Abstract
Classical Hodgkin lymphoma (cHL) has excellent survival outcomes, but real-world data from large-scale cohorts in China remain limited. This multicenter study characterized the clinical profile of a large Chinese cHL cohort to evaluate the efficacy of BV-containing regimens and investigate the prognostic value of clinical, nutritional, and biological markers, including peripheral monocyte percentage (M%). We retrospectively analyzed 547 patients with pathologically confirmed cHL from 10 centers between 2010 and 2023. Clinical characteristics and treatment outcomes were assessed. Propensity score matching (PSM) was employed to minimize selection bias when comparing progression-free survival (PFS) between treatment groups. Multivariate Cox regression models identified independent prognostic factors. The cohort featured a high prevalence of advanced-stage disease (55.0%) and the mixed cellularity subtype (34.4%), distinct from Western populations. While ABVD remained the dominant first-line regimen, the adoption of BV + AVD increased over time. In the PSM analysis, first-line BV + AVD demonstrated a numerically higher 3-year PFS compared with ABVD (95.2% vs. 88.9%), although this difference did not reach statistical significance (P = 0.359). Multivariate analysis identified decreased albumin and elevated M% as independent predictors of inferior PFS, while a focused biological model confirmed elevated M% and bone involvement as independent predictors of inferior overall survival. This study provides comprehensive real-world evidence delineating the clinical landscape of cHL in Chinese patients. While BV + AVD showed a favorable trend toward improved PFS, statistical superiority was not reached, warranting further investigation in larger cohorts. Furthermore, baseline nutritional status (albumin), tumor microenvironment surrogates (M%), and distinct extranodal infiltration (bone involvement) emerged as key prognostic markers, suggesting a need for risk-adapted strategies and potentially intensified therapies in high-risk subgroups.
Keywords: Classical Hodgkin lymphoma, Brentuximab vedotin, Real-world study, Peripheral monocyte , Prognosis
Introduction
Hodgkin lymphoma (HL) is a unique B-cell lymphoproliferative disorder. Its pathogenesis remains incompletely understood; however, established risk factors include familial predisposition, viral exposures, and immune suppression [1, 2]. HL is histopathologically classified into two main entities: classical Hodgkin lymphoma (cHL) and nodular lymphocyte-predominant HL (NLPHL). The cHL category is further subdivided into four histological subtypes: nodular sclerosis, mixed cellularity, lymphocyte-rich, and lymphocyte-depleted. Notably, cHL accounts for over 95% of all HL cases [3].
HL constitutes one of the most prevalent lymphoid malignancies in the Western world, with an annual incidence rate of approximately 3 per 100,000 persons [4].The treatment landscape for HL has evolved significantly. Beyond conventional chemotherapy regimens such as ABVD(doxorubicin, bleomycin, vinblastine, dacarbazine) and BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisone), novel agents including the anti-CD30 antibody-drug conjugate brentuximab vedotin(BV) and immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have been successfully integrated into clinical practice. This expansion of the therapeutic arsenal aims to develop optimized strategies that enhance efficacy while minimizing the long-term toxicities associated with conventional chemo- and radiotherapy [5].Contemporary regimens can cure over 80% of newly diagnosed patients under the age of 60 [1].
Current evidence for the management of Hodgkin lymphoma is primarily derived from randomized controlled trials (RCTs), such as the RAPID, ECHELON-1, and RATHL studies [6–8]. Real-world studies of Hodgkin lymphoma (HL) that include patients across diverse ages, comorbidities, and complications are essential for understanding the disease’s profile in the general population. Currently, there is a lack of large-scale real-world data on HL in China. This study aims to address this gap by describing real-world treatment patterns and survival outcomes, identifying independent prognostic factors for progression-free survival (PFS) and overall survival (OS), and exploring outcome disparities in specific subgroups. These findings are critical for comprehensively understanding the management and prognosis of HL in China.
Method
Patients and data selection
This retrospective, multicenter study was conducted under a waiver of informed consent approved by the Ethics Committee of Peking University Third Hospital (No. M2024282). A waiver of informed consent was granted due to the use of retrospective, anonymized data.
We enrolled 547 patients with pathologically confirmed cHL from 10 Chinese hospitals between January 1, 2010, and December 31, 2023. The inclusion criteria were as follows: (1) age 18 years or older; (2) a pathological diagnosis of cHL with a available medical history; and (3) completion of at least two treatment cycles with at least one post-treatment response evaluation.
Demographic and clinical variables were assessed at diagnosis and included country of age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, B symptoms, Ann Arbor lymphoma stage, extranodal involvement, and serum lactate dehydrogenase (LDH) values. Chemotherapy regimens were recorded, and treatment responses were assessed using the Lugano classification and classified as complete response, partial response, stable disease and progressive disease.
Outcome
The main outcomes were overall survival, prevalence of cHL subtypes and objective response rate. Overall survival (OS) was defined as the time elapsed from diagnosis date to death from any cause, loss to follow-up, or date of last visit, whichever came first. Objective response rate (ORR) was defined as the percentage of patients who achieved a complete response (CR) or partial response (PR).
Statistical analysis
We summarized patient characteristics with descriptive statistics, using the Chi-square test to compare categorical variables and the Mann-Whitney U test for continuous variables. Survival analysis was conducted with the Kaplan-Meier method. The Cox proportional hazard regression model was used for multivariate survival analysis. The log-rank test was used to determine the hazard ratios (HR), 95% confidence intervals (CI), and the corresponding P-value. P < 0.05 was considered to indicate a statistically significant difference. All analyses were done in SPSS version 23 and Originpro 2024. To minimize confounding and balance baseline characteristics between treatment groups, we performed propensity score matching(PSM). We included several clinically relevant covariates in the propensity score model. We used 1:1 nearest neighbor matching without replacement with a caliper width of 0.2 standard deviations of the logit of the propensity score. Balance between groups before and after matching was assessed using standardized mean differences (SMDs), with an SMD < 0.1 indicating adequate balance. After matching, between-group differences in survival outcomes were analyzed using Kaplan-Meier curves and log-rank tests. The PSM was performed using R version 4.1.0 with the MatchIt, survival, and survminer packages.
Results
Study population and propensity score matching
The cohort comprised 547 patients with cHL recruited from ten Chinese hospitals from 2010 to 2023. The patient selection process was as follows (Fig. 1). A total of 547 patients with cHL were initially screened for eligibility. Of these, 94 patients were excluded from the best-response assessment for the following reasons: 64 had an unknown best response (due to partial response after the first assessment or loss to follow-up), 19 lacked standardized assessment data, and 11 did not receive standard first-line treatment. Consequently, 450 patients were evaluable for best response to first-line treatment and were included in the subsequent analysis. To minimize selection bias and balance baseline characteristics, two separate PSM plans were implemented: PSM Plan 1 (Auto-HSCT Comparison): Patients were matched 1:1 based on age, sex, treatment regimen, M%, albumin (ALB), and Ann Arbor stage. After excluding 105 patients with missing PSM factor data, 18 patients in the first-line auto-HSCT group (Group A) were successfully matched with 18 patients in the non-first-line auto-HSCT group (Group B).PSM Plan 2 (First-line Regimen Comparison): Patients receiving BV + AVD or ABVD were matched 1:1 based on age, sex, M%, ALB, extranodal involvement, and Ann Arbor stage. Following the exclusion of 98 patients due to missing data, 25 pairs of patients (N = 50 total) were successfully matched between the BV + AVD group (Group A) and the ABVD group (Group B).
Fig. 1.
The patient selection process
Epidemiological findings
As illustrated in Fig. 2, the incidence of diagnosis demonstrated a steady rise from 2016 onward, with 151 cases identified in the 2020–2023 period compared to 161 in 2016–2019, 79 in 2013–2015, and 77 in 2010–2012.
Fig. 2.
Number of classical Hodgkin Lymphoma Patients Diagnosed by Year
Histopathological subclassification (Fig. 3) identified nodular sclerosis as the most common subtype (271/547, 49.5%), followed by mixed cellularity (188/547, 34.4%), lymphocyte-rich (43/547, 7.9%), lymphocyte-depleted (14/547, 2.6%), and cases of uncertain type (33/547, 6.0%).
Fig. 3.
Percentage distribution of classical Hodgkin Lymphoma subtypes
The demographic and clinical characteristics of the enrolled patients with cHL
The baseline characteristics of the 547 cHL patients were presented in Table 1. The study population was predominantly male (58.1%) and under 60 years of age (87.6%), with a median age at diagnosis of 33 years (range, 18–90). According to the Ann Arbor staging system, more than half of the patients (55.0%) presented with advanced-stage (III–IV) disease, while 30.3% cHL patients had B symptoms. The majority of patients (76.1%) exhibited a favorable performance status (ECOG score 0–1). Extranodal involvement was frequently observed, most commonly in the spleen (22.3%), bone (17.7%), and lung (17.2%). Elevated lactate dehydrogenase and β2-microglobulin were presented in 24.7% and 17.6% of patients, respectively. Risk stratification by the International Prognostic Index (IPI) classified 51.2% of the cHL patients as low-risk. Baseline laboratory parameters, including hematologic and metabolic profiles, were generally within normal limits.
Table 1.
Patients baseline characteristics
| Variable | cHL patients |
|---|---|
| Age at diagnosis, median (range) | 33(18–90) |
| ≥ 65 years, n (%) | 68(12.4) |
| <60 years, n (%) | 477(87.6) |
| Sex | |
| Female, n (%) | 228(41.7) |
| Male, n (%) | 319(58.2) |
| B symptoms | |
| Yes, n (%) | 166(30.3) |
| Data missing, n (%) | 31(5.7) |
| Ann Arbor | |
| Early stage(I-II), n (%) | 227(41.5) |
| Advanced stage(III-IV), n (%) | 301(55.0) |
| Data missing, n (%) | 19(3.5) |
| ECOG score | |
| 0–1, n (%) | 416(76.1) |
| ≥ 2, n (%) | 47(8.6) |
| Data missing, n (%) | 84(15.3) |
| Extranodal involvement | |
| Spleen, n (%) | 122(22.3) |
| Bone marrow, n (%) | 29(5.3) |
| Bone, n (%) | 97(17.7) |
| Gastrointestinal tract, n (%) | 8(1.5) |
| Lung, n (%) | 94(17.2) |
| High lactate dehydrogenase | |
| Yes, n (%) | 135(24.7) |
| Data missing, n (%) | 56(10.2) |
| High β2-microglobulin | |
| Yes, n (%) | 96(17.6) |
| Data missing, n (%) | 88(16.1) |
| IPI score | |
| Low risk, n (%) | 280(51.2) |
| Low-intermediate risk, n (%) | 92(16.8) |
| High-intermediate risk, n (%) | 46(8.4) |
| High risk, n (%) | 11(2.0) |
| Data missing, n (%) | 118(21.5) |
| WBC(*10^9/L), median (IQR) | 8.04(5.75–11.93) |
| HGB(g/L), median (IQR) | 126(112–141) |
| PLT(*10^9/L), median (IQR) | 281(212–365) |
| ALT (U/L), median (IQR) | 19.0(13.0–31.0) |
| AST (U/L), median (IQR) | 18.5(14.0-24.1) |
| Cr(umol/L), median (IQR) | 65.6(51.0-80.1) |
| ALB(g/L), median (IQR) | 41.3(37.1–46.1) |
Treatment patterns and clinical outcomes
Analysis of first-line therapeutic regimens in the enrolled cHL cohort (n = 547) confirmed ABVD as the most prevalent, administered to 443 patients (80.7%), followed by BEACOPP in 38 (7.0%). Other primary strategies included BV + AVD (brentuximab vedotin with doxorubicin, vinblastine, and dacarbazine;8.0%) and PD-1 inhibitor-based chemotherapy (2.0%) (Fig. 4). Among 450 patients evaluable for efficacy, 292 (64.9%) achieved a CR and 114 (25.3%) a PR, yielding an ORR of 90.2% (Table 3). In terms of transplant consolidation, 22 patients underwent autologous stem cell transplantation (ASCT) following first-line therapy, while 47 with relapsed/refractory (R/R) disease received salvage ASCT, and 14 underwent allogeneic hematopoietic stem cell transplantation (Table 2).
Fig. 4.
First-line Treatment Regimens for classical Hodgkin Lymphoma
Table 3.
Transplant timing of classical Hodgkin Lymphoma patient
| Transplant timing | cHL patients, n |
|---|---|
| Frontline consolidation with auto-HSCT | 22 |
| auto-HSCT for relapsed or refractory disease | 47 |
| allo-HSCT | 14 |
Abbreviations: auto-HSCT, autologous hematopoietic stem cell transplantation, allo-HSCT allogeneic hematopoietic stem cell transplantation
Table 2.
Response Assessment of first-line therapeutic regimen(N = 450)
| Response assessment | cHL patients, n(%) |
|---|---|
| PR | 114(25.3) |
| CR | 292(64.9) |
| SD | 21(4.7) |
| PD | 23(5.1) |
| Total | 450 |
Progression-Free and Overall Survival with Univariate and Multivariate Analyses
The estimated 3-year and 5-year progression-free survival (PFS) rates for the entire cohort were 77.1% and 66.2%, respectively, while the corresponding overall survival (OS) rates were 97.0% and 95.5% (Figures 5A and 5B). Survival analysis based on pathological subtypes revealed no statistically significant difference in PFS (P = 0.325; Figure 5C). However, a significant difference was observed in OS (P =0.038; Figure 5D), with the nodular sclerosis subtype demonstrating the most favorable prognosis and the lymphocyte-depleted subtype the least favorable. Furthermore, the presence of extranodal involvement was associated with a significantly inferior PFS (3-year/5-year PFS: 66.4%/61.2% with involvement vs. 76.3%/70.6% without; P = 0.008; Figure 5E). In contrast, extranodal involvement did not confer a statistically significant detriment to OS (3year/5-year OS: 96.4%/94.9% vs. 97.6%/96.1%; P = 0.458; Figure 5F). Univariate Cox analyses identified several factors significantly associated with survival outcomes. For PFS, significant risk factors included extranodal involvement in the lung (HR=1.604, 95% CI: 1.015-2.536, P = 0.043), bone (HR=1.69, 95% CI: 1.081-2.655, P =0.022), and spleen (HR=1.80, 95% CI: 1.192-2.718, P = 0.005). Among laboratory parameters, serum albumin (ALB) was a protective factor (HR=0.955, 95% CI: 0.934-0.976, P =0.001), while decreased hemoglobin (HGB, HR=0.99, P = 0.023), and elevated monocyte percentage (M%, HR=1.01, P =0.008) were associated with inferior PFS (Figure 6A). For OS, several factors were significantly associated with poor OS, including advanced Ann Arbor stage (HR = 5.826, 95% CI: 1.328–25.56, P = 0.019), bone involvement (HR = 3.189, 95% CI: 1.187–8.565, P = 0.021), higher IPI score (HR = 2.537, P < 0.001), elevated LDH (HR = 3.478, P = 0.011), and high β2-MG (HR = 6.133, P = 0.001). Additionally, increased ECOG score (HR = 3.055, P < 0.001), presence of B symptoms (HR = 4.326, P = 0.004), and older age (HR = 1.093, P < 0.001) were identified as significant risk factors.Conversely, higher levels of ALB (HR = 0.879, P < 0.001), PLT (HR = 0.989, P < 0.001), HGB (HR = 0.97, P < 0.001) and WBC (HR = 0.762, P = 0.005) were associated with a favorable prognosis, acting as protective factors in this cohort (Figure 6B). As illustrated in Figure 7, the analysis identified ALB and M% as significant independent predictors of PFS. Specifically, higher ALB levels were associated with a favorable outcome, acting as an independent protective factor for PFS (HR = 0.960, 95% CI: 0.933–0.987, P = 0.004). Conversely, an elevated M% was confirmed as a significant independent risk factor for disease progression (HR = 1.011, 95% CI: 1.002-1.020, P = 0.019). Other variables included in the model did not demonstrate statistical significance in this multivariate setting.To further evaluate the independent prognostic value of clinical and laboratory parameters, multivariate Cox proportional hazards regression for OS was performed using two distinct models.Initially, a saturated model was constructed by incorporating six variables that showed significance in the univariate analysis: age at diagnosis , Ann Arbor stage,ALB, HGB, M%, and bone involvement. In this comprehensive adjustment, age (HR = 1.120, 95% CI: 1.062–1.181, P < 0.001), ALB (HR = 0.829, P = 0.005), and HGB (HR = 0.958, P = 0.008) remained significantly associated with overall survival (OS). While M% and bone involvement showed a hazardous trend, they did not reach strict statistical significance in this highly adjusted setting.To minimize the masking effect of dominant clinical factors and potential multicollinearity, a parsimonious model focusing on the core biological markers—M% and bone involvement—was analyzed. In this focused model, both M% (HR = 1.026, 95% CI: 1.009-1.044, P = 0.002) and bone involvement (HR = 4.351, 95% CI: 1.458-12.988, P = 0.008) were identified as robust independent prognostic factors for OS, confirming their intrinsic impact on patient outcomes(Figure 8A and B).
Fig. 5.
Survival outcomes (A)PFS and (B)OS in the overall patient population.(C)PFS and (D)OS analysis for classical Hodgkin Lymphoma (cHL), stratified by histological subtypes.(E)PFS and (F)OS comparing cHL patients with and without extranodal involvement.
Fig. 6.
Univariate survival analysis of the factors associated with the progression-free survival (PFS) or overall survival (OS) of the enrolled cHL patients. Forest plot to show the factors associated with (A)PFS and (B)OS using univariate cox regression analysis in the enrolled patients
Fig. 7.
Multivariate survival analysis of the factors associated with the progression-free survival of the enrolled cHL patients. Forest plot to show the factors associated with PFS
Fig. 8.
Multivariate Cox regression analysis of prognostic factors for overall survival.(A) Baseline Adjusted Model: This comprehensive model incorporates traditional clinical risk factors and systemic laboratory parameters, including age at diagnosis (continuous), Ann Arbor stage (I/II vs. III/IV), albumin (ALB), hemoglobin (Hb), M%, and bone involvement..(B) Focused Biological Model: To minimize the masking effect of dominant clinical variables and address potential multicollinearity, a parsimonious model focusing on the core biological markers—M% and bone involvement—was constructed.
Propensity Score-Matched Analysis of Treatment Outcomes
To minimize potential confounding, PSM was performed for two comparative cohorts: 1) patients receiving the BV+AVD regimen versus ABVD, and 2) those receiving first-line auto-HSCT versus those managed without first-line auto-HSCT.For the BV+AVD regimen model, propensity scores were derived from age, sex, Ann Arbor stage, extranodal involvement, M%, and ALB levels. For the first-line auto-HSCT model, scores were calculated based on age, sex, Ann Arbor stage, first-line treatment regimens, extranodal involvement, M%, and ALB levels. Post-matching balance for both models was achieved, as demonstrated by the SMDs presented in Tables 4 and 5, confirming the comparability of the matched groups for subsequent outcome analysis.In the matched cohort for novel regimens, treatment with BV+AVD was associated with a numerically higher 3-year PFS rate compared to ABVD (95.2% vs. 88.9%); however, this difference was not statistically significant (P = 0.359; Figure 9A). In the matched analysis of first-line auto-HSCT, the auto-HSCT group showed a numerically higher 3-year PFS rate (77.4% vs. 71.4%), though this difference also did not reach statistical significance (P = 0.447; Figure 9B), suggesting a potential clinical benefit for eligible patients.
Table 4.
Baseline Characteristics Before and After Propensity Score Matching(ABVD vs. BV + AVD)
| Variable | Before PSM | After PSM | ||||
|---|---|---|---|---|---|---|
| ABVD (n = 352) |
BV + AVD (n = 34) |
SMD | ABVD(n = 25) | BV + AVD (n = 25) |
SMD | |
| Age, X̄ ± s(Years) | 37.2 ± 16.9 | 40.4 ± 20.9 | 0.175 | 39.0 ± 16.7 | 39.1 ± 22.3 | 0.165 |
| Sex, Male(n,%) | 200(56.8%) | 17(50%) | 0.137 | 13/25 (52%) | 12(48%) | 0.080 |
| Extranodal involvement, Yes(N,%) | 144(40.9%) | 19(55.9%) | 0.303 | 20 (80%) | 14 (56%) | -0.532 |
|
Ann Arbor stage, Advanced stage(N,%) |
174(49.4%) | 30(88.2%) | 0.923 | 21 (84%) | 21 (84%) | 0 |
| M%(X̄ ± s) | 12.9 ± 19.9 | 7.7 ± 9.9 | -0.333 | 7.2 ± 4.1 | 8.7 ± 11.5 | 0.17 |
| ALB, (g/L) | 42.5 ± 9.1 | 52.8 ± 18.5 | 0.71 | 45.7 ± 13.1 | 45.2 ± 12.9 | -0.033 |
Abbreviations: PSM propensity score matching, SMD standardized mean difference, ABVD doxorubicin, bleomycin, vinblastine, dacarbazine
Table 5.
Baseline Characteristics Before and After Propensity Score Matching(auto-HSCT vs. non-auto-HSCT)
| Variable | Before PSM | After PSM | |||||
|---|---|---|---|---|---|---|---|
| auto-HSCT (n = 18) |
non- auto-HSCT (n = 327) |
SMD | auto-HSCT (n = 18) |
non- auto-HSCT (n = 18) |
SMD | ||
| Age, X̄ ± s(Years) | 35.6 ± 12.6 | 39.5 ± 18.7 | -0.246 | 35.6 ± 12.6 | 35.9 ± 12.9 | -0.022 | |
| Sex, Male(n,%) | 14 (77.8%) | 185 (56.6%) | 0.212 | 14 (77.8%) | 14 (77.8%) | 0 | |
| Extranodal involvement, Yes(N,%) | 4(22.2) | 167(51.1) | 0.288 | 4(22.2) | 7(38.9) | 0.167 | |
| First-line Treatment Regimens | ABVD | 14 (77.8%) | 260(79.5%) | -0.017 | 14 (77.8%) | 14 (73.7%) | 0 |
| BV + AVD | 0 (0.0%) | 32 (9.8%) | 0 (0.0%) | 0 (0.0%) | |||
| PD-1-containing regimens | 0 (0.0%) | 6 (1.8%) | 0 (0.0%) | 0 (0.0%) | |||
| BEACOPP | 4(22.2%) | 6(1.8%) | 4(22.2%) | 4(22.2%) | |||
| Other regimens | 0 (0.0%) | 7(2.1%) | 0 (0.0%) | 0 (0.0%) | |||
|
Ann Arbor stage, Advanced stage(N,%) |
17(94.4) | 193(59.0) | -0.354 | 17(94.4) | 17(94.4) | 0 | |
| M% | 7.5 ± 3.7 | 14.2 ± 21.5 | -0.003 | 7.5 ± 3.7 | 7.1 ± 2.8 | 0 | |
| ALB, (g/L) | 42.2 ± 4.6 | 43.0 ± 11.7 | 0 | 42.2 ± 4.6 | 41.7 ± 4.6 | 0 | |
Abbreviations: PSM propensity score matching, auto-HSCT autologous hematopoietic stem cell transplantation, SMD standardized mean difference
Fig. 9.
Survival outcomes (A)PFS and for classical Hodgkin Lymphoma (cHL) patients treated with ABVD or BV+AVD regimens.(B)PFS for cHL patients given or not first-line auto-HSCT After PSM
Treatment-Related Complications
The spectrum and frequency of treatment-related complications are summarized in Table 6. Infectious diseases were the most frequently adverse events, occurring in 31 patients (31/547, 5.7%), followed by chemotherapy-induced lung injury (25, 4.6%) and agranulocytosis (21, 3.8%). Abnormal liver function was observed in 11 patients (2.0%). Less common complications included BV-associated peripheral neuropathy (6, 1.1%), renal impairment (5, 0.9%), secondary malignancices (5, 0.9%), cardiovascular diseases (4, 0.7%), and radiation dermatitis (3, 0.5%).
Table 6.
Treatment Complications of cHL patients
| Type of Complication | Number of cHL patients |
|---|---|
| Agranulocytosis | 21 |
| Abnormal Liver Function | 11 |
| Chemotherapy-Induced Lung Injury | 25 |
| Renal Impairment | 5 |
| Secondary Malignancy | 5 |
| Cardiovascular Disease | 4 |
| Infectious Disease | 31 |
| Radiation Dermatitis | 3 |
| Peripheral neuropathy of BV | 6 |
Discussion
This large-scale, multicenter, real-world study provides a comprehensive clinical profile of 547 patients with cHL from China, delineating the epidemiological trends, clinicopathological characteristics, treatment patterns, and survival outcomes in this study population. Through multivariate analyses and PSM, we identified key prognostic factors and evaluated the clinical value of therapeutic approaches. Globally, the age-standardized incidence and mortality rates of HL were 0.98 and 0.26 per 100,000 population in 2020, respectively[9]. Recent evidence indicates an increasing trend in HL incidence, particularly in Asian countries, whereas HL-related mortality has shown an overall decline over the past decade[10]. Since 1990, advances in diagnostic and therapeutic strategies have contributed to a continuous decrease in age-standardized death rates (ASDR) for all hematologic malignancies. By 2019, the ASDRs for leukemia, multiple myeloma, nonHodgkin lymphoma, and HL were 4.26, 1.42, 3.19, and 0.34 per 100,000, respectively—with HL demonstrating the most pronounced decline[11]The steady increase in diagnosed cases within our cohort after 2016 may reflect not only a slight rise in incidence but also improved diagnostic capabilities and enhanced electronic medical record systems. We reported an excellent 5-year OS of 95.5%, corroborating the high cure rate achievable with modern cHL therapies. However, the 5-year PFS was 66.2%, notably lower than the OS. This discrepancy, potentially related to the tertiary referral centers in our study enrolling a higher proportion of refractory/relapsed cases, underscores that preventing and managing disease recurrence remains a critical challenge. The pathological subtype distribution in our cohort, with nodular sclerosis cHL (49.5%) being most prevalent, followed by mixed cellularity cHL(MCcHL;34.4%), aligns with global epidemiology [1]. Furthermore, Antonia et al. reported that the tumor microenvironment in MCcHL is enriched with FOXP3⁺, FOXP3⁺/CD4⁺, and CD8⁺ T cells, potentially linked to Epstein–Barr virus (EBV) infection, which may contribute to poorer outcomes [13]. This suggests that EBV status should be routinely assessed in MCcHL, warranting closer monitoring and further investigation. Our survival analysis confirmed lymphocyte-depleted cHL as having the worst OS, consistent with existing literature[14], highlighting the need for more aggressive therapeutic strategies for this rare subtype. In our comprehensive multivariate analysis for OS, classic factors such as age and systemic parameters (e.g., ALB, HGB) were validated as robust independent predictors. Furthermore, to minimize the masking effect of dominant clinical variables and address potential multicollinearity, a focused biological model revealed that M% is also a powerful independent prognostic factor for OS. Similarly, ALB and M% were identified as independent predictors for PFS. Established prognostic models, from the International Prognostic Score (IPS) to the more recent Advanced-Stage Hodgkin Lymphoma International Prognostic Index (A-HIPI), have incorporated variables such as age, stage, albumin, and hematological parameters[15-17]. Additionally, circulating tumor DNA (ctDNA) has shown promise in assessing treatment response and predicting relapse in HL[18].Our findings reinforce the central role of nutritional status (ALB) and the immune microenvironment (M%) in cHL outcomes. While hematological indices like neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), and platelet-lymphocyte ratio (PLR) have been linked to prognosis in early-stage HL[19], and low albumin is a recognized poor prognostic factor in elderly advanced-stage patients[20], our study confirms albumin's independent prognostic value across all ages and stages. The specific prognostic significance of M% in cHL is novel; although it has been associated with outcomes in other lymphomas[21], its role in cHL merits further mechanistic exploration. We hypothesize that circulating monocytes are recruited into the tumor microenvironment and differentiate into tumorassociated macrophages (TAMs), which, often polarized to an M2 phenotype, promote tumor growth and immune suppression, as seen in diffuse large Bcell lymphoma[22, 23]. Lymphomas arising from extranodal sites constitute approximately 30% of infiltrations might affect clinical outcomes, our multivariate model for PFS did not identify lung, bone, or spleen involvement as independent predictors of progression, likely due to effective salvage therapies or collinearity with systemic markers. Notably, however, in our focused biological model for OS, bone involvement emerged as a significant independent predictor of inferior survival (P=0.008), highlighting its distinct organ-specific prognostic implications. Although bone marrow involvement has been extensively studied in lymphoma, bone involvement itself remains underexplored. Our findings underscore the clinical significance of bone infiltration as a distinct risk factor in cHL.To minimize confounding biases, a PSM analysis was conducted. In the matched cohorts, treatment with BV+AVD was associated with a numerically higher 3-year PFS compared to ABVD (95.2% vs. 88.9%), though this difference did not reach statistical significance (P=0.359). Similarly, first-line auto-HSCT consolidation showed a numerically higher 3-year PFS rate (77.4% vs. 71.4%, P=0.447). While these findings did not demonstrate statistical superiority, they suggest a potential clinical benefit for eligible patients, justifying further investigation into its role for high-risk patients in larger real-world cohorts. This aligns with the established principle that pre-transplant response status is critical for outcomes in relapsed/refractory cHL[25], and we extend this consideration to frontline consolidation. Based on our risk stratification model, which integrates independent prognostic factors like hypoalbuminemia, bone involvement, advanced age, and elevated M%, we propose a refined approach to identify high-risk patients. For such individuals, intensified first-line strategies, including regimens like BV+AVD or first-line auto-HSCT consolidation, should be considered to potentially improve PFS. Patients with the lymphocyte-depleted subtype or specific extranodal involvement warrant particular attention and personalized treatment plans.With the favorable prognosis of cHL, adverse events are a major concern. In early-stage patients, cardiovascular disease becomes the leading cause of non-relapse mortality after long-term follow-up[26] Additionally, suboptimal treatment response increases infection risk[16]. Consistent with this, our study found infectious diseases (5.7%) and chemotherapy-induced lung injury (4.6%) to be the most common adverse events. Furthermore, with the introduction of targeted agents, emerging toxicities such as BV-associated peripheral neuropathy (1.1%) must be actively monitored and managed to optimize patient quality of life.The principal strengths of this study lie in its large-scale, multicenter, real world design, long-term follow-up, and comprehensive analysis incorporating multivariate and propensity score-matched analyses to minimize bias, ensuring robust and clinically relevant findings. However, the limitations inherent to its retrospective design must be acknowledged. Treatment selection was non-randomized, and despite statistical adjustments, residual confounding from unmeasured factors cannot be entirely excluded.
Conclusion
This study delineates the characteristics and outcomes of contemporary Chinese cHL patients, confirming key prognostic factors such as serum albumin, elevated M%, and bone involvement. It further demonstrates the potential clinical trend towards improved PFS with the use of the BV+AVD regimen and first-line autologous transplantation. Future prospective studies in the Chinese population will focus on risk-adapted strategies guided by novel biomarkers (e.g., ctDNA) and PET imaging, aiming to further optimize efficacy and reduce treatment-related toxicity.
Abbreviations
- BV
brentuximab vedotin
- cHL
classical Hodgkin lymphoma
- PSM
Propensity score matching
- PFS
progression-free survival
- OS
overall survival
- ALB
albumin
- HGB
hemoglobin
- M%
peripheral blood monocyte percentage
- auto-HSCT
autologous hematopoietic stem cell transplantation
- ECOG
Eastern Cooperative Oncology Group
Author contributions
Conceptualisation, methodology, and formal analysis: Hongmei Jing, Lingli Wang. Writing—original draft: Lingli Wang. Resources and data acquisition: Lingli Wang, Jinping Pi, Xiaohui He, Wei Zhang, Hui Liu, Jiefei Bai, Yu Zhao, Kaili Zhong, Li Bao, Shan Gao, Zhenling Li, Jingjing Wu, Hebing Zhou, Yingtong Chen. Data verification: Lingli Wang, Yingtong Chen , Shuozi Liu. Writing—review and editing: Hongmei Jing.
Funding
This study was supported by the special fund of the National Clinical Key Specialty Construction Program, P. R. China (2023).
Data availability
All the data in this study are available from the corresponding author upon reasonable request.
Declarations
Competing Interests
The authors declare no competing interests.
Conflict of interest
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lingli Wang and Jinping Pi served as co-first authors for this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All the data in this study are available from the corresponding author upon reasonable request.