Predictors of Hepatic Decompensation Post-Y90 Treatment in Hepatocellular Carcinoma: New Insights into Segmental TARE and Post-Treatment Dosimetry

Predictors of Hepatic Decompensation Post-Y90 Treatment in Hepatocellular Carcinoma: New Insights into Segmental TARE and Post-Treatment Dosimetry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of Hepatic Decompensation Post-Y90 Treatment in Hepatocellular Carcinoma: New Insights into Segmental TARE and Post-Treatment Dosimetry Ali Afrasiabi, Bahareh Gholami, Andrew M. Moon, Alex Villalobos, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8427893/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background Transarterial radioembolization (TARE) with yttrium-90 (Y-90) microspheres is an established treatment for unresectable hepatocellular carcinoma (HCC). Although segmental TARE offers a favorable safety profile, hepatic decompensation remains a clinically significant complication. This study aimed to identify clinical, laboratory, and post-treatment dosimetric predictors of hepatic decompensation following segmental Y-90 TARE. Methods In this retrospective cohort study, 102 patients with HCC who underwent segmental Y-90 TARE between 2015 and 2025 were analyzed. Baseline demographic, clinical, laboratory, and imaging data were collected. Hepatic decompensation was defined as new or worsening ascites, hepatic encephalopathy, or bilirubin elevation greater than three times the upper limit of normal within 3 ± 1 month after treatment. Post-treatment dosimetry was performed using SPECT/CT with voxel-based analysis. Univariate and multivariate logistic regression models were used to identify predictors of hepatic decompensation. Results Hepatic decompensation occurred in 14 patients (13.7%). On univariate analysis, decompensation was associated with baseline hypoalbuminemia (< 3.5 g/dL), higher MELD and ALBI scores, Child-Pugh class B/C, INR ≥ 1.2, lower white blood cell count, and higher alkaline phosphatase levels. Patients who developed decompensation received lower median absorbed radiation doses to the treated segment (133 Gy vs. 196 Gy, p = 0.01) and had smaller total liver volumes (1536 cm³ vs. 1699 cm³, p = 0.02). In multivariate analysis, baseline hypoalbuminemia (< 3.5 g/dL) was the only independent predictor of hepatic decompensation (OR = 7.98, 95% CI 1.16–80.39, p = 0.04). Conclusion Hepatic decompensation after segmental Y-90 TARE is primarily driven by impaired baseline hepatic reserve rather than post-treatment dosimetry. Baseline hypoalbuminemia is a strong independent predictor of early hepatic decompensation and should be carefully considered during patient selection for radioembolization. Liver Cancer Transarterial Radioembolization Locoregional Therapy Hepatic Decompensation Figures Figure 1 Figure 2 Introduction Hepatocellular carcinoma (HCC) accounts for approximately 75–85% of primary liver cancers and is the third leading cause of cancer-related mortality worldwide [ 1 ]. Despite advances in the treatment of viral hepatitis and the implementation of surveillance programs, the global incidence of HCC continues to increase. For patients with unresectable HCC, transarterial radioembolization (TARE) using yttrium-90 (Y-90) microspheres is an established locoregional treatment option [ 2 ]. TARE exploits the preferential hepatic arterial blood supply of HCC and delivers targeted radiation with minimal embolic effect compared with transarterial chemoembolization (TACE), allowing its use in patients with portal vein thrombosis [ 3 – 5 ]. Although TARE is generally well tolerated, hepatic decompensation remains a clinically important complication, particularly in patients with underlying cirrhosis, and has been associated with Barcelona-Clinic Liver Cancer (BCLC) stage migration and reduced survival following radiation-based therapies [ 6 – 8 ]. The therapeutic effect of Y-90 TARE is mediated by beta radiation–induced tumor necrosis. The ablative potential of segmental Y-90 TARE has been demonstrated in the LEGACY study, which reported high rates of complete pathological necrosis when absorbed tumor doses exceed 400 Gy [ 9 , 10 ]. Treatment dosing strategies depend on microsphere type and baseline liver function, and recent efforts have focused on individualized dosimetry to optimize tumor control while limiting hepatic toxicity [ 11 , 12 ]. Despite these advances, the role of post-treatment dosimetry in predicting treatment-related complications, particularly hepatic decompensation, remains incompletely defined for both glass and resin Y-90 microspheres [ 13 , 14 ]. Dosimetric assessment is further complicated by variability in microsphere distribution, tumor vascularity, and particle density. Although pre-clinical studies suggest that higher microsphere density may improve tumor control, these findings have not been consistently confirmed in clinical studies [ 15 ]. While the overall rate of severe adverse events after TARE is low, most patients with HCC have underlying chronic liver disease, which increases their susceptibility to hepatic decompensation following radiation-based therapies. In recent years, there has been a shift from lobar TARE toward segmental TARE to improve treatment efficacy while preserving uninvolved liver parenchyma and reducing toxicity [ 16 ]. However, predictors of hepatic decompensation specific to segmental TARE remain poorly characterized. The aim of this study was to identify clinical, laboratory, and post-treatment dosimetric predictors of hepatic decompensation in patients with HCC undergoing segmental Y-90 TARE. By incorporating detailed voxel-based dosimetry, we sought to better understand the relative contribution of baseline hepatic reserve and treatment-related factors, with the goal of improving patient selection, treatment planning, and procedural safety. Study Design and Patient Population This retrospective cohort study was conducted at a single tertiary academic medical center. All patient data were handled in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Protected health information was de-identified prior to analysis, and access to the data was restricted to authorized study personnel. Institutional review board approval was obtained, and the requirement for informed consent was waived due to the retrospective nature of the study. The study included 102 consecutive patients with hepatocellular carcinoma (HCC) who underwent segmental yttrium-90 (Y-90) transarterial radioembolization (TARE) between January 2015 and December 2024. Inclusion criteria were: (1) diagnosis of unresectable HCC based on imaging or histopathology in accordance with the American Association for the Study of Liver Diseases (AASLD) guidelines; (2) treatment with segmental Y-90 TARE; and (3) availability of complete baseline clinical, laboratory, and post-treatment imaging data with a minimum follow-up of 2 months. Exclusion criteria included lobar or bilobar TARE, prior liver transplantation, concurrent systemic therapy at the time of TARE, or incomplete follow-up data. Data Collection Baseline demographic, clinical, laboratory, and imaging data were extracted from electronic medical records and imaging reports. Collected demographic variables included age and sex. Etiology of liver disease was categorized as alcohol-associated liver disease (ALD), hepatitis B virus (HBV), hepatitis C virus (HCV), metabolic dysfunction–associated steatotic liver disease (MASLD), or other etiologies (including hemochromatosis, Wilson disease, and unknown etiology). Eastern Cooperative Oncology Group (ECOG) performance status was recorded as 0 or 1–2. Baseline laboratory values obtained within 30 days prior to TARE included white blood cell count (WBC), platelet count, creatinine, albumin, total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), prothrombin time (PT), international normalized ratio (INR), and total protein. Liver function was assessed using the Model for End-Stage Liver Disease (original MELD) score, Albumin-Bilirubin (ALBI) score and grade, and Child-Pugh classification (A or B/C). Tumor-related variables included Barcelona Clinic Liver Cancer (BCLC) stage (A, B, or C), maximum tumor diameter (cm), tumor volume (cm³), number of lesions (single, two, or multiple), and prior liver-directed therapies, including transarterial chemoembolization (TACE), prior TARE, stereotactic body radiation therapy (SBRT), microwave ablation, radiofrequency ablation, or cryoablation. Y-90 Transarterial Radioembolization Procedure Segmental TARE was performed using either glass (TheraSphere®) or resin (SIR-Spheres®) Y-90 microspheres, at the discretion of treating interventional radiologist. Treatment planning included technetium-99m macroaggregated albumin (Tc-99m MAA) scintigraphy to assess lung shunt fraction (LSF%) and to confirm that single-session and cumulative lifetime lung radiation doses remained below 30 Gy and 50 Gy, respectively. Segmental TARE targeted physiologically expendable hepatic segments with the intent to deliver an ablative absorbed radiation dose. Dosimetry for all segmental treatments was calculated using the single-compartment Medical Internal Radiation Dose (MIRD) model. Liver volume (cm³) treated segment volume (cm³), and tumor volume (cm³) were derived from post-treatment imaging using MIM software (MIM Software Inc., Cleveland, OH). Post-Treatment Dosimetry All patients underwent Y-90 single-photon emission computed tomography/computed tomography (SPECT/CT) within 2 hours following the procedure. Voxel-based dosimetry was performed using MIM software, with segmentation of tumor, treated segment, and whole liver volumes. Extracted dosimetric and volumetric parameters included mean absorbed dose (Gy) to the tumor and treated segment, tumor volume (cm³), segment volume (cm³), total liver volume (cm³), segment-to-liver volume ratio (%), and percentage of preserved non-irradiated liver parenchyma. Outcome Assessment Hepatic decompensation was defined as any of the following occurring within 3 ± 1 months after TARE in the absence of radiographic tumor progression: (1) total bilirubin exceeding three times the upper limit of normal, (2) new-onset or worsening hepatic encephalopathy, or (3) new-onset ascites. Baseline hypoalbuminemia and hyperbilirubinemia were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0: hypoalbuminemia (grade 1: 3.0–3.5 g/dL; grade 2: 2.0–3.0 g/dL; grade 3: ≤2.0 g/dL) and hyperbilirubinemia (grade 1: 1.2–1.8 mg/dL; grade 2: >1.8–3.6 mg/dL; grade 3: >3.6 mg/dL). Follow-up clinical assessments and laboratory data were obtained at routine post-procedural visits within the defined follow-up window. Statistical Analysis Continuous variables were reported as medians with interquartile ranges (IQR) and compared between decompensated and non-decompensated groups using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, with comparisons performed via chi-square or Fisher’s exact tests. Univariate analyses assessed associations between baseline variables, tumor characteristics, and dosimetry and hepatic decompensation, with p-values < 0.05 considered significant. A multivariate logistic regression model was then constructed to identify independent predictors of decompensation, including variables with p < 0.05 in univariate analysis reporting odds ratios (OR) with 95% confidence intervals (CI). Data was analyzed using R Studio (R version 4.3.3). Results Patient Characteristics A total of 102 patients with hepatocellular carcinoma (HCC) treated with segmental yttrium-90 (Y-90) transarterial radioembolization (TARE) were included in the analysis. The median age was 66.8 years (interquartile range [IQR]: 59–72.3), and 78 patients (76.5%) were male. Cirrhosis was present in 96 patients (94.1%). The most common etiologies of liver disease were hepatitis C virus (52.9%), alcohol-associated liver disease (40.1%), and metabolic dysfunction–associated steatotic liver disease (33.0%). Some patients had more than one underlying etiology. Table 1 , Baseline Characteristics of Patients in the segmental TARE. † Continuous variables are reported as median (interquartile range). ‡ Categorical variables are reported as number (percentage). Bilirubin grade G1: 1.2–1.8 mg/dL, 3.6 > G2 > 1.8 mg/dL, G3 > 3.6. Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BCLC, Barcelona Liver Cancer group classification; ECOG, Eastern Cooperative of Oncology Group; mCi, millicurie; Gy, Gray; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; INR, international normalized ratio; MELD, Model for End-Stage Liver Disease; MASLD, metabolic dysfunction-associated steatotic liver disease; WBC, white blood cells; LSF, lung shunt fraction. Other ethiology = Hemochromatosis, Alpha 1 anti-trypsin deficiency. Variable No decompensation N = 88 Decompensated N = 14 P-value Age ( year) 64.54(59–72) 68.71(64.8–74.2) 0.03* Gender ‡ 0.08 Female 18(20.5) 6(43%) Male 70(79.5%) 8(57%) LSF% † 1.5 (0.5–3.2) 2.2 (1.5–4.7) 0.1 De Novo treatment 67(76%) 9(64%) 0.5 Tumor size (cm) † 3.3 (2.1, 4.6) 2.6 (1.9, 4.1) 0.2 Baseline laboratory data : WBC † 5.3(4-7.1) 4.1(3.75-5) 0.007* WBC category ‡ 0.05 4500 59(67%) 5(36%) Platelets † 120(83.8–176) 84.5(69.8–120) 0.04* Platelet category ‡ 0.2 150 29(33%) 2(14.3%) Bilirubin † mg/dl 0.9(0.6–1.2) 1.1(0.77-2) 0.2 Bilirubin Grade ‡ 0.03* 0 66(75%) 9(64%) 1 16(18%) 1(7%) 2 6(7%) 4(29%) AST † U/L 44(33–61) 48(36-62.5) 0.4 ALT † U/L 34(21–51) 31(25-36.5) 0.7 ALP † U /L 106(89–135) 137(123–180) 0.02* PT † Second 13(11.8–13.6) 13.4(12.8–15.4) 0.04* INR † 1.1(1.04–1.2) 1.2(1.13–1.37) 0.02* INR Category ‡ 0.1 INR > = 1.2 22(25%) 7(50%) INR < 1.2 66(75%) 7(50%) Total protein † g/dl 7.2(6.7, 7.6) 6.6(6, 7.1) 0.009 Albumin Level ‡ 0.001* =3.5 g/dl 56(64%) 2(14%) Tumor characteristics BCLC‡ 0.6 A 41(47%) 8(57%) B 47(53%) 6(43%) MELD score ‡ 0.009* 10 19(22%) 8(57%) MELD score † 8(7–10) 11(8–14) 0.01* ALBI grade ‡ 0.2 grade 1 24(27%) 1(7%) grade 2 /3 64(73%) 13(93%) ALBI score † -2.37 (-2.6 - -2) -1.75 (-2.1 - -1.5) < 0.001* Child-Pugh score ‡ 0.04* A 68(77%) 7(50%) B/C 20(23%) 7(50%) ECOG ‡ 1 0 48(54.5%) 7(50%) 1,2 40(45.5%) 7(50%) Etiology of liver disease ‡ Alcohol 35(38%) 6(37%) 1 HBV 2(2%) 1(6%) 0.4 HCV 50(54%) 4(25%) 0.06 MASLD 29(31%) 5(31%) 1 Other 8(9%) 3(19%) 0.2 Hepatic Decompensation Hepatic decompensation occurred in 14 of 102 patients (13.7%) within 3 ± 1 months following segmental TARE. Clinical manifestations included new or worsening ascites (n = 8), hepatic encephalopathy (n = 1), and bilirubin elevation greater than three times the upper limit of normal (n = 10), with several patients experiencing more than one decompensation event. Patients who developed hepatic decompensation were older than those without decompensation (median age 68.7 vs. 64.5 years, p = 0.03). There were no significant differences between groups with respect to sex, lung shunt fraction, de novo versus previously treated disease, tumor size, BCLC stage, ECOG performance status, or microsphere type. Baseline Laboratory and Liver Function Parameters Patients who developed hepatic decompensation demonstrated significantly worse baseline liver function compared with those without decompensation. Median white blood cell count was lower in the decompensation group (4.1 vs. 5.3 ×10⁹/L, p = 0.007), and thrombocytopenia was more pronounced (median platelet count 84.5 vs. 120 ×10⁹/L, p = 0.04). Alkaline phosphatase levels were higher in the decompensation group (137 vs. 106 U/L, p = 0.02), as were prothrombin time (13.4 vs. 13.0 seconds, p = 0.04) and international normalized ratio (1.2 vs. 1.1, p = 0.02). Hypoalbuminemia was strongly associated with hepatic decompensation. Albumin levels < 3.5 g/dL were present in 85.7% of patients who developed decompensation compared with 36.4% of those who did not (p = 0.001). Total bilirubin levels did not differ significantly between groups; however, higher bilirubin grade distribution was observed in the decompensation group (p = 0.03). Global liver function scores also differed significantly between groups. Patients with hepatic decompensation had higher MELD scores (median 11 vs. 8, p = 0.01), higher ALBI scores (–1.75 vs. − 2.37, p < 0.001), and a greater proportion of Child-Pugh class B/C disease (50% vs. 22.7%, p = 0.04). Dosimetric and Volumetric Parameters Post-treatment dosimetric analysis demonstrated that patients who developed hepatic decompensation received lower median absorbed radiation doses to the treated segment compared with those without decompensation (133 Gy vs. 196 Gy, p = 0.01). A similar trend was observed for mean tumor absorbed dose (394 Gy vs. 500 Gy), although this did not reach statistical significance (p = 0.05). Volumetric analysis showed that patients with hepatic decompensation had smaller total liver volumes (median 1536 cm³ vs. 1699 cm³, p = 0.02). There were no significant differences between groups in treated segment volume, tumor volume, or percentage of preserved non-irradiated liver. A trend toward a higher segment-to-liver volume ratio was observed in the decompensation group, but this did not reach statistical significance (21.0% vs. 16.1%, p = 0.07). Table 2 Dosimetric and volumetric data for predicting hepatic decompression in segmental TARE. † Continuous variables are reported as median (interquartile range). ‡ Categorical variables are reported as number (percentage). Variable No decompensation N = 88 Decompensated N = 14 P-value Tumor volume† ml 15.8(9.95,35.9) 14.3(6.17,23.9) 0.4 Angiosome volume† ml 275(195,431) 322(290,371) 0.3 Liver volume† ml 1699(1411,2146) 1536(1346,1664) 0.02* Mean Tumor Absorbed dose† Gy 500(333–804) 394(303–419) 0.05 Mean Angiosome absorbed dose† Gy 196(137–284) 133(117–155) 0.01* Angiosome volume to liver volume ratio† 16% (10.8–26.5) 21% (18.3–24.6) 0.07 Non-radiated liver volume to liver volume ratio † 84% (73–89) 79% (75–81) 0.07 Microsphere Type‡ 0.3 Resin 30(34.1%) 8(57.1%) Glass 58(65.9%) 6(42.9%) Multivariate Analysis Variables with a p-value < 0.05 on univariate analysis were included in the multivariate logistic regression model. Baseline hypoalbuminemia (< 3.5 g/dL) was the only independent predictor of hepatic decompensation (odds ratio 7.98, 95% confidence interval 1.16–80.39, p = 0.04). Age, MELD score, ALP, bilirubin grade, Child-Pugh class, and post-treatment dosimetric parameters were not independently associated with hepatic decompensation after adjustment. Table 3 , Multivariate model for predicting hepatic decompression in segmental TARE. Bilirubin grade Parameters OR 95%CI p-value Age 1.08 0.98, 1.2 0.1 Female ref 0.7 Male 0.71 1.43, 3.86 WBC = > 4500 ref 0.1 WBC 3.5 g/dl ref 0.04* Albumin < 3.5 g/dl 7.98 1.16, 80.39 ALP 147 U/L 1.5 0.3, 7.5 Bilirubin grade 0 ref Bilirubin grade 1 0.13 0.005, 1.2 0.1 Bilirubin grade 2 1.08 0.08, 16.1 0.9 Meld score 10 1.8 0.2, 14 Child-Pugh score A ref 0.5 Child-Pugh score B/C 0.46 0.04, 3.7 Mean Tumor absorbed dose Gy 0.99 0.98, 1.01 0.2 Mean Segment absorbed dose Gy 0.99 0.98, 1.01 0.9 Liver volume ml 0.99 0.99, 1 0.9 G1: 1.2–1.8 mg/dL, 3.6 > G2 > 1.8 mg/dL, G3 > 3.6. Abbreviations: ALP, alkaline phosphatase; Gy, Gray; MELD, Model for End-Stage Liver Disease; MASLD, metabolic dysfunction-associated steatotic liver disease; WBC, white blood cells. Discussion In this retrospective cohort of patients with hepatocellular carcinoma undergoing segmental yttrium-90 (Y-90) transarterial radioembolization (TARE), hepatic decompensation occurred in 14 of 102 patients (13.7%) within 3 ± 1 months. This rate is lower than the decompensation rates reported in studies that primarily evaluated lobar treatment. Reincke et al. reported hepatic decompensation in approximately 28% of patients after TARE and identified impaired baseline liver function as a major driver of toxicity [ 20 ]. Similarly, Wong et al. reported clinically significant hepatic decompensation requiring admission in 10.9% of patients within 60 days after (lobar) Y-90 TARE and emphasized the importance of baseline liver function and Child-Pugh score for patient selection [ 19 ]. Compared with these lobar cohorts [ 19 , 20 ], our lower event rate is consistent with the expected safety advantage of segmental treatment, which aims to limit radiation exposure to non-tumoral liver parenchyma [ 16 ]. On univariate analysis, patients who developed decompensation had worse baseline liver function, including lower albumin, higher MELD and ALBI scores, a higher proportion of Child-Pugh class B/C disease, and higher INR and alkaline phosphatase levels. This pattern is consistent with prior reports showing that reduced hepatic reserve increases the risk of post-TARE hepatic dysfunction [ 19 , 20 ]. However, after multivariable adjustment, baseline hypoalbuminemia (< 3.5 g/dL) remained the only independent predictor of hepatic decompensation in our cohort (OR 7.98). Wong et al. similarly identified preprocedure albumin 1.2, ascites, and elevated MELD and Child-Pugh scores [ 19 ]. Reincke et al. reported ALBI score as a key multivariable predictor of decompensation after TARE [ 20 ]. Taken together, these studies support the concept that albumin-based metrics (albumin alone and ALBI) capture the vulnerability of hepatic reserve in patients undergoing radioembolization [ 19 , 20 ]. Because our study focused on segmental TARE, albumin alone may be a practical and clinically accessible marker to identify patients at higher risk even when a selective approach is used. We did not identify a significant association between microsphere type (glass versus resin) and hepatic decompensation. This finding aligns with prior comparative studies suggesting similar safety profiles between glass and resin microspheres when patients are appropriately selected [ 22 , 23 ]. While some reports have suggested potential efficacy differences between platforms (e.g., response rates or survival) [ 22 , 23 ], our results indicate that platform choice was not a major determinant of early hepatic decompensation in segmental TARE. We incorporated voxel-based post-treatment dosimetry to explore whether delivered dose or treated liver volume correlated with hepatic decompensation. Patients who developed decompensation had lower mean segment absorbed dose (133 Gy vs. 196 Gy). This direction of association likely reflects treatment selection and dose de-escalation in patients with poorer baseline liver function rather than a causal relationship between lower segment dose and hepatic toxicity. This interpretation is supported by the lack of an independent dosimetric association in multivariable analysis. Prior work has emphasized that adequate hepatic functional reserve is central to minimizing radioembolization-induced liver injury, even as dosimetry methods become more sophisticated [ 21 ]. In addition, ablative dose concepts are most strongly linked to tumor response and complete pathological necrosis rather than hepatic decompensation, as demonstrated in LEGACY and related dosimetry literature [ 9 , 10 ]. We observed that patients who developed decompensation had smaller total liver volumes. Although the percent of non-irradiated liver and the segment-to-liver volume ratio did not reach statistical significance in our cohort, volume-based hepatic reserve remains biologically plausible as a contributor to post-treatment tolerance. Prior work in ablative radioembolization has also evaluated liver-treated fraction and biochemical toxicity thresholds, supporting the importance of preserved functional parenchyma (especially when pursuing high-dose selective approaches) [ 28 ]. In addition, broader hepatology guidance on preventing and managing complications of radioembolization supports careful selection based on baseline liver function and risk factors for radioembolization-induced liver disease [ 29 ]. Future studies should evaluate volume-based metrics normalized to body size and functional liver volume and should assess whether integrating volumetry with albumin-based measures improve risk stratification for segmental TARE. This study has limitations. The retrospective design introduces potential selection bias. The number of decompensation events was modest, which limits statistical power and precision of confidence intervals. The follow-up window of 3 ± 1 months may miss later hepatic decompensation; longer-term outcome studies have reported late events after Y-90 TARE [ 27 ]. Finally, prior liver-directed therapies and treatment heterogeneity may affect baseline hepatic reserve, although we did not observe a significant association between de novo versus previously treated disease and decompensation. Conclusion In patients with hepatocellular carcinoma undergoing segmental yttrium-90 transarterial radioembolization, hepatic decompensation occurred in 13.7% of cases. Baseline hypoalbuminemia (< 3.5 g/dL) was the only independent predictor of early hepatic decompensation, underscoring the importance of hepatic functional reserve in patient selection. Post-treatment dosimetric and volumetric parameters, as well as microsphere type, were not independently associated with decompensation. These findings highlight serum albumin as a simple and clinically useful marker for risk stratification before segmental Y-90 radioembolization. Declarations Funding/Support: Dr. Kokabi receives research support from SIRTeX Medical. The remaining authors have no funding/support disclosures. Conflict of Interest Disclosures: Dr. Kokabi is a consultant for SIRTeX Medical, Boston Scientific, Okami Medical, Terumo Medical, Trisalus Life Sciences Dr. Villalobos is a consultant for SIRTeX Medical. The remaining authors have no conflicts of interest to disclose. For this retrospective study, formal consent was not required. No additional informed consent was required to participate in this retrospective study. The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Competing interests: not applicable Acknowledgement: not applicable Author Contributions: Afrasiabi: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, visualization, writing – original draft, and writing – review/editing. Gholami: Conceptualization, data curation, formal analysis, investigation, resources, software, visualization, writing – original draft, and writing – review/editing. Moon: Conceptualization, data curation, formal analysis, investigation, resources, software, visualization, writing – original draft, and writing – review/editing. Villalobos: Data curation, formal analysis, writing – review/editing. Moon: Writing – review/editing. Pisanie: Writing – review/editing. Du Pisanie: Writing – review/editing. Harris: Writing – review/editing. Yu: Writing – review/editing. Mauro: Writing – review/editing. Mohnasky: Writing – review/editing. Gad: Writing – review/editing. Kokabi: Conceptualization, methodology, project administration, resources, supervision, validation, writing – original draft, and writing – review/editing. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Hepatic decompensation after transarterial radioembolization: A retrospective analysis of risk factors and outcome in patients with hepatocellular carcinoma. Hepatol Commun. 2022;6(11):3223–33. Lam M, Garin E, Maccauro M, Kappadath SC, Sze DY, Turkmen C, et al. A global evaluation of advanced dosimetry in transarterial radioembolization of hepatocellular carcinoma with Yttrium-90: the TARGET study. Eur J Nucl Med Mol Imaging. 2022;49(10):3340–52. Van Der Gucht A, Jreige M, Denys A, Blanc-Durand P, Boubaker A, Pomoni A, et al. Resin Versus Glass Microspheres for (90)Y Transarterial Radioembolization: Comparing Survival in Unresectable Hepatocellular Carcinoma Using Pretreatment Partition Model Dosimetry. J Nucl Med. 2017;58(8):1334–40. Villalobos A, Arndt L, Cheng B, Dabbous H, Loya M, Majdalany B, et al. Yttrium-90 Radiation Segmentectomy of Hepatocellular Carcinoma: A Comparative Study of the Effectiveness, Safety, and Dosimetry of Glass-Based versus Resin-Based Microspheres. J Vasc Interv Radiol. 2023;34(7):1226–34. Elsawy AA, Dawoud MM, Elarabawy RA, Mohamed WS, Dawoud RM. Role of residual liver volumetry and function in prediction of liver tolerability after transarterial chemoembolization for hepatocellular carcinoma in cirrhotic patients: deriving a clinical decision support score. Egypt J Radiol Nuclear Med. 2020;51(1):152. Lee S, Choi J, Park JH, Lim CY, Yang E, Yoon SM, et al. Dynamic liver volume change in predicting hepatic decompensation and long-term effects of stereotactic body radiation therapy. J Gastroenterol Hepatol. 2024;39(8):1648–55. Reincke M, Schultheiss M, Doppler M, Verloh N, Uller W, Sturm L, et al. Hepatic decompensation after transarterial radioembolization: A retrospective analysis of risk factors and outcome in patients with hepatocellular carcinoma. Hepatol Commun. 2022;6(11):3223–33. Lee HM, Alder L, Nguyen M, Dougherty SC, Qu Y, Thacker LR, et al. Long-term outcome analysis of Y90 radioembolization in hepatocellular carcinoma. J Gastrointest Oncol. 2023;14(3):1378–91. De la Garza-Ramos C, Gabr A, Riaz A, et al. Biochemical safety of ablative yttrium-90 radioembolization: Percent liver treated thresholds associated with biochemical toxicity. J Hepatocell Carcinoma. 2021;8:249–60. 10.2147/JHC.S294803 . Sangro B, Martínez-Urbistondo D, Bester L, et al. Prevention and treatment of complications of selective internal radiation therapy (radioembolization). Hepatology. 2017;66(2):654–67. 10.1002/hep.29107 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviews received at journal 07 Feb, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 28 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 22 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8427893","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566972180,"identity":"9989e04f-3573-4ed4-b3a3-3b629d6800cd","order_by":0,"name":"Ali Afrasiabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACNnbGBoYEGyCLmYGNIaECyDgAkQGKYwf8zECpD2kwLWeI0CLZDJSckQaxkYGxjQgtBoeZG5h5Eu7Jy7czP3vwcN42Ob4DPGYPfjDYyG44gEsLI0hLseGGw2zmBonbbhtLHuAxN+xhSDPGq4X3RwLjBmYGMwmglsQNQFukGRgOJ+LSYg/S8ichwX5+M/s3icQ5t+uhWv7j1AJ1WEJiw2EeoC0NtxMMIFoOENSSvOEwT7lBwrHbhjMPs5VJ9hgkG8/EpeV4+wOQFtv5/ce3PfxRc1ue73jzNokfFXayfTi0AAH7D1Q+M9gonMpHwSgYBaNgFBABAH/BXsAWkx/QAAAAAElFTkSuQmCC","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Afrasiabi","suffix":""},{"id":566972182,"identity":"2ade570b-1fdb-4601-b7a5-f00fbaa67039","order_by":1,"name":"Bahareh Gholami","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Bahareh","middleName":"","lastName":"Gholami","suffix":""},{"id":566972190,"identity":"95c9ff64-4585-4436-85af-0be8491bffb9","order_by":2,"name":"Andrew M. 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12:47:39","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108178,"visible":true,"origin":"","legend":"","description":"","filename":"220b266083ee47fe92b20a8451b1cc721structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8427893/v1/93ad99a6de4812085df48b53.xml"},{"id":99341293,"identity":"bc4f823e-a27d-44cd-8f24-a091c593e5eb","added_by":"auto","created_at":"2026-01-01 06:20:12","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122059,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8427893/v1/6b944644a13ebb884e43e1d7.html"},{"id":99788527,"identity":"bf8e6d98-b1e5-41f4-a8fb-0cd9f80d360f","added_by":"auto","created_at":"2026-01-08 12:47:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162640,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patients included and excluded from the study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8427893/v1/0756574ebe6e0d4bba936fd5.png"},{"id":99788965,"identity":"c4eccd6b-9c06-4325-8cda-2d5ab5e53e7b","added_by":"auto","created_at":"2026-01-08 12:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":310324,"visible":true,"origin":"","legend":"\u003cp\u003eDosimetry and volumetry in a 71-year-old patient. A total of 21 mCi of resin Y90 was injected in segment 5. A. Pretreatment MRI (axial, arterial phase, T1-weighted) shows a hyperenhancing lesion in segment 5 of the liver, consistent with hepatocellular carcinoma. The lesion is well-circumscribed with arterial phase hyperenhancement. B. post-treatment voxel-based dosimetry (CT-SPECT fusion) demonstrates focal Y-90 activity confined to the treated hepatic segment. Iso-dose contours from 20% to 95% (color-coded) are overlaid on axial CT, with the tumor region (red inner ROI) receiving a mean absorbed dose of 900 Gy. The surrounding segment parenchyma absorbed ~267 Gy, with preserved non-treated liver parenchyma outside the high-dose field. C. SPECT-based dose map (heat map rendering) shows concentrated microsphere deposition and absorbed radiation within the tumor zone. The 95% isodose shell confirms conformal, high-dose coverage, while the peripheral liver remains largely spared.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8427893/v1/a025080f5b2106e85f681608.png"},{"id":99801698,"identity":"dd685f1a-6791-4b01-b324-f70c10252b0c","added_by":"auto","created_at":"2026-01-08 14:07:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8427893/v1/0b3dad80-6ec9-4b07-be01-7aff4c4f5228.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of Hepatic Decompensation Post-Y90 Treatment in Hepatocellular Carcinoma: New Insights into Segmental TARE and Post-Treatment Dosimetry","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) accounts for approximately 75–85% of primary liver cancers and is the third leading cause of cancer-related mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in the treatment of viral hepatitis and the implementation of surveillance programs, the global incidence of HCC continues to increase. For patients with unresectable HCC, transarterial radioembolization (TARE) using yttrium-90 (Y-90) microspheres is an established locoregional treatment option [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. TARE exploits the preferential hepatic arterial blood supply of HCC and delivers targeted radiation with minimal embolic effect compared with transarterial chemoembolization (TACE), allowing its use in patients with portal vein thrombosis [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although TARE is generally well tolerated, hepatic decompensation remains a clinically important complication, particularly in patients with underlying cirrhosis, and has been associated with Barcelona-Clinic Liver Cancer (BCLC) stage migration and reduced survival following radiation-based therapies [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe therapeutic effect of Y-90 TARE is mediated by beta radiation–induced tumor necrosis. The ablative potential of segmental Y-90 TARE has been demonstrated in the LEGACY study, which reported high rates of complete pathological necrosis when absorbed tumor doses exceed 400 Gy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Treatment dosing strategies depend on microsphere type and baseline liver function, and recent efforts have focused on individualized dosimetry to optimize tumor control while limiting hepatic toxicity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite these advances, the role of post-treatment dosimetry in predicting treatment-related complications, particularly hepatic decompensation, remains incompletely defined for both glass and resin Y-90 microspheres [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDosimetric assessment is further complicated by variability in microsphere distribution, tumor vascularity, and particle density. Although pre-clinical studies suggest that higher microsphere density may improve tumor control, these findings have not been consistently confirmed in clinical studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While the overall rate of severe adverse events after TARE is low, most patients with HCC have underlying chronic liver disease, which increases their susceptibility to hepatic decompensation following radiation-based therapies. In recent years, there has been a shift from lobar TARE toward segmental TARE to improve treatment efficacy while preserving uninvolved liver parenchyma and reducing toxicity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, predictors of hepatic decompensation specific to segmental TARE remain poorly characterized.\u003c/p\u003e \u003cp\u003eThe aim of this study was to identify clinical, laboratory, and post-treatment dosimetric predictors of hepatic decompensation in patients with HCC undergoing segmental Y-90 TARE. By incorporating detailed voxel-based dosimetry, we sought to better understand the relative contribution of baseline hepatic reserve and treatment-related factors, with the goal of improving patient selection, treatment planning, and procedural safety.\u003c/p\u003e\n\n "},{"header":"Study Design and Patient Population","content":"\u003cp\u003eThis retrospective cohort study was conducted at a single tertiary academic medical center. All patient data were handled in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Protected health information was de-identified prior to analysis, and access to the data was restricted to authorized study personnel. Institutional review board approval was obtained, and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003cp\u003eThe study included 102 consecutive patients with hepatocellular carcinoma (HCC) who underwent segmental yttrium-90 (Y-90) transarterial radioembolization (TARE) between January 2015 and December 2024. Inclusion criteria were: (1) diagnosis of unresectable HCC based on imaging or histopathology in accordance with the American Association for the Study of Liver Diseases (AASLD) guidelines; (2) treatment with segmental Y-90 TARE; and (3) availability of complete baseline clinical, laboratory, and post-treatment imaging data with a minimum follow-up of 2 months. Exclusion criteria included lobar or bilobar TARE, prior liver transplantation, concurrent systemic therapy at the time of TARE, or incomplete follow-up data.\u003c/p\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eBaseline demographic, clinical, laboratory, and imaging data were extracted from electronic medical records and imaging reports. Collected demographic variables included age and sex. Etiology of liver disease was categorized as alcohol-associated liver disease (ALD), hepatitis B virus (HBV), hepatitis C virus (HCV), metabolic dysfunction–associated steatotic liver disease (MASLD), or other etiologies (including hemochromatosis, Wilson disease, and unknown etiology). Eastern Cooperative Oncology Group (ECOG) performance status was recorded as 0 or 1–2.\u003c/p\u003e\u003cp\u003eBaseline laboratory values obtained within 30 days prior to TARE included white blood cell count (WBC), platelet count, creatinine, albumin, total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), prothrombin time (PT), international normalized ratio (INR), and total protein. Liver function was assessed using the Model for End-Stage Liver Disease (original MELD) score, Albumin-Bilirubin (ALBI) score and grade, and Child-Pugh classification (A or B/C).\u003c/p\u003e\u003cp\u003eTumor-related variables included Barcelona Clinic Liver Cancer (BCLC) stage (A, B, or C), maximum tumor diameter (cm), tumor volume (cm³), number of lesions (single, two, or multiple), and prior liver-directed therapies, including transarterial chemoembolization (TACE), prior TARE, stereotactic body radiation therapy (SBRT), microwave ablation, radiofrequency ablation, or cryoablation.\u003c/p\u003e\u003ch3\u003eY-90 Transarterial Radioembolization Procedure\u003c/h3\u003e\u003cp\u003eSegmental TARE was performed using either glass (TheraSphere®) or resin (SIR-Spheres®) Y-90 microspheres, at the discretion of treating interventional radiologist. Treatment planning included technetium-99m macroaggregated albumin (Tc-99m MAA) scintigraphy to assess lung shunt fraction (LSF%) and to confirm that single-session and cumulative lifetime lung radiation doses remained below 30 Gy and 50 Gy, respectively.\u003c/p\u003e\u003cp\u003eSegmental TARE targeted physiologically expendable hepatic segments with the intent to deliver an ablative absorbed radiation dose. Dosimetry for all segmental treatments was calculated using the single-compartment Medical Internal Radiation Dose (MIRD) model. Liver volume (cm³) treated segment volume (cm³), and tumor volume (cm³) were derived from post-treatment imaging using MIM software (MIM Software Inc., Cleveland, OH).\u003c/p\u003e\u003ch3\u003ePost-Treatment Dosimetry\u003c/h3\u003e\u003cp\u003eAll patients underwent Y-90 single-photon emission computed tomography/computed tomography (SPECT/CT) within 2 hours following the procedure. Voxel-based dosimetry was performed using MIM software, with segmentation of tumor, treated segment, and whole liver volumes. Extracted dosimetric and volumetric parameters included mean absorbed dose (Gy) to the tumor and treated segment, tumor volume (cm³), segment volume (cm³), total liver volume (cm³), segment-to-liver volume ratio (%), and percentage of preserved non-irradiated liver parenchyma.\u003c/p\u003e\u003ch3\u003eOutcome Assessment\u003c/h3\u003e\u003cp\u003eHepatic decompensation was defined as any of the following occurring within 3 ± 1 months after TARE in the absence of radiographic tumor progression: (1) total bilirubin exceeding three times the upper limit of normal, (2) new-onset or worsening hepatic encephalopathy, or (3) new-onset ascites.\u003c/p\u003e\u003cp\u003eBaseline hypoalbuminemia and hyperbilirubinemia were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0: hypoalbuminemia (grade 1: 3.0–3.5 g/dL; grade 2: 2.0–3.0 g/dL; grade 3: ≤2.0 g/dL) and hyperbilirubinemia (grade 1: 1.2–1.8 mg/dL; grade 2: \u0026gt;1.8–3.6 mg/dL; grade 3: \u0026gt;3.6 mg/dL). Follow-up clinical assessments and laboratory data were obtained at routine post-procedural visits within the defined follow-up window.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were reported as medians with interquartile ranges (IQR) and compared between decompensated and non-decompensated groups using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, with comparisons performed via chi-square or Fisher’s exact tests. Univariate analyses assessed associations between baseline variables, tumor characteristics, and dosimetry and hepatic decompensation, with p-values \u0026lt; 0.05 considered significant. A multivariate logistic regression model was then constructed to identify independent predictors of decompensation, including variables with p \u0026lt; 0.05 in univariate analysis reporting odds ratios (OR) with 95% confidence intervals (CI). Data was analyzed using R Studio (R version 4.3.3).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 102 patients with hepatocellular carcinoma (HCC) treated with segmental yttrium-90 (Y-90) transarterial radioembolization (TARE) were included in the analysis. The median age was 66.8 years (interquartile range [IQR]: 59\u0026ndash;72.3), and 78 patients (76.5%) were male. Cirrhosis was present in 96 patients (94.1%). The most common etiologies of liver disease were hepatitis C virus (52.9%), alcohol-associated liver disease (40.1%), and metabolic dysfunction\u0026ndash;associated steatotic liver disease (33.0%). Some patients had more than one underlying etiology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Baseline Characteristics of Patients in the segmental TARE. \u0026dagger; Continuous variables are reported as median (interquartile range). \u0026Dagger; Categorical variables are reported as number (percentage). Bilirubin grade G1: 1.2\u0026ndash;1.8 mg/dL, 3.6\u0026thinsp;\u0026gt;\u0026thinsp;G2\u0026thinsp;\u0026gt;\u0026thinsp;1.8 mg/dL, G3\u0026thinsp;\u0026gt;\u0026thinsp;3.6. Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BCLC, Barcelona Liver Cancer group classification; ECOG, Eastern Cooperative of Oncology Group; mCi, millicurie; Gy, Gray; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; INR, international normalized ratio; MELD, Model for End-Stage Liver Disease; MASLD, metabolic dysfunction-associated steatotic liver disease; WBC, white blood cells; LSF, lung shunt fraction. Other ethiology\u0026thinsp;=\u0026thinsp;Hemochromatosis, Alpha 1 anti-trypsin deficiency.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo decompensation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecompensated\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (\u003cb\u003eyear)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.54(59\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.71(64.8\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLSF%\u003c/b\u003e\u0026nbsp;\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.5\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.5\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDe Novo treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size (cm)\u003c/b\u003e \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3 (2.1, 4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (1.9, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline laboratory data\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e\u0026nbsp;\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3(4-7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1(3.75-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC category\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets\u003c/b\u003e\u0026nbsp;\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120(83.8\u0026ndash;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.5(69.8\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet category\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(85.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin\u003c/b\u003e\u0026dagger; mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9(0.6\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(0.77-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin Grade\u003c/b\u003e\u0026nbsp;\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u003c/b\u003e\u0026dagger;\u0026nbsp;U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(33\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(36-62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u003c/b\u003e\u0026dagger;\u0026nbsp;U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(21\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(25-36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALP\u003c/b\u003e\u0026dagger; \u003cb\u003eU\u003c/b\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(89\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137(123\u0026ndash;180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePT\u003c/b\u003e\u0026dagger; Second\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(11.8\u0026ndash;13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4(12.8\u0026ndash;15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINR\u003c/b\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(1.04\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(1.13\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINR Category\u003c/b\u003e \u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u0026thinsp;\u0026lt;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal protein\u003c/b\u003e\u0026dagger;\u0026nbsp;g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2(6.7, 7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6(6, 7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin Level\u003c/b\u003e \u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.5\u0026nbsp;g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=3.5\u0026nbsp;g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMELD score\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMELD score\u003c/b\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(7\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(8\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALBI grade\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade 2 /3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALBI score\u003c/b\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.37 (-2.6 - -2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.75 (-2.1 - -1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild-Pugh score\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eECOG\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEtiology of liver disease\u003c/b\u003e\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMASLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHepatic Decompensation\u003c/h3\u003e\n\u003cp\u003eHepatic decompensation occurred in 14 of 102 patients (13.7%) within 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1 months following segmental TARE. Clinical manifestations included new or worsening ascites (n\u0026thinsp;=\u0026thinsp;8), hepatic encephalopathy (n\u0026thinsp;=\u0026thinsp;1), and bilirubin elevation greater than three times the upper limit of normal (n\u0026thinsp;=\u0026thinsp;10), with several patients experiencing more than one decompensation event.\u003c/p\u003e \u003cp\u003ePatients who developed hepatic decompensation were older than those without decompensation (median age 68.7 vs. 64.5 years, p\u0026thinsp;=\u0026thinsp;0.03). There were no significant differences between groups with respect to sex, lung shunt fraction, de novo versus previously treated disease, tumor size, BCLC stage, ECOG performance status, or microsphere type.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Laboratory and Liver Function Parameters\u003c/h2\u003e \u003cp\u003ePatients who developed hepatic decompensation demonstrated significantly worse baseline liver function compared with those without decompensation. Median white blood cell count was lower in the decompensation group (4.1 vs. 5.3 \u0026times;10⁹/L, p\u0026thinsp;=\u0026thinsp;0.007), and thrombocytopenia was more pronounced (median platelet count 84.5 vs. 120 \u0026times;10⁹/L, p\u0026thinsp;=\u0026thinsp;0.04). Alkaline phosphatase levels were higher in the decompensation group (137 vs. 106 U/L, p\u0026thinsp;=\u0026thinsp;0.02), as were prothrombin time (13.4 vs. 13.0 seconds, p\u0026thinsp;=\u0026thinsp;0.04) and international normalized ratio (1.2 vs. 1.1, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003eHypoalbuminemia was strongly associated with hepatic decompensation. Albumin levels\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL were present in 85.7% of patients who developed decompensation compared with 36.4% of those who did not (p\u0026thinsp;=\u0026thinsp;0.001). Total bilirubin levels did not differ significantly between groups; however, higher bilirubin grade distribution was observed in the decompensation group (p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eGlobal liver function scores also differed significantly between groups. Patients with hepatic decompensation had higher MELD scores (median 11 vs. 8, p\u0026thinsp;=\u0026thinsp;0.01), higher ALBI scores (\u0026ndash;1.75 vs. \u0026minus;\u0026thinsp;2.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a greater proportion of Child-Pugh class B/C disease (50% vs. 22.7%, p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDosimetric and Volumetric Parameters\u003c/h2\u003e \u003cp\u003ePost-treatment dosimetric analysis demonstrated that patients who developed hepatic decompensation received lower median absorbed radiation doses to the treated segment compared with those without decompensation (133 Gy vs. 196 Gy, p\u0026thinsp;=\u0026thinsp;0.01). A similar trend was observed for mean tumor absorbed dose (394 Gy vs. 500 Gy), although this did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eVolumetric analysis showed that patients with hepatic decompensation had smaller total liver volumes (median 1536 cm\u0026sup3; vs. 1699 cm\u0026sup3;, p\u0026thinsp;=\u0026thinsp;0.02). There were no significant differences between groups in treated segment volume, tumor volume, or percentage of preserved non-irradiated liver. A trend toward a higher segment-to-liver volume ratio was observed in the decompensation group, but this did not reach statistical significance (21.0% vs. 16.1%, p\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDosimetric and volumetric data for predicting hepatic decompression in segmental TARE. \u0026dagger; Continuous variables are reported as median (interquartile range). \u0026Dagger; Categorical variables are reported as number (percentage).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo decompensation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecompensated\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor volume\u0026dagger;\u0026nbsp;ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.8(9.95,35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3(6.17,23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiosome volume\u0026dagger;\u0026nbsp;ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275(195,431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322(290,371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver volume\u0026dagger;\u0026nbsp;ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1699(1411,2146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1536(1346,1664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Tumor Absorbed dose\u0026dagger; Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500(333\u0026ndash;804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394(303\u0026ndash;419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Angiosome absorbed dose\u0026dagger; Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196(137\u0026ndash;284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133(117\u0026ndash;155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiosome volume to liver volume ratio\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16% (10.8\u0026ndash;26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21% (18.3\u0026ndash;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-radiated liver volume to liver volume ratio \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84% (73\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79% (75\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrosphere Type\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(57.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(42.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Analysis\u003c/h2\u003e \u003cp\u003eVariables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 on univariate analysis were included in the multivariate logistic regression model. Baseline hypoalbuminemia (\u0026lt;\u0026thinsp;3.5 g/dL) was the only independent predictor of hepatic decompensation (odds ratio 7.98, 95% confidence interval 1.16\u0026ndash;80.39, p\u0026thinsp;=\u0026thinsp;0.04). Age, MELD score, ALP, bilirubin grade, Child-Pugh class, and post-treatment dosimetric parameters were not independently associated with hepatic decompensation after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Multivariate model for predicting hepatic decompression in segmental TARE. Bilirubin grade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98, 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43, 3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u0026thinsp;\u0026lt;\u0026thinsp;4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71, 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;3.5 g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16, 80.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;147 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u0026thinsp;\u0026gt;\u0026thinsp;147 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3, 7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin grade 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin grade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005, 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08, 16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeld score\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeld score\u0026thinsp;\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2, 14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh score A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh score B/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04, 3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Tumor absorbed dose Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Segment absorbed dose Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver volume ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99, 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eG1: 1.2\u0026ndash;1.8 mg/dL, 3.6\u0026thinsp;\u0026gt;\u0026thinsp;G2\u0026thinsp;\u0026gt;\u0026thinsp;1.8 mg/dL, G3\u0026thinsp;\u0026gt;\u0026thinsp;3.6. Abbreviations: ALP, alkaline phosphatase; Gy, Gray;\u003c/p\u003e \u003cp\u003eMELD, Model for End-Stage Liver Disease; MASLD, metabolic dysfunction-associated steatotic liver disease; WBC, white blood cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort of patients with hepatocellular carcinoma undergoing segmental yttrium-90 (Y-90) transarterial radioembolization (TARE), hepatic decompensation occurred in 14 of 102 patients (13.7%) within 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1 months. This rate is lower than the decompensation rates reported in studies that primarily evaluated lobar treatment. Reincke et al. reported hepatic decompensation in approximately 28% of patients after TARE and identified impaired baseline liver function as a major driver of toxicity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, Wong et al. reported clinically significant hepatic decompensation requiring admission in 10.9% of patients within 60 days after (lobar) Y-90 TARE and emphasized the importance of baseline liver function and Child-Pugh score for patient selection [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Compared with these lobar cohorts [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], our lower event rate is consistent with the expected safety advantage of segmental treatment, which aims to limit radiation exposure to non-tumoral liver parenchyma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn univariate analysis, patients who developed decompensation had worse baseline liver function, including lower albumin, higher MELD and ALBI scores, a higher proportion of Child-Pugh class B/C disease, and higher INR and alkaline phosphatase levels. This pattern is consistent with prior reports showing that reduced hepatic reserve increases the risk of post-TARE hepatic dysfunction [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, after multivariable adjustment, baseline hypoalbuminemia (\u0026lt;\u0026thinsp;3.5 g/dL) remained the only independent predictor of hepatic decompensation in our cohort (OR 7.98). Wong et al. similarly identified preprocedure albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL as a significant predictor of decompensation, along with INR\u0026thinsp;\u0026gt;\u0026thinsp;1.2, ascites, and elevated MELD and Child-Pugh scores [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Reincke et al. reported ALBI score as a key multivariable predictor of decompensation after TARE [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Taken together, these studies support the concept that albumin-based metrics (albumin alone and ALBI) capture the vulnerability of hepatic reserve in patients undergoing radioembolization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Because our study focused on segmental TARE, albumin alone may be a practical and clinically accessible marker to identify patients at higher risk even when a selective approach is used.\u003c/p\u003e \u003cp\u003eWe did not identify a significant association between microsphere type (glass versus resin) and hepatic decompensation. This finding aligns with prior comparative studies suggesting similar safety profiles between glass and resin microspheres when patients are appropriately selected [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While some reports have suggested potential efficacy differences between platforms (e.g., response rates or survival) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our results indicate that platform choice was not a major determinant of early hepatic decompensation in segmental TARE.\u003c/p\u003e \u003cp\u003eWe incorporated voxel-based post-treatment dosimetry to explore whether delivered dose or treated liver volume correlated with hepatic decompensation. Patients who developed decompensation had lower mean segment absorbed dose (133 Gy vs. 196 Gy). This direction of association likely reflects treatment selection and dose de-escalation in patients with poorer baseline liver function rather than a causal relationship between lower segment dose and hepatic toxicity. This interpretation is supported by the lack of an independent dosimetric association in multivariable analysis. Prior work has emphasized that adequate hepatic functional reserve is central to minimizing radioembolization-induced liver injury, even as dosimetry methods become more sophisticated [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, ablative dose concepts are most strongly linked to tumor response and complete pathological necrosis rather than hepatic decompensation, as demonstrated in LEGACY and related dosimetry literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe observed that patients who developed decompensation had smaller total liver volumes. Although the percent of non-irradiated liver and the segment-to-liver volume ratio did not reach statistical significance in our cohort, volume-based hepatic reserve remains biologically plausible as a contributor to post-treatment tolerance. Prior work in ablative radioembolization has also evaluated liver-treated fraction and biochemical toxicity thresholds, supporting the importance of preserved functional parenchyma (especially when pursuing high-dose selective approaches) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, broader hepatology guidance on preventing and managing complications of radioembolization supports careful selection based on baseline liver function and risk factors for radioembolization-induced liver disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Future studies should evaluate volume-based metrics normalized to body size and functional liver volume and should assess whether integrating volumetry with albumin-based measures improve risk stratification for segmental TARE.\u003c/p\u003e \u003cp\u003eThis study has limitations. The retrospective design introduces potential selection bias. The number of decompensation events was modest, which limits statistical power and precision of confidence intervals. The follow-up window of 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1 months may miss later hepatic decompensation; longer-term outcome studies have reported late events after Y-90 TARE [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, prior liver-directed therapies and treatment heterogeneity may affect baseline hepatic reserve, although we did not observe a significant association between de novo versus previously treated disease and decompensation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with hepatocellular carcinoma undergoing segmental yttrium-90 transarterial radioembolization, hepatic decompensation occurred in 13.7% of cases. Baseline hypoalbuminemia (\u0026lt;\u0026thinsp;3.5 g/dL) was the only independent predictor of early hepatic decompensation, underscoring the importance of hepatic functional reserve in patient selection. Post-treatment dosimetric and volumetric parameters, as well as microsphere type, were not independently associated with decompensation. These findings highlight serum albumin as a simple and clinically useful marker for risk stratification before segmental Y-90 radioembolization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding/Support: Dr. Kokabi receives research support from SIRTeX Medical. The remaining authors have no funding/support disclosures.\u003c/p\u003e\n\u003cp\u003eConflict of Interest Disclosures: Dr. Kokabi is a consultant for SIRTeX Medical, Boston Scientific, Okami Medical, Terumo Medical, Trisalus Life Sciences\u003c/p\u003e\n\u003cp\u003eDr. Villalobos is a consultant for SIRTeX Medical. The remaining authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eFor this retrospective study, formal consent was not required.\u003c/p\u003e\n\u003cp\u003eNo additional informed consent was required to participate in this retrospective study.\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: not applicable\u003c/p\u003e\n\u003cp\u003eAcknowledgement: not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfrasiabi: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, visualization, writing \u0026ndash; original draft, and writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eGholami: Conceptualization, data curation, formal analysis, investigation, resources, software, visualization, writing \u0026ndash; original draft, and writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eMoon: Conceptualization, data curation, formal analysis, investigation, resources, software, visualization, writing \u0026ndash; original draft, and writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eVillalobos: Data curation, formal analysis, writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eMoon: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003ePisanie: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eDu Pisanie: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eHarris: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eYu: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eMauro: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eMohnasky: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eGad: Writing \u0026ndash; review/editing.\u003c/p\u003e\n\u003cp\u003eKokabi: Conceptualization, methodology, project administration, resources, supervision, validation, writing \u0026ndash; original draft, and writing \u0026ndash; review/editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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J Gastroenterol Hepatol. 2024;39(8):1648\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReincke M, Schultheiss M, Doppler M, Verloh N, Uller W, Sturm L, et al. Hepatic decompensation after transarterial radioembolization: A retrospective analysis of risk factors and outcome in patients with hepatocellular carcinoma. Hepatol Commun. 2022;6(11):3223\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HM, Alder L, Nguyen M, Dougherty SC, Qu Y, Thacker LR, et al. Long-term outcome analysis of Y90 radioembolization in hepatocellular carcinoma. J Gastrointest Oncol. 2023;14(3):1378\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe la Garza-Ramos C, Gabr A, Riaz A, et al. Biochemical safety of ablative yttrium-90 radioembolization: Percent liver treated thresholds associated with biochemical toxicity. J Hepatocell Carcinoma. 2021;8:249\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/JHC.S294803\u003c/span\u003e\u003cspan address=\"10.2147/JHC.S294803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangro B, Mart\u0026iacute;nez-Urbistondo D, Bester L, et al. Prevention and treatment of complications of selective internal radiation therapy (radioembolization). Hepatology. 2017;66(2):654\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.29107\u003c/span\u003e\u003cspan address=\"10.1002/hep.29107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Liver Cancer, Transarterial Radioembolization, Locoregional Therapy, Hepatic Decompensation","lastPublishedDoi":"10.21203/rs.3.rs-8427893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8427893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eTransarterial radioembolization (TARE) with yttrium-90 (Y-90) microspheres is an established treatment for unresectable hepatocellular carcinoma (HCC). Although segmental TARE offers a favorable safety profile, hepatic decompensation remains a clinically significant complication. This study aimed to identify clinical, laboratory, and post-treatment dosimetric predictors of hepatic decompensation following segmental Y-90 TARE.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eIn this retrospective cohort study, 102 patients with HCC who underwent segmental Y-90 TARE between 2015 and 2025 were analyzed. Baseline demographic, clinical, laboratory, and imaging data were collected. Hepatic decompensation was defined as new or worsening ascites, hepatic encephalopathy, or bilirubin elevation greater than three times the upper limit of normal within 3 ± 1 month after treatment. Post-treatment dosimetry was performed using SPECT/CT with voxel-based analysis. Univariate and multivariate logistic regression models were used to identify predictors of hepatic decompensation.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eHepatic decompensation occurred in 14 patients (13.7%). On univariate analysis, decompensation was associated with baseline hypoalbuminemia (\u0026lt; 3.5 g/dL), higher MELD and ALBI scores, Child-Pugh class B/C, INR ≥ 1.2, lower white blood cell count, and higher alkaline phosphatase levels. Patients who developed decompensation received lower median absorbed radiation doses to the treated segment (133 Gy vs. 196 Gy, p = 0.01) and had smaller total liver volumes (1536 cm³ vs. 1699 cm³, p = 0.02). In multivariate analysis, baseline hypoalbuminemia (\u0026lt; 3.5 g/dL) was the only independent predictor of hepatic decompensation (OR = 7.98, 95% CI 1.16–80.39, p = 0.04).\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eHepatic decompensation after segmental Y-90 TARE is primarily driven by impaired baseline hepatic reserve rather than post-treatment dosimetry. Baseline hypoalbuminemia is a strong independent predictor of early hepatic decompensation and should be carefully considered during patient selection for radioembolization.\u003c/p\u003e","manuscriptTitle":"Predictors of Hepatic Decompensation Post-Y90 Treatment in Hepatocellular Carcinoma: New Insights into Segmental TARE and Post-Treatment Dosimetry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-01 06:20:05","doi":"10.21203/rs.3.rs-8427893/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T01:02:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T14:04:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T22:32:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T21:26:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288764545284831593101515043632316684951","date":"2026-01-21T16:49:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331537086183322597393783126621412876214","date":"2026-01-20T10:52:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228977757988352549741145106025404671594","date":"2026-01-18T10:30:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T10:46:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282309766854734077013394776632837659927","date":"2025-12-29T14:20:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T12:53:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T02:51:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-23T09:23:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Gastrointestinal Cancer","date":"2025-12-22T18:50:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"57a50319-e2f1-4e3a-9be6-9916887d50db","owner":[],"postedDate":"January 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T00:53:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-01 06:20:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8427893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8427893","identity":"rs-8427893","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}