Open Access

A modified prognostic model in patients with diffuse large B‑cell lymphoma treated with immunochemotherapy

  • Authors:
    • Peiqi Zhao
    • Lei Zhu
    • Lanfang Li
    • Shiyong Zhou
    • Lihua Qiu
    • Zhengzi Qian
    • Wengui Xu
    • Huilai Zhang
  • View Affiliations

  • Published online on: January 20, 2021     https://doi.org/10.3892/ol.2021.12479
  • Article Number: 218
  • Copyright: © Zhao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

In the era of immunochemotherapy, the traditional international prognostic index (IPI) has partially lost its predictive value in diffuse large B‑cell lymphoma (DLBCL) and the National Comprehensive Cancer Network‑IPI (NCCN‑IPI) is unable to effectively identify high‑risk patients. Thus, the present study aimed to develop a modified prognostic model (M‑PM) to identify high‑risk patients that require aggressive treatment. The present study included 169 patients with newly diagnosed DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (RCHOP) or RCHOP‑like regimens, between 2011‑2017. The results demonstrated that the risk discrimination was improved in the NCCN‑IPI compared with the IPI, and patients were divided into four risk groups with a 5‑year overall survival rate of 93.8, 76.5, 54.3 and 39.4%, respectively. However, the NCCN‑IPI failed to identify the high‑risk DLBCL population. The newly developed M‑PM presented here included four parameters: Age (≥65 years), an elevated lactate dehydrogenase level, Eastern Cooperative Oncology Group score ≥2 and total metabolic tumor volume ≥300 cm3. The M‑PM also divided patients into four risk groups that comprised 40.8, 23.1, 26.0 and 10.1% of the patients, and the 5‑year survival rates of these groups were 92.4, 70.6, 52.3 and 24.5%, respectively. Taken together, the results of the present study demonstrated that the M‑PM was more accurate compared with the IPI and the NCCN‑IPI, which served as an effective tool for identifying patients with DLBCL at high risk of an adverse prognosis.

Introduction

Diffuse large B-cell lymphoma (DLBCL) is one of the most common subtypes of Non-Hodgkin's lymphoma (NHL) in adults, accounting for ~30% of NHLs (1). DLBCL has significant heterogeneity in clinical manifestations, biological characteristics, and prognosis (24). Although >50% of patients with DLBCL may be cured by upfront chemoimmunotherapy (2), ~40–50% of patients relapse and/or the disease becomes refractory, and it is estimated that one third of patients will eventually die of the disease (5).

Given the notable heterogeneity within DLBCL, an accurate and reliable prediction tool is essential to optimize the treatment of patients. Since 1993, the international prognostic index (IPI) has become a major clinical predictive tool for the prognosis of patients with DLBCL (6). Based on the number of adverse prognostic factors, four independent risk groups were identified, and the 5-year overall survival (OS) rate was between 26–73% (6). However, as rituximab significantly improves the prognosis of patients with DLBCL, use of the IPI in identifying high-risk groups is questionable (7,8).

In 2014, Zhou et al (9) proposed the National Comprehensive Cancer Network (NCCN)-IPI based on IPI, which highlights the prognostic effects of age, lactate dehydrogenase (LDH) level and extranodal involvement site. Although previous studies have reported that the NCCN-IPI has better risk stratification than the IPI between low-risk and high-risk DLBCL (5-year OS, 96% vs. 33% for NCCN-IPI; 5-year OS 90% vs. 54% for IPI), NCCN-IPI fails to identify extremely high-risk populations (1013). As was the case with its predecessor, the prognostic factors of NCCN-IPI mainly come from the clinical indicators of DLBCL (14). However, the model does not contain information obtained from standardized imaging techniques.

Positron Emission Tomography-Computed Tomography (PET/CT) has great value in the accurate staging, evaluation of efficacy, determination of prognosis and guidance for the subsequent treatment of malignant lymphoma (1517). High fluorodeoxyglucose (FDG) uptake is a surrogate indicator of aggressive biological characteristics of malignant lymphoma (18). The total metabolic tumor volume (TMTV) assessed by PET/CT can be used as an index to measure tumor volume and invasiveness (18). Previous studies have demonstrated that TMTV has a greater prognostic value than Ann Arbor stage and can be used as a prognostic factor independent of IPI (19,20). High TMTV is associated with poor progression-free survival (PFS) (21). Analyses from the GOYA study revealed that higher TMTV is significantly associated with poor prognosis in patients independent of IPI (22). Notably, quantitative tumor imaging indicators, such as TMTV have the potential to replace traditional IPI factors that reflect tumor burden, such as extranodal diseases and Ann Arbor stage (19,20).

Thus, in the era of immunochemotherapy, the present study aimed to design a novel prognostic model of DLBCL composed of standardized imaging techniques and clinical parameters, and to assess its prognostic value in patients with DLBCL.

Patients and methods

Patients and data collection

This retrospective study included 169 patients treated at Tianjin Medical University Cancer Institute and Hospital between January 2011 and December 2017. The present study was approved by the Institutional Review Board of the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China) and written informed consent was obtained from all the patients. The inclusion criteria were as follows: i) Age (>18 years), regardless of sex; ii) CD20 positive patients with DLBCL who had not received treatment in the past and iii) first-line treatment options were rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (RCHOP) or RCHOP-like regimens. Diagnosis of DLBCL was confirmed by Dr. Meng and Dr. Zhai from the Department of Pathology at The Tianjin Medical University Cancer Hospital (Tianjin, China). The immunohistochemical markers CD10, BCL-6 and multiple myeloma oncogene 1 were detected and patients were subsequently divided into germinal-center B-cell-like (GCB) or non-GCB subtypes, using Hans' algorithm (23). All patients were restaged according to the Lugano classification system (24). Bulky disease was defined as a measurable tumor mass ≥7.5 cm in diameter.

The IPI includes five risk factors: Age (>60 years), LDH above upper normal value, the involvement of extra-lymph node tissues or organs >1, Eastern Cooperative Oncology Group (ECOG) score ≥2 and disease stage III/IV (6). According to the number of poor prognostic factors, IPI divided patients into four groups: Low-risk group, low-intermediate risk group, intermediate-high risk group and high-risk group. The NCCN-IPI relies on the same five poor prognostic indicators as the IPI, but the patient's age, elevated LDH levels and specific extranodal involvement are more heavily weighted (9). Patients were also divided into four groups by the NCCN-IPI (Table I).

Table I.

Characteristics and scoring of the different systems.

Table I.

Characteristics and scoring of the different systems.

CharacteristicIPIScoreNCCN-IPIScoreM-PMScore
Age, yearsNA0>40 to ≤601NA0
>601>60 to ≤752≥651
NA0>753NA0
Ann Arbor stageIII–IV1III–IV1NA0
LDH, normalized>11>1 to ≤31>11
NA0>32NA0
Extranodal disease≥21NA0NA0
aDistinct extranodal diseaseNA0Any1NA0
Performance status≥21≥21≥21
COONA0NA0NA0
TMTV, cm3NA0NA0≥3001
Maximum scoreNA5NA8NA4

a Extranodal involvement of bone marrow, central nervous system, liver/gastrointestinal tract, or lung. IPI, international prognostic index; NCCN, National Comprehensive Cancer Network; M-PM, modified prognostic model; LDH, lactate dehydrogenase; COO, cell of origin; TMTV, total metabolic tumor volume; NA, not applicable.

PET imaging

All patients underwent FDG PET/CT scanning prior to chemotherapy. PET/CT studies were performed according to protocols and manufacturer guidelines (25). A total of two experienced nuclear medicine experts calculated the quantitative parameters. The TMTV was obtained by summarizing the metabolic volume of all lymph nodes and extra-lymph node lesions. Bone marrow involvement was included in the volume measurement only in the presence of focal uptake. The spleen was involved if the focal or disseminated uptake was >150% of the liver background.

Statistical analysis

Statistical analysis was performed using SPSS 22 software (IBM Corp.). OS time refers to the time from random assignment to mortality for any reason (lost follow-up is the last follow-up time; patients who are still alive at the end of the study are the end date of follow-up) (26). PFS refers to the time from the start of randomization to the first tumor progression, relapse, mortality or last contact (26). The Kaplan-Meier method was used to calculate OS and PFS time, and the log-rank test was used to determine statistically significant differences between the two groups. Univariate and multivariate analyses were performed according to the Cox regression model to assess prognostic value. P≤0.05 was considered to indicate a statistically significant difference.

Results

Clinical characteristics of patients

The clinical characteristics of the 169 patients are presented in Table II. The patients' median follow-up time was 60 months. All patients received induction chemotherapy containing R-CHOP or RCHOP-like regimens, and 88.8 and 11.2% received RCHOP and R-mini-CHOP, respectively. A total of 49 patients (29.0%) were >65 years old and men have slightly more cases than women (53.3%). There were 25 patients (14.8%) with bulky disease (mass >7.5 cm). The involvement of bone marrow occurred in 23 cases (13.6%). A total of 52 patients (30.8%) were diagnosed at stage I–II, while the remaining 117 patients (69.2%) were diagnosed at stage III–IV. A total of 99 patients (58.6%) had elevated LDH levels, and 28 patients (16.6%) had an ECOG performance status ≥2. Extranodal involvement was present in 43 patients (25.4%). According to the IPI, 56 patients (33.1%) and 35 patients (20.7%) had low or low-intermediate IPI scores, respectively, whereas 44 patients (26.1%) and 34 patients (20.1%) were categorized as intermediate-high or high risk, respectively. According to the NCCN-IPI, 42 cases (24.9%) were classified as low risk, 51 as low-intermediate risk, 48 as intermediate-high and 28 cases as high risk. Notably, fewer patients were classified as low risk by the NCCN-IPI compared with the traditional IPI.

Table II.

Clinical characteristics of patients (n=169).

Table II.

Clinical characteristics of patients (n=169).

CharacteristicNumber of patients, n (%)
Age, years
  <65120 (71.0)
  ≥6549 (29.0)
Sex
  Male90 (53.3)
  Female79 (46.7)
Presence of B-symptoms
  No118 (69.8)
  Yes51 (30.2)
Performance status
  0–1141 (83.4)
  ≥228 (16.6)
Serum LDH
  Normal70 (41.4)
  Elevated99 (58.6)
Stage
  I–II52 (30.8)
  III–IV117 (69.2)
Extranodal involvement
  <2126 (74.6)
  ≥243 (25.4)
BM involvement
  Absent146 (86.4)
  Present23 (13.6)
IPI
  Low, 0–156 (33.1)
  Low-intermediate, 235 (20.7)
  High-intermediate, 344 (26.1)
  High, 4–534 (20.1)
Subtype
  GCB72 (42.6)
  Non-GCB97 (57.4)
NCCN-IPI
  Low, 0–142 (24.9)
  Low-intermediate, 2–351 (30.1)
  High-intermediate, 4–548 (28.4)
  High, ≥628 (16.6)
Therapy
  RCHOP150 (88.8)
  RminiCHOP19 (11.2)
Bulky disease
  No144 (85.2)
  Yes25 (14.8)

[i] ECOG; Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase; BM, bone marrow; IPI, international prognostic index; GCB, germinal-center B-cell-like; NCCN, National Comprehensive Cancer Network; RCHOP, rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone.

TMTV

The median TMTV of the entire population was 291.4 cm3 (50.3–1598.4 cm3; Fig. 1). The receiver operating curve (ROC) analysis demonstrated that the best TMTV cut-off value for PFS and OS estimation was 300 cm3 (data not shown). For PFS and OS, the area under the curve (AUC) values were 0.701 and 0.724, respectively (data not shown). The sensitivity and specificity of the 300 cm3 cut-off for PFS were 75.1 and 67.3%, respectively, while the sensitivity and specificity for OS were 76.6 and 66.7%, respectively (data not shown).

The critical value of the ROC curve was used for Kaplan-Meier survival analysis. The results demonstrated that TMTV was a reliable predictor of OS at the univariable level (Table III). As presented in Fig. 2, the 5-year PFS rate of patients with high TMTV (≥300 cm3, n=94, 55.6%) was 38.3%, while that of patients with low TMTV (<300 cm3, n=75, 44.4%) was 72% (P<0.001). The 5-year OS rate of patients with high TMTV was 44.7%, while that of patients with low TMTV was 80% (P<0.001). In addition, significant differences were observed in OS between patients with TMTV values above and below 300 cm3 at the multivariate level [hazard ratio (HR), 4.21; 95% confidence interval (CI), 2.71–7.32; P<0.001; Table III].

Table III.

Multivariate analysis of variables for overall survival.

Table III.

Multivariate analysis of variables for overall survival.

UnivariateMultivariate


Variable (risk factor)HR (95% CI)P-valueHR (95% CI)P-value
Age, years (65 vs. ≥65)3.32 (1.52–6.13)<0.0012.14 (1.20–4.41)0.002
aB-symptoms, Yes vs. No1.54 (1.16–3.26)0.0192.24 (1.38–4.36)0.016
Cell of origin, GCB vs. Non-GCBNS NS
Serum LDH, normal vs. elevated3.29 (1.79–9.12)<0.0014.21 (2.12–14.54)<0.001
ECOG PS, 0–1 vs. 2–42.65 (2.16–5.72)0.0113.32 (2.45–6.62)0.025
Ann Arbor stage, I-11 vs. III–IV3.11 (1.88–5.22)0.015NS
Number of extranodal sites, 0–1 vs. ≥21.89 (1.16–5.51)0.013NS
TMTV, cm3 (≥300 vs. <300)3.13 (2.43–6.76)<0.0014.21 (2.71–7.32)<0.001
Bone marrow involvement, Yes vs. No2.37 (1.27–5.43)0.024NS
bBulky mass, Yes vs. No1.37 (1.19–4.21)0.033NS

a B-symptoms were defined as night sweats, disease-related fevers, or weight loss ≥10% of body weight.

b Tumor size of bulky mass was ≥7.5 cm. GCB, germinal-center B-cell-like; LDH, lactate dehydrogenase; ECOG; Eastern Cooperative Oncology Group; PS, performance status; TMTV, total metabolic tumor volume; HR, hazard ratio; CI, confidence interval; NS, no significance.

Univariate and multivariate analyses

As presented in Table III, the following prognostic factors were assessed in the univariate and multivariable analyses: Age (<65 years vs. ≥65 years), disease stage (I–II vs. III–IV), ECOG performance status (0–1 vs. 2–4), B-symptoms (yes vs. no), LDH level (normal vs. elevated), the number of extranodal sites involved (0–1 vs. ≥2), cell of origin (GCB vs. Non-GCB), bulky disease (yes vs. no), bone marrow involvement (yes vs. no) and TMTV (≥300 cm3 vs. <300 cm3). Univariate analysis demonstrated that age ≥65 years (P<0.001), B-symptoms (P=0.019), elevated LDH levels (P<0.001), ECOG performance status 2–4 (P=0.011), advanced stage (P=0.015), number of extranodal sites >1 (P=0.013), bulky disease (P=0.033), bone marrow involvement (P=0.024) and TMTV ≥300 cm3 (P<0.001) were all associated with poor prognosis. However, multivariate analysis demonstrated that only age ≥65 years (HR, 2.14; 95% CI, 1.20–4.41; P=0.002), B-symptoms (HR, 2.24; 95% CI, 1.38–4.36; P=0.016), elevated serum LDH levels (HR, 4.21; 95% CI, 2.12–14.54; P<0.001), ECOG performance status 2–4 (HR, 3.32; 95% CI, 2.45–6.62; P=0.025) and TMTV ≥300 cm3 (HR, 4.21; 95% CI, 2.71–7.32; P<0.001) were considered independent prognostic factors.

Comparison of the M-PM with existing prognostic indexes

In the rituximab era, the M-PM model presented here combined metabolic parameters and clinical characteristics into a new integrative prognostic factor (Table I). According to the number of IPI risk factors, patients were distributed into four different risk groups. However, the IPI failed to effectively differentiate between the intermediate-high group and the high-risk group. As presented in Fig. 3, the 5-year PFS rate of patients at high risk was 41.2% and the OS rate was 50.8%, whereas those at intermediate-high risk had a 5-year PFS rate of 52.3% and an OS rate of 60.4% (P=0.017 for PFS; P=0.028 for OS). The NCCN-IPI also divided patients into four different risk groups. Each group had significantly different 5-year OS and PFS rates, and the index had a better discriminative ability compared with IPI. According to the NCCN-IPI, the 5-year PFS rate of patients at intermediate-high risk was 45.8% and the 5-year OS rate was 54.3%, while the 5-year PFS rate of patients at high risk was 28.6% and the 5-year OS rate was 39.4% (P=0.004 for PFS; P=0.012 for OS) (Table IV and Fig. 3).

Table IV.

5-year overall survival rates and distribution of patients in the risk groups using IPI, NCCN-IPI and M-PM scores.

Table IV.

5-year overall survival rates and distribution of patients in the risk groups using IPI, NCCN-IPI and M-PM scores.

IPINCCN-IPIM-PM



Risk levelScore (%)5-year PFS, %5-year OS, %Score (%)5-year PFS, %5-year OS, %Score (%)5-year PFS, %5-year OS, %
Low0–1 (33.1)82.190.80–1 (24.9)83.393.8%0–1 (40.8)81.292.4
Low-intermediate2 (20.7)71.478.42–3 (30.1)68.676.5%2 (23.1)61.570.6
Intermediate-high3 (26.1)52.360.44–5 (28.4)45.854.3%3 (26.0)43.252.3
High4–5 (20.1)41.250.86–8 (16.6)28.639.4%4 (10.1)23.524.5

[i] IPI, international prognostic index; NCCN, National Comprehensive Cancer Network; M-PM, modified prognostic model; PFS, progression-free survival; OS, overall survival.

Patients were also divided into four different risk groups by the M-PM, namely the low risk, low-intermediate risk, intermediate-high risk and high risk groups. The groups had a median follow-up time of 61, 58, 59 and 60 months, respectively. As presented in Table IV, the 5-year PFS rates of the four groups were 81.2, 61.5, 43.2 and 23.5%, respectively, while the 5-year OS rates were 92.4, 70.6, 52.3 and 24.5%, respectively (P<0.001 for PFS and OS; Fig. 3). The M-PM identified a group with even worse outcomes, with a 5-year OS rate of only 24.5%, which neither the NCCN-IPI or the IPI identified. Thus, it was concluded that the predictive value of the M-PM was significantly stronger compared with the IPI and the NCCN-IPI for predicting high risk DLBCL (P<0.01). This high-risk group in the M-PM only represented a small number of patients (10.1%), which was lower than that identified by the NCCN-IPI (16.6%) and the IPI (20.1%) (Table IV).

Discussion

DLBCL is a disease with biological heterogeneity, which is reflected in the different curative effects and survival of patients (24). The IPI is the most recognized and widely used prognostic evaluation model in DLBCL (6). In the rituximab era, the cure rate of DLBCL has improved, and the value of IPI's prognostic risk stratification has weakened, particularly among intermediate-high risk and high-risk groups (8,27). In addition, NCCN-IPI and other prognostic evaluation systems cannot sufficiently distinguish high risk patients with a short survival time (28,29). Thus, it remains critical to develop a more accurate prognostic model for DLBCL.

Common prognostic indexes of lymphoma include age, ECOG score and increased LDH levels, which are associated with a short survival time in DLBCL (6). In the original IPI model, the age limit was set to 60 years, which represented the demarcation point for myeloablative therapy and stem cell transplantation at the time (30). Currently, this restriction is no longer in place (30). With the extensive application of growth factors and rituximab, an increasing number of elderly patients have received sufficient immunochemotherapy (30). Thus, in the novel prediction model presented here, the age limit was altered to 65 years. The results of the present study confirmed that age (≥65 years) is a key prognostic indicator. Notably, age (<65 years) is also commonly used as an age node for dose-intensified immunochemotherapy or hematopoietic stem cell transplantation in mantle cell lymphomas (30).

Disease stage is a crude substitute for total tumor burden, which is illustrated by bulky stage I disease vs. stage IV lesions, with extensive but small extranodal lesions (31). FDG-PET/CT uses quantitative indicators of metabolically active tissues, such as TMTV, as indicators of tumor volume (31). In previous studies, the prognosis of patients with DLBCL was often stratified based on interim PET-CT parameters, such as the Deauville score (32,33). The prognostic model presented here is based on baseline PET-CT parameters and clinical markers, and can predict the prognosis of patients earlier. The Maximum Standardized Uptake Value (SUVmax) is the most common metabolic parameter used in the clinic (3437). However, measurement of SUVmax can only detect the most obvious metabolic activity of a tumor at a single site, and is unable to reflect the metabolic activity of the whole tumor, the size and volume of the tumor (34). Furthermore, several factors affect the accuracy of SUVmax, such as uptake interval, injection dose, injection leakage, tumor size and heterogeneity, blood sugar and hormone levels (34,35). Thus, the prognostic value of SUVmax in DLBCL remains controversial (36,37). TMTV and TLG reflect tumor volume and tumor activity (38). Our previous study demonstrated that TMTV is a more robust predictor of survival than TLG (39). Thus, TMTV was incorporated into the novel prognostic model presented here.

Previous studies have reported that TMTV has a strong predictive value for newly treated patients with DLBCL (40,41). Higher TMTV is significantly associated with worse PFS and OS in patients with DLBCL (19,20,42,43). Previous studies have also demonstrated that TMTV measured on 18F-FDG PET/CT can be used as an important index in determining the prognosis of DLBCL (19,43). However, there is still insufficient consensus on the calculation of TMTV. Currently, the two most common methods are based on the fixed threshold of SUV 2.5 (MTV 2.5) and the use of 41% SUVmax isocontour (MTV 41) (41,44). These methods are based on the principle of the fixed threshold to calculate the metabolism of local tumors (41,44). The European Association of Nuclear Medicine recommends a SUVmax threshold of 41%, which has been used in patients with DLBCL with good interobserver repeatability (40). Given that the patients included in these studies had different ethnicities and research methods, different cut-off values were used (20,40,45). The results of the present study demonstrated that TMTV ≥300 cm3 was significantly associated with poor prognosis.

High TMTV was significantly associated with advanced tumor stage and bulky disease (9,14,46). High TMTV often represents a large tumor load and it may eventually replace the traditional IPI factors reflecting tumor load, such as extranodal diseases and Ann Arbor stage (9,14,46). Thus, it would be useful to add indicators associated with tumor metabolic characteristics to the current prognostic model in the form of quantitative PET indicators.

Some studies have included the functional parameters of PET into the prognostic evaluation model of DLBCL (32,47,48). However, most prognostic models are based on the results of interim PET-CT indicators (32,47). The present study predominantly used baseline TMTV and clinical indicators to predict the prognosis of DLBCL. To the best of our knowledge, the M-PM presented here is the first prognostic model to combine PET metabolic parameters and clinical characteristics in the rituximab era. According to the M-PM, there were 40.8, 33.1, 26.0 and 10.1% of patients with a low, low-intermediate, intermediate-high, and high risk, respectively.

Patients with low or intermediate risk can be cured by the RCHOP regimen (49); however, an unmet clinical requirement for patients at high risk remains. The M-PM can effectively identify this group of exceedingly high-risk patients. Future clinical trials should aim to maximize disease control and survival in high risk patients and develop promising targeted drugs.

The present study was not without limitations. First, it was a single-center, retrospective study with a moderate sample size, so there may have been some statistical errors. Secondly, the calculation of TMTV is time-consuming and the current calculation method is not uniform. In addition, transplantation and CAR-T may also affect the overall survival of patients. In our studies, patients who relapsed or refractory did not receive CAR-T cell therapy and allogeneic hematopoietic stem cell transplantation. A total of four patients received autologous hematopoietic stem cell transplantation, of which two patients relapsed 2 years after transplantation. However, a small sample size will not affect the final outcome. In the context of modern treatment, the model presented here should be verified in prospective trials. Furthermore, the present study failed to predict time to next therapy using the novel model. Thus, further studies are required to confirm the results presented here.

In conclusion, the present study proposed a novel modified prognostic model for newly diagnosed DLBCL, which consists of clinical parameters, biological parameters, and standardized imaging techniques. The results confirmed that in the rituximab era, the predictive value of the M-PM is more accurate than that of the IPI and the NCCN-IPI, particularly in the high risk DLBCL group. Furthermore, the M-PM can identify the high-risk population with a 5-year OS rate <30%. Taken together, these results suggest that M-PM may be applied to prospective clinical trials. The prognostic model presented here requires verification through large-scale and multi-center trials. In addition, whether M-PM will retain its strong risk stratification ability in the context of targeted treatment and novel biomarkers also warrants further investigation.

Acknowledgements

Not applicable.

Funding

The present study was supported by the Natural Science Foundation of China (grant no. 81402575).

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

HZ and WX designed the present study. PZ and LZ analyzed the experimental data and drafted the initial manuscript. LL, ZQ, LQ and SZ helped perform some experiments, obtained the data and revised the manuscript for important intellectual content. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Institutional Review Board of Tianjin Medical University Cancer Institute and Hospital (Tianjin, China). Written informed consent was obtained from all the patients.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Jaffe ES: The 2008 WHO classification of lymphomas: Implications for clinical practice and translational research. Hematology Am Soc Hematol Educ Program. 523–531. 2009. View Article : Google Scholar : PubMed/NCBI

2 

Sehn LH and Gascoyne RD: Diffuse large B-cell lymphoma: Optimizing outcome in the context of clinical and biologic heterogeneity. Blood. 125:22–32. 2015. View Article : Google Scholar : PubMed/NCBI

3 

Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, Lawrence MS, Roemer MGM, Li AJ, Ziepert M, et al: Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 24:679–690. 2018. View Article : Google Scholar : PubMed/NCBI

4 

Schmitz R, Wright GW, Huang DW, Johnson CA, Phelan JD, Wang JQ, Roulland S, Kasbekar M, Young RM, Shaffer AL, et al: Genetics and pathogenesis of diffuse large B-cell lymphoma. N Engl J Med. 378:1396–1407. 2018. View Article : Google Scholar : PubMed/NCBI

5 

Cunningham D, Hawkes EA, Jack A, Qian W, Smith P, Mouncey P, Pocock C, Ardeshna KM, Radford JA, McMillan A, et al: Rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone in patients with newly diagnosed diffuse large B-cell non-Hodgkin lymphoma: A phase 3 comparison of dose intensification with 14-day versus 21-day cycles. Lancet. 381:1817–1826. 2013. View Article : Google Scholar : PubMed/NCBI

6 

International Non-Hodgkin's Lymphoma Prognostic Factors Project, . A predictive model for aggressive non-Hodgkin's lymphoma. N Engl J Med. 329:987–994. 1993. View Article : Google Scholar : PubMed/NCBI

7 

Bari A, Marcheselli L, Sacchi S, Marcheselli R, Pozzi S, Ferri P, Balleari E, Musto P, Neri S, Aloe Spiriti MA and Cox MC: Prognostic models for diffuse large B-cell lymphoma in the rituximab era: A never-ending story. Ann Oncol. 21:1486–1491. 2010. View Article : Google Scholar : PubMed/NCBI

8 

Ziepert M, Hasenclever D, Kuhnt E, Glass B, Schmitz N, Pfreundschuh M and Loeffler M: Standard International prognostic index remains a valid predictor of outcome for patients with aggressive CD20+ B-cell lymphoma in the rituximab era. J Clin Oncol. 28:2373–2380. 2010. View Article : Google Scholar : PubMed/NCBI

9 

Zhou Z, Sehn LH, Rademaker AW, Gordon LI, Lacasce AS, Crosby-Thompson A, Vanderplas A, Zelenetz AD, Abel GA, Rodriguez MA, et al: An enhanced International Prognostic Index (NCCN-IPI) for patients with diffuse large B-cell lymphoma treated in the rituximab era. Blood. 123:837–842. 2014. View Article : Google Scholar : PubMed/NCBI

10 

Adams HJ and Kwee TC: Prognostic value of interim FDG-PET in R-CHOP-treated diffuse large B-cell lymphoma: Systematic review and meta-analysis. Crit Rev Oncol Hematol. 106:55–63. 2016. View Article : Google Scholar : PubMed/NCBI

11 

Nakaya A, Fujita S, Satake A, Nakanishi T, Azuma Y, Tsubokura Y, Hotta M, Yoshimura H, Ishii K, Ito T and Nomura S: Enhanced international prognostic index in Japanese patients with diffuse large B-cell lymphoma. Leuk Res Rep. 6:24–26. 2016.PubMed/NCBI

12 

Prochazka KT, Melchardt T, Posch F, Schlick K, Deutsch A, Beham-Schmid C, Weiss L, Gary T, Neureiter D, Klieser E, et al: NCCN-IPI score-independent prognostic potential of pretreatment uric acid levels for clinical outcome of diffuse large B-cell lymphoma patients. Br J Cancer. 115:1264–1272. 2016. View Article : Google Scholar : PubMed/NCBI

13 

Montalbán C, Díaz-López A, Dlouhy I, Rovira J, Lopez-Guillermo A, Alonso S, Martín A, Sancho JM, García O, Sánchez JM, et al: Validation of the NCCN-IPI for diffuse large B-cell lymphoma (DLBCL): The addition of β2-microglobulin yields a more accurate GELTAMO-IPI. Br J Haematol. 176:918–928. 2017. View Article : Google Scholar : PubMed/NCBI

14 

Dybkaer K, Bøgsted M, Falgreen S, Bødker JS, Kjeldsen MK, Schmitz A, Bilgrau AE, Xu-Monette ZY, Li L, Bergkvist KS, et al: Diffuse large B-cell lymphoma classification system that associates normal B-cell subset phenotypes with prognosis. J Clin Oncol. 33:1379–1388. 2015. View Article : Google Scholar : PubMed/NCBI

15 

Cheson BD, Pfistner B, Juweid ME, Gascoyne RD, Specht L, Horning SJ, Coiffier B, Fisher RI, Hagenbeek A, Zucca E, et al: Revised response criteria for malignant lymphoma. J Clin Oncol. 25:579–586. 2007. View Article : Google Scholar : PubMed/NCBI

16 

Schot BW, Zijlstra JM, Sluiter WJ, van Imhoff GW, Pruim J, Vaalburg W and Vellenga E: Early FDG-PET assessment in combination with clinical risk scores determines prognosis in recurring lymphoma. Blood. 109:486–491. 2007. View Article : Google Scholar : PubMed/NCBI

17 

Culpin RE, Sieniawski M, Angus B, Menon GK, Proctor SJ, Milne P, McCabe K and Mainou-Fowler T: Prognostic significance of immunohistochemistry-based markers and algorithms in immunochemotherapy-treated diffuse large B cell lymphoma patients. Histopathology. 63:788–801. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Wight JC, Chong G, Grigg AP and Hawkes EA: Prognostication of diffuse large B-cell lymphoma in the molecular era: Moving beyond the IPI. Blood Rev. 32:400–415. 2018. View Article : Google Scholar : PubMed/NCBI

19 

Kim J, Hong J, Kim SG, Hwang KH, Kim M, Ahn HK, Sym SJ, Park J, Cho EK, Shin DB and Lee JH: Prognostic value of metabolic tumor volume estimated by (18) F-FDG positron emission tomography/computed tomography in patients with diffuse large B-cell lymphoma of stage II or III disease. Nucl Med Mol Imaging. 48:187–195. 2014. View Article : Google Scholar : PubMed/NCBI

20 

Sasanelli M, Meignan M, Haioun C, Berriolo-Riedinger A, Casasnovas RO, Biggi A, Gallamini A, Siegel BA, Cashen AF, Véra P, et al: Pretherapy metabolic tumour volume is an independent predictor of outcome in patients with diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging. 41:2017–2022. 2014. View Article : Google Scholar : PubMed/NCBI

21 

Tout M, Casasnovas O, Meignan M, Lamy T, Morschhauser F, Salles G, Gyan E, Haioun C, Mercier M, Feugier P, et al: Rituximab exposure is influenced by baseline metabolic tumor volume and predicts outcome of DLBCL patients: A Lymphoma Study Association report. Blood. 129:2616–2623. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Vitolo U, Trněný M, Belada D, Burke JM, Carella AM, Chua N, Abrisqueta P, Demeter J, Flinn I, Hong X, et al: Obinutuzumab or rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone in previously untreated diffuse large B-cell lymphoma. J Clin Oncol. 35:3529–3537. 2017. View Article : Google Scholar : PubMed/NCBI

23 

Hans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, Müller-Hermelink HK, Campo E, Braziel RM, Jaffe ES, et al: Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 103:275–282. 2004. View Article : Google Scholar : PubMed/NCBI

24 

Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E, Lister TA; Alliance, Australasian Leukaemia and Lymphoma Group; Eastern Cooperative Oncology Group; European Mantle Cell Lymphoma Consortium, ; et al: Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: The Lugano classification. J Clin Oncol. 32:3059–3068. 2014. View Article : Google Scholar : PubMed/NCBI

25 

Zhu L, Bian H, Yang L, Liu J, Chen W, Li X, Wang J, Song X, Dai D, Ye Z, et al: 18Fluorodeoxyglucose-positron emission tomography/computed tomography features of suspected solitary pulmonary lesions in breast cancer patients following previous curative treatment. Thorac Cancer. 10:1086–1095. 2019. View Article : Google Scholar : PubMed/NCBI

26 

Zhao P, Li L, Zhou S, Qiu L, Qian Z, Liu X, Meng B and Zhang H: CD5 expression correlates with inferior survival and enhances the negative effect of p53 overexpression in diffuse large B-cell lymphoma. Hematol Oncol. 37:360–367. 2019. View Article : Google Scholar : PubMed/NCBI

27 

Sehn LH, Berry B, Chhanabhai M, Fitzgerald C, Gill K, Hoskins P, Klasa R, Savage KJ, Shenkier T, Sutherland J, et al: The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood. 109:1857–1861. 2007. View Article : Google Scholar : PubMed/NCBI

28 

McMillan DC: Systemic inflammation, nutritional status and survival in patients with cancer. Curr Opin Clin Nutr Metab Care. 12:223–226. 2009. View Article : Google Scholar : PubMed/NCBI

29 

Li X, Zhang Y, Zhao W, Liu Z, Shen Y, Li J and Shen Z: The Glasgow Prognostic Score as a significant predictor of diffuse large B cell lymphoma treated with R-CHOP in China. Ann Hematol. 94:57–63. 2015. View Article : Google Scholar : PubMed/NCBI

30 

Dreyling M, Campo E, Hermine O, Jerkeman M, Le Gouill S, Rule S, Shpilberg O, Walewski J and Ladetto M; ESMO Guidelines Committee, : Newly diagnosed and relapsed mantle cell lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 28 (Suppl 4):iv62–iv71. 2017. View Article : Google Scholar : PubMed/NCBI

31 

El-Galaly TC, Villa D, Gormsen LC, Baech J, Lo A and Cheah CY: FDG-PET/CT in the management of lymphomas: Current status and future directions. J Intern Med. 284:358–376. 2018. View Article : Google Scholar : PubMed/NCBI

32 

Sun H, Yu Z, Ma N, Zhou J, Tian R, Zhao M and Wang T: Risk stratification of diffuse large B-cell lymphoma with interim PET/CT by combining deauville scores and international prognostic index. Cancer Manag Res. 11:9449–9457. 2019. View Article : Google Scholar : PubMed/NCBI

33 

Yim SK, Yhim HY, Han YH, Jeon SY, Lee NR, Song EK, Jeong HJ, Kim HS and Kwak JY: Early risk stratification for diffuse large B-cell lymphoma integrating interim Deauville score and International Prognostic Index. Ann Hematol. 98:2739–2748. 2019. View Article : Google Scholar : PubMed/NCBI

34 

Kostakoglu L and Chauvie S: Metabolic tumor volume metrics in lymphoma. Semin Nucl Med. 48:50–66. 2018. View Article : Google Scholar : PubMed/NCBI

35 

Akamatsu G, Ikari Y, Nishida H, Nishio T, Ohnishi A, Maebatake A, Sasaki M and Senda M: Influence of statistical fluctuation on reproducibility and accuracy of SUVmax and SUVpeak: A Phantom Study. J Nucl Med Technol. 43:222–226. 2015. View Article : Google Scholar : PubMed/NCBI

36 

Park S, Moon SH, Park LC, Hwang DW, Ji JH, Maeng CH, Cho SH, Ahn HK, Lee JY, Kim SJ, et al: The impact of baseline and interim PET/CT parameters on clinical outcome in patients with diffuse large B cell lymphoma. Am J Hematol. 87:937–940. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Gallicchio R, Mansueto G, Simeon V, Nardelli A, Guariglia R, Capacchione D, Soscia E, Pedicini P, Gattozzi D, Musto P and Storto G: F-18 FDG PET/CT quantization parameters as predictors of outcome in patients with diffuse large B-cell lymphoma. Eur J Haematol. 92:382–389. 2014. View Article : Google Scholar : PubMed/NCBI

38 

Kim TM, Paeng JC, Chun IK, Keam B, Jeon YK, Lee SH, Kim DW, Lee DS, Kim CW, Chung JK, et al: Total lesion glycolysis in positron emission tomography is a better predictor of outcome than the International Prognostic Index for patients with diffuse large B cell lymphoma. Cancer. 119:1195–1202. 2013. View Article : Google Scholar : PubMed/NCBI

39 

Zhao P, Zhu L, Song Z, Wang X, Ma W, Zhu X, Qiu L, Li L, Zhou S, Qian Z, et al: Combination of baseline total metabolic tumor volume measured on FDG-PET/CT and β2-microglobulin have a robust predictive value in patients with primary breast lymphoma. Hematol Oncol. 38:493–500. 2020. View Article : Google Scholar : PubMed/NCBI

40 

Cottereau AS, Lanic H, Mareschal S, Meignan M, Vera P, Tilly H, Jardin F and Becker S: Molecular profile and FDG-PET/CT total metabolic tumor volume improve risk classification at diagnosis for patients with diffuse large B-cell lymphoma. Clin Cancer Res. 22:3801–3809. 2016. View Article : Google Scholar : PubMed/NCBI

41 

Mikhaeel NG, Smith D, Dunn JT, Phillips M, Møller H, Fields PA, Wrench D and Barrington SF: Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL. Eur J Nucl Med Mol Imaging. 43:1209–1219. 2016. View Article : Google Scholar : PubMed/NCBI

42 

Song MK, Chung JS, Shin HJ, Lee SM, Lee SE, Lee HS, Lee GW, Kim SJ, Lee SM and Chung DS: Clinical significance of metabolic tumor volume by PET/CT in stages II and III of diffuse large B cell lymphoma without extranodal site involvement. Ann Hematol. 91:697–703. 2012. View Article : Google Scholar : PubMed/NCBI

43 

Xie M, Zhai W, Cheng S, Zhang H, Xie Y and He W: Predictive value of F-18 FDG PET/CT quantization parameters for progression-free survival in patients with diffuse large B-cell lymphoma. Hematology. 21:99–105. 2016. View Article : Google Scholar : PubMed/NCBI

44 

Meignan M, Sasanelli M, Casasnovas RO, Luminari S, Fioroni F, Coriani C, Masset H, Itti E, Gobbi PG, Merli F and Versari A: Metabolic tumour volumes measured at staging in lymphoma: Methodological evaluation on phantom experiments and patients. Eur J Nucl Med Mol Imaging. 41:1113–1122. 2014. View Article : Google Scholar : PubMed/NCBI

45 

Ilyas H, Mikhaeel NG, Dunn JT, Rahman F, Møller H, Smith D and Barrington SF: Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 45:1142–1154. 2018. View Article : Google Scholar : PubMed/NCBI

46 

Gulec SA, Suthar RR, Barot TC and Pennington K: The prognostic value of functional tumor volume and total lesion glycolysis in patients with colorectal cancer liver metastases undergoing 90Y selective internal radiation therapy plus chemotherapy. Eur J Nucl Med Mol Imaging. 38:1289–1295. 2011. View Article : Google Scholar : PubMed/NCBI

47 

Yang DH, Ahn JS, Byun BH, Min JJ, Kweon SS, Chae YS, Sohn SK, Lee SW, Kim HW, Jung SH, et al: Interim PET/CT-based prognostic model for the treatment of diffuse large B cell lymphoma in the post-rituximab era. Ann Hematol. 92:471–479. 2013. View Article : Google Scholar : PubMed/NCBI

48 

Zhang YY, Song L, Zhao MX and Hu K: A better prediction of progression-free survival in diffuse large B-cell lymphoma by a prognostic model consisting of baseline TLG and % ∆SUVmax. Cancer Med. 8:5137–147. 2019. View Article : Google Scholar : PubMed/NCBI

49 

Friedberg JW and Fisher RI: Diffuse large B-cell lymphoma. Hematol Oncol Clin North Am. 22941–952. (ix)2008. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

March-2021
Volume 21 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Zhao P, Zhu L, Li L, Zhou S, Qiu L, Qian Z, Xu W and Zhang H: A modified prognostic model in patients with diffuse large B‑cell lymphoma treated with immunochemotherapy. Oncol Lett 21: 218, 2021
APA
Zhao, P., Zhu, L., Li, L., Zhou, S., Qiu, L., Qian, Z. ... Zhang, H. (2021). A modified prognostic model in patients with diffuse large B‑cell lymphoma treated with immunochemotherapy. Oncology Letters, 21, 218. https://doi.org/10.3892/ol.2021.12479
MLA
Zhao, P., Zhu, L., Li, L., Zhou, S., Qiu, L., Qian, Z., Xu, W., Zhang, H."A modified prognostic model in patients with diffuse large B‑cell lymphoma treated with immunochemotherapy". Oncology Letters 21.3 (2021): 218.
Chicago
Zhao, P., Zhu, L., Li, L., Zhou, S., Qiu, L., Qian, Z., Xu, W., Zhang, H."A modified prognostic model in patients with diffuse large B‑cell lymphoma treated with immunochemotherapy". Oncology Letters 21, no. 3 (2021): 218. https://doi.org/10.3892/ol.2021.12479