Introduction

Hepatocellular carcinoma (HCC) remains a substantial global health concern because of its increasing incidence and limited therapeutic options, especially in its advanced stages.1 Sorafenib is a potent multikinase inhibitor that was first introduced as a standard first-line treatment in two Phase III clinical trials.2,3 Recently, immune checkpoint inhibitors (ICIs) have revolutionized the treatment methods for HCC. Compared with standard systemic therapy, combination therapy involving ICIs has demonstrated improved survival rates in patients with advanced HCC.4–8 However, not all patients benefit from ICIs.9 Therefore, reliable markers are urgently required to facilitate treatment selection and monitor therapeutic response.10,11

Many biomarkers that are potentially useful in predicting the treatment efficacy of ICIs in other types of cancer do not exhibit a similar efficacy in advanced HCC. For example, in patients with advanced HCC, tumor staining for programmed death ligand 1 (PD-L1) exhibits only a borderline or marginal association with tumor response to nivolumab treatment.12 Tumors with a mismatch repair deficiency respond well to ICIs,13 but this deficiency is rare in HCC.14 Although early α-fetoprotein (AFP) response may assist in predicting treatment response,15 its usefulness is limited to patients with baseline abnormal AFP levels.

The spleen is an organ that is intricately involved in immunomodulation and intriguingly associated with systemic inflammation and cancer progression. In general, immunotherapy exerts a systemic effect on multiple organs, including the spleen, which plays a vital role in hematopoiesis and immune response.16–20 For instance, Susok et al18 reported an increase in SV in patients treated with ICIs for melanoma. By contrast, Castagnoli et al20 negated the predictive value of SV in treatment response among patients who underwent immunotherapy for non–small-cell lung cancer. Muller et al21 noted that an increase in SV before and throughout immunotherapy served as a major predictive factor of poor overall survival (OS) in patients with advanced HCC. However, few studies have compared the role of SV changes in patients undergoing treatment with ICIs or sorafenib for advanced HCC. To understand the effects of systemic therapy on patients with HCC, an in-depth exploration of the effect of systemic therapy on splenic dynamics is required.

SV changes may reflect shifts in splenic immune cells, and these shifts may influence the response of patients undergoing immunotherapy for HCC. The aim of this study is to investigate whether changes in SV during treatment would serve as a valuable prognostic marker. We examined the predictive value of SV changes in patients who underwent immunotherapy for advanced HCC. For comparison purposes, we included a group of patients who received sorafenib as a first-line treatment for advanced HCC.

Material and Methods Study Population: Immunotherapy

This study was approved by the institutional review board. Because of the study’s retrospective design, informed consent was not required.

Patients with advanced HCC who underwent immunotherapy in clinical trials between August 2015 and February 2022 at our institution were retrospectively analyzed (Figure 1). The immunotherapy regimens included anti-programmed cell death protein 1 (anti-PD-1), anti-PD-L1, and anticytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) antibodies. ICIs combined with antiangiogenic targeted therapies and other agents currently under investigation were also included.

Figure 1 Study flowchart.

Comprehensive patient data, namely demographic information (age, sex), hepatitis etiology, liver function reserve (Child–Pugh class and albumin-bilirubin [ALBI] grade), tumor stage, Barcelona Clinic Liver Cancer (BCLC) stage, AFP level, tumor involvement extent, and number of previous systemic therapies, were retrieved from medical records.

The inclusion criteria were histologically confirmed or clinically diagnosed HCC, a baseline computed tomography (CT) scan performed within 4 weeks of treatment initiation, and at least one measurable lesion in accordance with Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Those with previous exposure to immunotherapy and the absence of pretreatment CT scans were excluded.

Follow-up CT scans were conducted every 1 to 3 months after the initiation of treatment. Tumor assessment was conducted in accordance with RECIST version 1.1. The disease control rate (DCR, including complete response, partial response, and stable disease) and objective response rate (ORR, including complete and partial response) were determined on the basis of optimal radiological response observed following immunotherapy. A durable clinical benefit (DCB) was defined as complete response, partial response, or stable disease lasting over 6 months.11,22 Patient follow-up continued until December 31, 2022.

Study Population: Sorafenib

For patients who received sorafenib as a first-line treatment, a prospectively enrolled patient cohort was used (Figure 1).23 This prospective study was approved by the institutional review board. Written informed consent was obtained from each patient before their inclusion in the study.

All relevant clinicopathological variables were prospectively collected from each patient’s medical records.

Image Data Acquisition and Analysis

To evaluate treatment response, four-phase dynamic contrast-enhanced CT scans were conducted using 16- or 64-channel CT scanners. All scans were conducted in an axial plane with a tube voltage ranging from 100 to 130 kV and a slice thickness of 5 mm. SV was manually delineated using 3D Slicer software (version 4.10) on a venous-phase CT scan by an experienced radiologist, who was blinded to the clinical outcomes and had 15 years of abdominal imaging experience. The splenic areas of each axial image were then aggregated to calculate the total SV (Figure 2). Splenomegaly was defined as an SV greater than 314.5 cm3.24,25

Figure 2 CT-based SV measurement. Upper panel displays the CT scans of a 66-year-old man who underwent therapy with atezolizumab, bevacizumab, and tocilizumab. His SV increased by approximately 54.3%, from 85 cm3 at baseline to 132 cm3 at his 41-day follow-up CT scan. His condition remained stable, and he experienced a DCB, with PFS of 13.8 months. Lower panel displays the CT scans of a 39-year-old man with advanced HCC treated with sorafenib. His SV increased by approximately 34.7%, from 656 cm3 at baseline to 884 cm3 at his 42-day follow-up CT scan. Despite treatment, his condition worsened, and he exhibited PFS of 1.4 months.

Statistical Analysis

Data are reported as means ± standard deviations for continuous variables and as absolute numbers and percentages for categorical variables. The distribution of continuous variables was evaluated for normality by using the Shapiro–Wilk test. The characteristics of patients exhibiting increased or decreased SV were compared using Pearson’s chi-square or Fisher’s exact test for categorical data, Student’s t-test for normally distributed data, and the Mann–Whitney U-test for non–normally distributed data.

Progression-free survival (PFS) was calculated as the period from the date of immunotherapy or sorafenib treatment to the date of disease progression, death, or the last follow-up. It was estimated using the Kaplan–Meier method and compared between different groups by using a Log rank test in univariate analysis. Sex, age, hepatitis etiology, tumor involvement extent, AFP level, liver function reserve, and splenomegaly were adjusted in logistic regression and a Cox proportional hazards model to examine the effect of SV changes on DCB and PFS, respectively. Immunotherapy regimens and treatment lines were also adjusted in the immunotherapy group. In addition, to reduce the effect of potential confounding factors, we used weighted logistic and Cox proportional hazards regression models to adjust for differences in baseline characteristics, employing inverse probability of treatment weighting (IPTW). A two-sided p value of <0.05 was considered statistically significant. All statistical analyses were conducted using SAS software version 9.4 (SAS Institute, Cary, NC, USA) and R (version 4.3.3; http://www.R-project.org).

Results Patient Characteristics in the Immunotherapy and Sorafenib Groups

The immunotherapy group comprised 143 patients. Table 1 lists the demographic, clinical, and imaging characteristics of the patients. At the time of immunotherapy initiation, most of the patients had chronic hepatitis B virus infection (75%), a Child–Pugh score of 5 (84%), BCLC stage C disease (83%), and extrahepatic metastasis (77%). Before immunotherapy was initiated, approximately half of the patients (71 out of 143) had undergone other systemic therapies. The median follow-up period was 36.3 months (95% confidence interval [CI], 29.1–43.5 months), and the ORR was 29.4% (42 out of 143 patients), with a DCR of 72% (103 out of 143 patients). A total of 66 patients (46%) exhibited a DCB, with median PFS of 5.4 months (95% CI, 3.6–7.2 months).

Table 1 Patient Characteristics and Treatment Response in the Immunotherapy and Sorafenib Groups

The sorafenib group comprised 57 patients. At the time of treatment initiation, most of the patients had chronic hepatitis B virus infection (63%), a Child–Pugh score of 5 (93%), BCLC stage C disease (91%), and extrahepatic metastasis (63%). The median follow-up period was 24.5 months (95% CI, 20.1–28.9 months), and the ORR was 7% (4 out of 57 patients), with a DCR of 45.6% (26 out of 57 patients). A total of 10 patients (18%) exhibited a DCB, with median PFS of 2.2 months (95% CI, 1.8–2.6 months).

Compared with patients who received sorafenib, a larger proportion of those who underwent immunotherapy exhibited an increase in SV after treatment (75.5% vs 36.8%, p < 0.001). Figure 3 depicts waterfall plots illustrating the relationship between therapeutic response and SV change in the immunotherapy (Figure 3A) and sorafenib (Figure 3B) groups.

Figure 3 Waterfall plots of optimal tumor response versus SV changes. (A) immunotherapy group. (B) sorafenib group.

Changes in SV and Treatment Outcomes in the Immunotherapy Group

In the immunotherapy group, patients who exhibited an increase in SV after treatment (n = 108) were significantly younger, more likely to undergo immunotherapy as a first-line treatment, and more likely to have ALBI grade 1 liver function reserve compared with those who exhibited a decrease in SV after treatment (n = 35, Table 2). Neither baseline SV nor splenomegaly was associated with an increase or decrease in SV. Posttreatment changes in ALBI scores were not significantly different between patients with increased and decreased SV (p = 0.871).

Table 2 Comparison of SV Changes in the Immunotherapy Group

Compared with patients who exhibited a decrease in SV after treatment, those who exhibited an increase in SV after treatment were more likely to have a higher DCR (76.9% vs 57.1%, p = 0.024) and greater DCB (52.8% vs 25.7%, p = 0.005; Table 2). After patient demographics, hepatitis etiology, tumor involvement extent, liver function reserve, AFP level, and immunotherapy regimens or lines were adjusted for, increased SV after treatment remained a significant predictor of DCB in multivariate analysis (odds ratio, 6.94; 95% CI, 2.07–23.27, p < 0.001; Table 3).

Table 3 Multivariate Analysis of DCB by Logistic Regression in the Immunotherapy and Sorafenib Groups

Patients who exhibited an increase in SV after treatment had significantly longer PFS than did those who exhibited a decrease in SV after treatment (median: 6.9 vs 3.6 months, p = 0.028; Figure 4A). After other potential predictors were adjusted for, increased SV after treatment remained an independent predictor of improved PFS (hazard ratio [HR], 0.51; 95% CI, 0.3–0.87, p = 0.014; Table 4).

Table 4 Multivariate Analysis of PFS by Cox Regression in the Immunotherapy and Sorafenib Groups

Figure 4 Kaplan–Meier curves of PFS versus SV changes. (A) immunotherapy group. (B) sorafenib group. All p values were determined using a Log rank test.

Changes in SV and Treatment Outcomes in the Sorafenib Group

In the sorafenib group, an increase in SV was not associated with any clinical variables, including age and liver function reserve (Table 5). In patients who received sorafenib, an increase in SV was not associated with a DCB in univariate analysis and multivariate analysis (p = 0.232, Table 3). Compared with patients who exhibited a decrease in SV after treatment, those who exhibited an increase in SV after treatment tended to have shorter PFS (median: 2.1 vs 2.5 months, p = 0.094; Figure 4B). In patients who received sorafenib, an increase in SV after treatment exhibited borderline significance in predicting poor PFS in multivariate analysis (HR, 1.87; 95% CI, 0.95–3.71, p = 0.072; Table 4).

Table 5 Comparison of SV Changes in the Sorafenib Group

Comparison of SV Change in Immunotherapy and Sorafenib Groups Using IPTW for DCB and PFS

After IPTW adjustment, increased SV remained a significant positive predictor for DCB (odds ratio, 3.42; 95% CI, 1.41–8.25, p = 0.006; Table 6) in the immunotherapy group and a significant negative predictor for DCB (odds ratio, 0.11; 95% CI, 0.01–0.8, p = 0.03; Table 6) in the sorafenib group. Besides, increased SV remained a significant predictor for longer PFS (HR, 0.52; 95% CI, 0.31–0.89, p = 0.018; Table 7) in the immunotherapy group, but it was not significant in the sorafenib group.

Table 6 Multivariate Analysis of DCB by Logistic Regression in the Immunotherapy and Sorafenib Groups After Inverse Probability of Treatment Weighting Adjustment

Table 7 Multivariate Analysis of PFS by Cox Regression in the Immunotherapy and Sorafenib Groups After Inverse Probability of Treatment Weighting Adjustment

Discussion

In this study, we examined changes in SV as a potential indicator of treatment response in patients undergoing immunotherapy or receiving sorafenib for advanced HCC. During the initial follow-up, we observed a significant increase in SV (75.5%) in the immunotherapy group compared with the sorafenib group (36.8%). The increase in SV was not associated with the changes of liver function reserve. In the immunotherapy group, compared with patients who exhibited a decrease in SV, those who exhibited an increase in SV showed a higher DCR and greater DCB. In addition, an increase in SV predicted a greater DCB and extended PFS. By contrast, in the sorafenib group, an increase in SV was not associated with treatment response but was presumably associated with reduced PFS. After IPTW adjustment, the increase in SV remained a significant predictor for DCB and PFS in the immunotherapy group.

Systemic chemotherapy has profound implications for hematopoiesis and immunocompetence.16,26 Emerging evidence suggests that the spleen can be used as an indicator of systemic immune response during immunotherapy.16 Animal studies have indicated that anti-PD-L1 and anti-PD-1 treatments may affect the exhaustion status and clonality of T lymphocytes in the spleen and increase the number of splenic CD4+ and CD8+ cells, monocytes, macrophages, and natural killer cells after treatment.27–30 Interestingly, this response is associated with an increase in spleen size.29 Therefore, determining the effects of immunotherapy and sorafenib on the spleen may provide an in-depth understanding of their underlying mechanisms and efficacy in patients with HCC.

Studies have reported contradictory findings regarding SV changes during chemotherapy and immunotherapy for different types of cancer. For example, Susok et al18 investigated SV changes during treatment initiation in 49 patients with stage III and IV melanoma. After 3 months, they observed an increase in SV in 31 out of 44 patients (70.5%). However, they reported no significant relationship between this increase in SV and treatment response. Seith et al31 used 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging to examine 17 patients receiving ICIs for melanoma. They discovered that, compared with the nonresponder group, the responder group exhibited a marked increase in SV. Castagnoli et al20 evaluated SV changes in 70 patients receiving pembrolizumab for locally advanced or metastatic non–small-cell lung cancer. Their results revealed no significant changes in SV with pembrolizumab treatment, thus indicating no correlation between SV changes and treatment outcomes. Furthermore, a recent study involving 45 patients with metastatic renal cell carcinoma receiving nivolumab as second-line or subsequent therapy reported that an increase in SV was associated with shorter PFS and OS.32 These studies highlight the complex and potentially variable role of SV changes in predicting the efficacy of cancer treatments.

In this study, we observed a 75.5% increase in SV after the initiation of immunotherapy. This finding is consistent with that of Muller et al,21 who reported a similar increase of 76% in 50 patients undergoing immunotherapy for HCC. In their study, they did not consider an increase in SV as a prognostic factor of OS. However, they did not examine the relationship between SV and DCB or PFS. In the present study, we focused solely on these two outcomes because of the complexity of OS, which is influenced by numerous variables, such as tumor stage at diagnosis, liver function, patient performance status, treatment history, additional treatments,33 the presence of other medical conditions, the type of immunotherapy administered, sarcopenia, myosteatosis,34 and nutritional status.35 Further research is required to elucidate the effect of SV changes on OS while controlling for these confounding factors.

SV is a prognostic predictor in patients with HCC who undergo both curative and palliative treatment.36–39 In this study, we discovered that splenomegaly also served as a negative predictor of PFS in patients undergoing immunotherapy. As an indirect measure of drug-induced hepatotoxicity, CT-quantified SV expansion is used to capture the increase in portal pressure associated with liver injury.17,19,40–42 In our immunotherapy group, changes in SV during the initial follow-up served as a favorable predictor of DCB and PFS. However, in the sorafenib group, SV increases were likely associated with a decrease in PFS, which may be indicative of liver decompensation and portal hypertension secondary to tumor enlargement. Recently, multiple deep learning techniques have been developed to achieve a fully automated evaluation of SV by relying on CT data,39,43 thus indicating the potential of SV as a promising imaging biomarker for seamless integration into standard radiological workflows.

This study has some limitations. First, this study was retrospectively conducted at a single institution on a relatively small number of patients who received various immunotherapeutic agents. Because of the limited sample size, no subgroup analysis per agent was conducted. Subsequent studies should validate the role of SV as a prognostic factor in patients’ response to different immunotherapeutic agents and treatment regimens. Second, this study included only patients who underwent follow-up abdominal CT scans, which may have introduced a degree of selection bias. Third, because CT examinations were performed with clinical discretion, the frequency and interval of these scans were not uniform throughout the study population.

Conclusion

In conclusion, this study highlights the potential of SV changes as a prognostic marker for patients who undergo immunotherapy for advanced HCC. In patients who undergo immunotherapy, a substantial increase in SV correlates with improved treatment response. However, in patients who receive sorafenib, an increase in SV may predict poor PFS. Despite these correlations, the clinical importance of SV changes in terms of OS requires further investigation.

Abbreviations

HCC, Hepatocellular carcinoma; ICI, Immune checkpoint inhibitor; PD-L1, Programmed death ligand 1; AFP, α-Fetoprotein; SV, Spleen volume; PD-1, Programmed cell death protein 1; CTLA-4, Cytotoxic T-lymphocyte-associated protein 4; ALBI, Albumin-bilirubin; BCLC, Barcelona Clinic Liver Cancer; CT, Computed tomography; RECIST, Response Evaluation Criteria in Solid Tumors; DCR, Disease control rate; ORR, Objective response rate; DCB, Durable clinical benefit; PFS, Progression-free survival; CI, Confidence interval; HR, Hazard ratio.

Data Sharing Statement

For ethical reasons, the data are not publicly available. The data sets generated and analyzed in this study are available from the corresponding author upon reasonable request.

Ethics Approval and Informed Consent

Institutional Review Board approval of National Taiwan University Hospital was obtained.

This study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki.

For patients receiving immunotherapy, written informed consent was waived by the Institutional Review Board because of retrospective analysis. Besides, stringent measures have been implemented to ensure the confidentiality and privacy of patient data. All medical records were anonymized and securely stored, accessible only to authorized research personnel. The data was handled in accordance with applicable privacy laws and guidelines to protect patient identities and maintain the integrity of the research.

For patients receiving sorafenib, written informed consent was obtained from all patients receiving sorafenib.

Acknowledgments

We would like to acknowledge the service provided by the RCF5 Lab. of Department of Medical Research at National Taiwan University Hospital.

Author Contributions

Study design and concept: Bang-Bin Chen, Yu-Yun Shao.

Data collection: All authors.

Study analysis: Bang-Bin Chen, Yu-Yun Shao.

Data interpretation and review and approval the manuscript submission: All authors.

Manuscript writing: Bang-Bin Chen, Yu-Yun Shao.

Funding

This study has received funding by This study was funded by the Ministry of Science and Technology, Taiwan (MOST-103-2314-B-002-181-MY2, MOST-105-2314-B-002-194, MOST-106-2314-B-002-213, MOST-108-2314-B-002-072-MY3, MOST-110-2314-B-002-144, MOST-111-2314-B-002-120, and MOST-111-2314-B-002-130-MY2), National Science and Technology Council, Taiwan (NSC 112-2314-B-002-267), Ministry of Health and Welfare, Taiwan (MOHW109-TDU-B-211-114002 and MOHW112-TDU-B-211-144002), National Taiwan University Hospital (NTUH 105S2954 and NTUH 108-S4150), and Good Liver Foundation, Taiwan.

Disclosure

Dr Chih-Hung Hsu reports grants from Roche, grants from AstraZeneca, grants from Eli Lilly, grants from Surface Oncology, personal fees from MSD, personal fees from Eisai, outside the submitted work. The authors report no other competing interests in this work.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. doi:10.3322/caac.21660

2. Llovet JM, Ricci S, Mazzaferro V, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008;359(4):378–390. doi:10.1056/NEJMoa0708857

3. Cheng AL, Kang YK, Chen Z, et al. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet Oncol. 2009;10(1):25–34. doi:10.1016/S1470-2045(08)70285-7

4. Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. J Hepatol. 2022;76(3):681–693. doi:10.1016/j.jhep.2021.11.018

5. Shao YY, Wang SY, Lin SM, Diagnosis G, Systemic Therapy G. Management consensus guideline for hepatocellular carcinoma: 2020 update on surveillance, diagnosis, and systemic treatment by the Taiwan Liver Cancer Association and the Gastroenterological Society of Taiwan. J Formos Med Assoc. 2021;120(4):1051–1060. doi:10.1016/j.jfma.2020.10.031

6. Finn RS, Qin S, Ikeda M, et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N Engl J Med. 2020;382(20):1894–1905. doi:10.1056/NEJMoa1915745

7. Abou-Alfa GK, Lau G, Kudo M, et al. Tremelimumab plus durvalumab in unresectable hepatocellular carcinoma. NEJM Evid. 2022;1(8):EVIDoa2100070. doi:10.1056/EVIDoa2100070

8. Rugo HS, Bardia A, Marmé F, et al. LBA76 Overall survival (OS) results from the phase III TROPiCS-02 study of sacituzumab govitecan (SG) vs treatment of physician’s choice (TPC) in patients (pts) with HR+/HER2-metastatic breast cancer (mBC). Ann Oncol. 2022;33:S808–S869.

9. Llovet JM, Castet F, Heikenwalder M, et al. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol. 2022;19(3):151–172. doi:10.1038/s41571-021-00573-2

10. Scheiner B, Pomej K, Kirstein MM, et al. Prognosis of patients with hepatocellular carcinoma treated with immunotherapy - development and validation of the CRAFITY score. J Hepatol. 2022;76(2):353–363. doi:10.1016/j.jhep.2021.09.035

11. Song R, Liu F, Ping Y, Zhang Y, Wang L. Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction. Biomark Res. 2023;11(1):57. doi:10.1186/s40364-023-00498-1

12. El-Khoueiry AB, Sangro B, Yau T, et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, Phase 1/2 dose escalation and expansion trial. Lancet. 2017;389(10088):2492–2502. doi:10.1016/S0140-6736(17)31046-2

13. Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–2520. doi:10.1056/NEJMoa1500596

14. Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017. doi:10.1200/PO.17.00073

15. Shao YY, Liu TH, Hsu C, et al. Early alpha-foetoprotein response associated with treatment efficacy of immune checkpoint inhibitors for advanced hepatocellular carcinoma. Liver Int. 2019;39(11):2184–2189. doi:10.1111/liv.14210

16. Cataldi M, Vigliotti C, Mosca T, Cammarota M, Capone D. Emerging role of the spleen in the pharmacokinetics of monoclonal antibodies, nanoparticles and exosomes. Int J Mol Sci. 2017;18(6):1249. doi:10.3390/ijms18061249

17. Choi SJ, Lee SS, Jung KH, et al. Noncirrhotic portal hypertension after trastuzumab emtansine in HER2-positive breast cancer as determined by deep learning-measured spleen volume at CT. Radiology. 2022;305(3):606–613. doi:10.1148/radiol.220536

18. Susok L, Reinert D, Lukas C, Stockfleth E, Gambichler T. Volume increase of spleen in melanoma patients undergoing immune checkpoint blockade. Immunotherapy. 2021;13(11):885–891. doi:10.2217/imt-2021-0022

19. Overman MJ, Maru DM, Charnsangavej C, et al. Oxaliplatin-mediated increase in spleen size as a biomarker for the development of hepatic sinusoidal injury. J Clin Oncol. 2010;28(15):2549–2555. doi:10.1200/JCO.2009.27.5701

20. Castagnoli F, Doran S, Lunn J, et al. Splenic volume as a predictor of treatment response in patients with non-small cell lung cancer receiving immunotherapy. PLoS One. 2022;17(7):e0270950. doi:10.1371/journal.pone.0270950

21. Muller L, Gairing SJ, Kloeckner R, et al. Baseline splenic volume outweighs immuno-modulated size changes with regard to survival outcome in patients with hepatocellular carcinoma under immunotherapy. Cancers. 2022;14(15):3574. doi:10.3390/cancers14153574

22. Ren Z, Ducreux M, Abou-Alfa GK, et al. Tislelizumab in patients with previously treated advanced hepatocellular carcinoma (RATIONALE-208): a multicenter, non-randomized, open-label, Phase 2 trial. Liver Cancer. 2023;12(1):72–84. doi:10.1159/000527175

23. Shao YY, Cheng AL, Hsu CH. An underdiagnosed hypothyroidism and its clinical significance in patients with advanced hepatocellular carcinoma. Oncologist. 2021;26(5):422–426. doi:10.1002/onco.13755

24. Bezerra AS, D’Ippolito G, Faintuch S, Szejnfeld J, Ahmed M. Determination of splenomegaly by CT: is there a place for a single measurement? AJR Am J Roentgenol. 2005;184(5):1510–1513. doi:10.2214/ajr.184.5.01841510

25. Prassopoulos P, Daskalogiannaki M, Raissaki M, Hatjidakis A, Gourtsoyiannis N. Determination of normal splenic volume on computed tomography in relation to age, gender and body habitus. Eur Radiol. 1997;7(2):246–248. doi:10.1007/s003300050145

26. Ugel S, Peranzoni E, Desantis G, et al. Immune tolerance to tumor antigens occurs in a specialized environment of the spleen. Cell Rep. 2012;2(3):628–639. doi:10.1016/j.celrep.2012.08.006

27. Markel JE, Noore J, Emery EJ, Bobnar HJ, Kleinerman ES, Lindsey BA. Using the spleen as an in vivo systemic immune barometer alongside osteosarcoma disease progression and immunotherapy with α-PD-L1. Sarcoma. 2018;2018:8694397. doi:10.1155/2018/8694397

28. Sato-Kaneko F, Yao S, Ahmadi A, et al. Combination immunotherapy with TLR agonists and checkpoint inhibitors suppresses head and neck cancer. JCI Insight. 2017;2(18):e93397. doi:10.1172/jci.insight.93397

29. Ji C, Roy MD, Golas J, et al. Myocarditis in cynomolgus monkeys following treatment with immune checkpoint inhibitors. Clin Cancer Res. 2019;25(15):4735–4748. doi:10.1158/1078-0432.CCR-18-4083

30. Knudson KM, Hicks KC, Alter S, Schlom J, Gameiro SR. Mechanisms involved in IL-15 superagonist enhancement of anti-PD-L1 therapy. J Immunother Cancer. 2019;7(1):82. doi:10.1186/s40425-019-0551-y

31. Seith F, Forschner A, Weide B, et al. Is there a link between very early changes of primary and secondary lymphoid organs in (18)F-FDG-PET/MRI and treatment response to checkpoint inhibitor therapy? J Immunother Cancer. 2020;8(2):e000656. doi:10.1136/jitc-2020-000656

32. Aslan V, Karabörk Kılıç AC, Özet A, et al. The role of spleen volume change in predicting immunotherapy response in metastatic renal cell carcinoma. BMC Cancer. 2023;23(1):1045. doi:10.1186/s12885-023-11558-y

33. Armstrong S, Roy T, Singh B, et al. TKIs beyond immunotherapy predict improved survival in advanced HCC. J Cancer Res Clin Oncol. 2023;149(6):2559–2574. doi:10.1007/s00432-022-04115-w

34. Chen BB, Liang PC, Shih TT, et al. Sarcopenia and myosteatosis are associated with survival in patients receiving immunotherapy for advanced hepatocellular carcinoma. Eur Radiol. 2023;33(1):512–522. doi:10.1007/s00330-022-08980-4

35. Jiang Y, Tu X, Zhang X, et al. Nutrition and metabolism status alteration in advanced hepatocellular carcinoma patients treated with anti-PD-1 immunotherapy. Support Care Cancer. 2020;28(11):5569–5579. doi:10.1007/s00520-020-05478-x

36. Takeishi K, Kawanaka H, Itoh S, et al. Impact of splenic volume and splenectomy on prognosis of hepatocellular carcinoma within Milan criteria after curative hepatectomy. World J Surg. 2018;42(4):1120–1128. doi:10.1007/s00268-017-4232-z

37. Bae JS, Lee DH, Yoo J, et al. Association between spleen volume and the post-hepatectomy liver failure and overall survival of patients with hepatocellular carcinoma after resection. Eur Radiol. 2021;31(4):2461–2471. doi:10.1007/s00330-020-07313-7

38. Wu WC, Chiou YY, Hung HH, et al. Prognostic significance of computed tomography scan-derived splenic volume in hepatocellular carcinoma treated with radiofrequency ablation. J Clin Gastroenterol. 2012;46(9):789–795. doi:10.1097/MCG.0b013e31825ceeb5

39. Müller L, Kloeckner R, Mähringer-Kunz A, et al. Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC. Eur Radiol. 2022;32(9):6302–6313. doi:10.1007/s00330-022-08737-z

40. Jung EJ, Ryu CG, Kim G, et al. Splenomegaly during oxaliplatin-based chemotherapy for colorectal carcinoma. Anticancer Res. 2012;32(8):3357–3362.

41. Kosmin M, Makris A, Jawad N, Woolf D, Miles D, Padhani AR. Splenic enlargement and bone marrow hyperplasia in patients receiving trastuzumab-emtansine for metastatic breast cancer. Target Oncol. 2017;12(2):229–234. doi:10.1007/s11523-017-0477-6

42. Iranmanesh P, Vazquez O, Terraz S, et al. Accurate computed tomography-based portal pressure assessment in patients with hepatocellular carcinoma. J Hepatol. 2014;60(5):969–974. doi:10.1016/j.jhep.2013.12.015

43. Lee CM, Lee SS, Choi WM, et al. An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis. Eur Radiol. 2021;31(5):3355–3365. doi:10.1007/s00330-020-07430-3