race, and geographic locations. Men and urban populations experienced steeper reductions in AAMRs, while rural populations exhibited slower improvements or slight increases in mortality. These findings highlight the need for targeted public health interventions to address disparities and further reduce LCC and HF-related mortality in vulnerable populations.

e17617 Background: Abnormal uterine bleeding (AUB) is a cardinal symptom of endometrial cancer (EC). However, 90-95% of those with AUB will undergo invasive endometrial sampling with the finding of benign etiology. Our group has identified EC-related methylated DNA markers (MDMs) detectable in vaginal fluid obtained via self-collected tampon with high sensitivity to detect EC. To provide alternative vaginal fluid collection options, we compared total DNA recovery and detection of EC MDMs between tampon and swab collection methods. Methods: Patients ≥45 yrs with AUB or undergoing routine gynecologic care, and patients ≥18 yrs with biopsy-confirmed EC or atypical endometrial hyperplasia (AEH) were prospectively enrolled for paired vaginal fluid biospecimens collection. Participants self-collected vaginal fluid via tampon before provider-collected vaginal fluid sampling via swab immediately prior to clinically indicated endometrial sampling or hysterectomy. Cases (EC/AEH) and benign controls were determined by expert review. DNA was extracted from vaginal fluid. Bisulfite converted DNA was then used in long-probe quantitative amplified signal (LQAS) assays for 10 MDMs and 2 reference markers (RMs). Total DNA recovery using the RMs and detection of EC-specific MDMs were compared across sampling techniques. MDMs were standardized using the log of the ratio of the MDM and RM1. Standardized MDMs were compared between swabs and tampons using median and interquartile range (IQR). Results: A total of 40 eligible participants were evaluated in this study, 20 cases and 20 controls. Median time between tampon and swab collection was 27 hours (range 1-53). DNA quantity as measured using both RM1 and RM2 were comparable between swab and tampon in both cases and controls; MDM detection was also comparable between swab and tampon in both cases and controls (Table 1; 8 representative MDMs shown). Though not powered for sensitivity and specificity estimation, EC-related MDMs appear higher in EC cases vs controls, using either sample type. Conclusions: Sampling vaginal fluid with provider-collected swabs or self-collected tampons are both effective for detection of validated EC MDMs. Studies are ongoing to optimize an MDM panel to detect EC in vaginal fluid. Control Tampon Control Swab Case Tampon Case Swab RM1 3.5 [3.3-3.8] 4.2 [3.8-4.2] 3.6 [3.2-3.9] 4.2 [3.8-4.5] MDM1 0.5 [0.4-0.7] 0.4 [0.2-0.6] 1.3 [0.8-1.8] 1.1 [0.6-1.7] MDM2 1.5 [1.2-1.9] 0.9 [0.7-1.2] 3.2 [2.2-4.7] 2.9 [1.7-4.5] MDM3 0.5 [0.4-0.6] 0.4 [0.3-0.5] 1.5 [0.9-2.3] 1.0 [0.8-2.3] MDM4 0.9 [0.6-1.3] 0.5 [0.4-0.7] 2.3 [1.3-3.7] 1.9 [1.0-3.6] MDM5 1.3 [0.6-1.4] 0.8 [0.6-1.0] 3.3 [2.5-4.1] 3.1 [2.4-3.9] MDM6 1.6 [1.3-2.1] 1.2 [0.8-1.7] 3.3 [2.6-4.3] 3.1 [2.3-4.1] MDM7 1.5 [1.1-1.7] 1.0 [0.4-1.4] 3.5 [2.6-4.1] 3.3 [2.1-3.8] MDM8 0.5 [0.3-0.6] 0.5 [0.4-0.7] 1.8 [1.0-2.7] 1.7 [1.0-2.5] Median (IQR) for RM1 and 8 MDMs by case/control and sampling technique.

e16330 Background: Sarcomatoid hepatocellular carcinoma (SHC) is a rare and aggressive variant of hepatocellular carcinoma (HCC), accounting for less than 2% of all primary liver cancers. Characterized by the presence of both epithelial and mesenchymal components, SHC demonstrates a high degree of cellular plasticity, often associated with epithelial-mesenchymal transition. This phenotypic heterogeneity contributes to its aggressive clinical behavior, including rapid tumor progression, early metastasis, and poor response to conventional therapies. As a result, patients diagnosed with SHC typically have a median survival of less than 12 months, significantly lower than that observed in patients with conventional HCC. Methods: We conducted a retrospective study at the Mayo Clinic Comprehensive Cancer Center, focusing on patients diagnosed with SHC between April 2017 and December 2023. The data collected included demographics, performance status, underlying risk factors, alpha-fetoprotein (AFP) levels, next generation sequencing (NGS) testing, and the treatment received. Results: A total of ten patients with confirmed diagnoses of SHC were included, with median age at the time of diagnosis 63.5 years (37-77), predimonantly male (9 out of 10), and identifying white (8 out of 10). Alcoholic cirrhosis was identified as an underlying risk factor in three patients, NASH cirrhosis in two, and HCV in two patients. The majority (6 out of 10) of the patients had localized disease, with 4 patients having denovo metastatic disease with abdominal lymph node involvement. Five patients had NGS testing (Tempus, Guardant 360, and ProvsSeq523), with resultant pathogenic variants including TP53 in 3 patients, NTRK3 fusion, IDH2, EGFR, TERT, MLH1, ARIS1A, and PALB2. Four patients were treated with combined Yitrrium-90 and systemic therapy. Three patient started systemic therapy, and two patients underwent surgical resection. Systemic Therapy used included combined Atezolizumab/ Bevacizuamab, Pembrolizumab/ Lenvatinib, Gemcitabine/ Docetaxel, Gemcitabine/ Cisplatin, Cabozantinib, and pembrolizumab monotherapy. The median overall survival for this group was 11.5 months; 95% confidence interval (CI): 4.66-40.1 months. Seventy percent experienced progression, with a median progression-free survival after first line of treatment of 2.5 months; 95% CI: 1-15.3 months. Conclusions: Sarcomatoid HCC remains aggressive and difficult to treat cancer with limited overall survival. Immunotherapy based regimens have limited efficacy for this HCC variant.