International Prognostic Index Model Research Articles

The prognostic value of controlling nutritional status (CONUT) score–based nomogram on extranodal natural killer/T cell lymphoma patients

Controlling nutritional status (CONUT) score as an original nutritional assessment tool can be used to assess the prognosis of patients with a variety of malignancies. However, the predictive power of CONUT in extranodal natural killer/T cell lymphoma (ENKTL) patients has never been demonstrated. Our retrospective multicenter study aimed to explore the prognostic value of CONUT in newly diagnosed ENKTL. A total of 1085 newly diagnosed ENKTL patients between 2003 and 2021 were retrospectively retrieved. Cox proportional hazard model was used to explore the prognostic factors of overall survival (OS). The survival rate of ENKTL was evaluated using Kaplan-Meier analysis, and log-rank test was applied to the difference between groups. We investigated the prognostic performance of CONUT, the International Prognostic Index (IPI), the Korean Prognostic Index (KPI), and the Prognostic Index of Natural Killer Cell Lymphoma (PINK) using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). The median age at diagnosis for the whole cohort was 47 years, and the male to female ratio was 2.2:1. The 5-year OS for all patients was 72.2%. Multivariable analysis showed that CONUT, age, bone marrow involvement, ECOG PS score, and Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL stage were identified as independent predictive factors for OS. Based on multivariable results, a prognostic nomogram was developed. Subgroup analysis demonstrated that patients with severe malnutrition had poorest clinical outcome. In addition, ROC curves and DCA analysis proved that compared with IPI, KPI, and PINK models, the CONUT score-based nomogram showed a better prognostic predictive efficiency of ENKTL. CONUT could effectively stratify the prognosis of ENKTL and the proposed nomogram based on CONUT was an effective prognostic model for prediction.

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma

AbstractThe objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.

18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy

To develop a combined model incorporating the clinical and PET features for identifying patients with diffuse large B-cell lymphoma (DLBCL) at high risk of progression or relapse after first-line therapy, compared to International Prognostic Index (IPI) and Deauville score (DS) assessment. 271 18F-FDG PET images with DLBCL were retrospectively collected and randomly divided into the training (n=190) and test dataset (n=81). All visible lesions were annotated. Baseline, end-of-treatment (EoT), and delta PET radiomics features were extracted. IPI model, the baseline clinical model group (MG), DS model, the combined clinical MG, the PET-based radiomics MG, and the combined MG were constructed to predict 2-year time to progression (2Y-TTP). For each MG, the cross-combination method was performed to generate 1680 candidate models based on three normalization methods, 20 features, 4 feature-selection methods, and 7 classifiers. The model achieving the highest AUC was selected as the best for each MG. Cox regression analysis was further performed. In the test set, the best combined model showed better discriminative power compared to IPI model, the best baseline clinical model, DS model, the best combined clinical model, and the best PET-based radiomics model (AUC 0.898 vs. 0.584, 0.695, 0.756, 0.824, 0.832; p < 0.001, 0.014, 0.018, 0.152, 0.042, respectively). The combined model was superior to other models for progression-free-survival prediction (C-index: 0.853 vs. 0.568, 0.666, 0.753, 0.808, 0.814, respectively). A combined model for identifying patients at high risk of progression or relapse after first-line therapy was constructed, superior to IPI and DS assessment.

External Validation Shows That Baseline PET Radiomics Outperform the IPI Risk Score for Prediction of Outcome in DLBCL

Introduction Up to one-third of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients relapse or fail to achieve complete remission following first-line treatment. Adding PET radiomics features to currently used clinical predictors may improve the identification of poor prognosis patients. The objective of this study was to externally validate the radiomics model developed in the HOVON-84 trial (Eertink et al, EJNMMI 2021) using datasets from 6 other DLBCL research studies within the PETRA database. Methods 1195 newly diagnosed DLBCL patients with baseline 18F-FDG PET/CT scans with 2 years follow up in 7 studies in the PETRA database (https://petralymphoma.org) were included. 308 patients from the HOVON 84 were used as test set previously described. 887 patients from 6 studies in the PETRA database were used as external validation datasets. Primary outcome measures were 2-year progression free survival (PFS) and 2-year time to progression (TTP). Lesions were delineated using a fully automated preselection of 18F-FDG avid structures defined by a standardized Uptake Value (SUV) ≥ 4.0 and volume >3mL. Missed lesions were manually added and non-tumor regions were removed using the ACCURATE tool. Metabolic tumor volume (MTV), the maximum distance between the largest lesion and any other lesion (DmaxBulk) and the peak standardized uptake value (SUVpeak) were extracted for all patients. We tested the predictive value of the currently used international prognostic index (IPI) score and the radiomics model developed in the HOVON-84 trial, which included MTV, DmaxBulk, SUVpeak, WHO performance status and age. Model performance was assessed using the receiver-operator-characteristics area under the curve (AUC). To allow comparison of the continuous radiomics model with individual predicted probabilities per patient with the categorical IPI risk score, the high-risk group for the radiomics model was set as equal the size of the high-risk IPI group. Diagnostic performance was assessed using the positive predictive value (PPV). Differences between model performances of prediction models, expressed as AUC, were assessed with the two-sided DeLong test. Results Using 2-year PFS as outcome, the IPI model yielded an AUC of 0.62 using all 1195 patients (Table 1). Within the 6 external datasets, the AUC of the IPI model ranged from 0.51 for the SAKK study to 0.65 for the PETAL study. The radiomics model yielded an AUC of 0.71, which was significantly higher than the IPI model (p < 0.001). The AUC of the radiomics model ranged between 0.59 for the HOVON-130 study to 0.75 for the PETAL study. For all external datasets, the AUC of the radiomics model was higher than the AUC of the IPI model. Comparable results were found using 2-year TTP as outcome. The IPI model yielded an AUC of 0.62 and the radiomics model yielded an AUC of 0.71 when using all 1195 patients (p < 0.001). Again, the AUCs of the radiomics models were consistently higher than the AUCs of the IPI model for all individual studies. High-risk patients according to the IPI model had a 2-year PFS of 60.3% (95% confidence interval (CI): 54.4-67.0). High risk patients by the radiomics model had a 2-year PFS of 50.9% (95% CI: 44.8-57.7). The PPV increased from 35.5% for the IPI model to 49.1% for the radiomics model. For 2-year TTP as outcome, high-risk IPI patients had an overall survival rate of 66.4% (95%CI: 60.3-73.0). High-risk radiomics patients had a survival rate of 55.5% (95% CI: 49.1-62.6). The PPV increased from 33.7% to 44.6% for the radiomics model compared to the IPI model. Conclusion The radiomics model that was developed in the HOVON-84 dataset remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI risk score in all studies. Differences between IPI and radiomics model performance between individual studies are most likely caused by differences in patient characteristics between studies. Our radiomics model is able to select high-risk patients more accurately than the IPI model, with higher model performance and PPV. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma

AbstractWe investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography–computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.

18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma

PurposeAccurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment.MethodsThree hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values.ResultsThe IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively).ConclusionPrediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance.Trial registration number and dateEudraCT: 2006–005,174-42, 01–08-2008.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value for long-term survival in diffuse large B-cell lymphoma.

For patients with diffuse large B-cell lymphoma (DLBCL), survival at 24 months is a milestone for long-term survival. The purpose of this study was to develop a multigene risk score (MGRS) to refine the International Prognostic Index (IPI) model to identify patients with DLBCL at high risk of death within 24 months. Using a robust statistical strategy, we built a MGRS incorporating nine mRNAs and two lncRNAs. Stratification and multivariable Cox regression analysis confirmed the MGRS as an independent risk factor. A nomogram based on IPI+MGRS model was constructed and its calibration plot showed close agreement between predicted 2-year survival rate and observed rate. The 2-year AUC was bigger with the IPI+MGRS model (ΔAUC=0.162; 95%CI 0.1295–0.1903) than with the IPI model, and the IPI+MGRS model more accurately predicted the prognostic risk of DLBCL. The 2-year survival decision curve revealed the IPI+MGRS model was more useful clinically than the IPI model. Functional enrichment analysis showed that the MGRS correlated with cell cycle, DNA replication and repair. The results were validated using an independent external dataset. In conclusion, we successfully developed an integrated mRNA–lncRNA signature to refine the IPI model for predicting long-term survival of patients with DLBCL.

A Prognostic Model Incorporating Tumor Lesion Morphology and Texture Features Identifies High-Risk Patient Subpopulations in De Novo DLBCL and FL

Background: Despite effective first-line (1L) treatment options for patients with NHL almost 40% of patients with diffuse large B cell lymphoma (DLBCL) will have a poor response or disease progression after 1L treatment. In follicular lymphoma (FL) 15-20% of patients experience early relapse, and almost 8% may develop transformation to more aggressive forms of the disease (such as DLBCL) after 1L treatment. More accurate identification of patients at high-risk for a poor prognosis with the standard of care could lead to improved outcomes. Although the International Prognostic Index (IPI) and its FL extension (FLIPI) are often used to stratify patients by prognosis, they have relatively modest sensitivity and specificity for predicting individualized risk. Radiomics is a promising approach to improve upon existing prognostic models because it provides a comprehensive quantification of tumor lesion morphology and texture derived from FDG-PET scans and may provide new and important information about disease biology and progression risk on an individual level. Methods: A collection of 107 radiomics features [pyradiomics v2.20] that describe shape, size or volume and texture of tumor lesions, including complex features that are believed to reflect the underlying biological tumor phenotype and microenvironment, were derived for n=1093 de novo DLBCL patients with available baseline FDG-PET scans from the Phase III GOYA study (NCT01287741) evaluating obinutuzumab plus CHOP chemotherapy (G-CHOP) versus rituximab plus CHOP chemotherapy (R-CHOP) (Vitolo, et al. J Clin Oncol 2017). The same set of features were also extracted from n=451 de novo FL patients with available baseline FDG-PET scans from the Phase III GALLIUM study (NCT01332968) comparing obinutuzumab plus chemotherapy with rituximab plus chemotherapy [Marcus, et al. N Engl J Med 2017]. To investigate the association between the derived radiomics features along with baseline clinical variables and progression-free survival (PFS), a Cox proportional hazard model with L1 regularization was trained and internally validated using the GOYA study. We used a nested Monte Carlo Cross Validation (nMCCV) strategy to train our model and provide high- and low-risk group predictions on held-out samples of data. This modeling strategy allows us to make a group prediction on all GOYA patients while reducing overfitting. To evaluate prognostic performance, we ported the final model trained using the GOYA study (called the Li prognostic model) to the fully independent GALLIUM study. Results: Using our nMCCV approach we identified 11 factors, with an inclusion probability of &amp;gt;50%, that are associated with PFS of DLBCL patients (Figure A). Included within the top features are several image-derived morphometric (i.e. metabolic tumor volume, surface area) and radiomics features (i.e. tumor elongation, NGTDM contrast, GLCM inverse variance). When stratifying patients on the predicted (via majority vote) low-risk vs high-risk groupings we found that our high-risk group had significantly worse prognosis vs the low-risk group (Figure B). In comparison, the high-risk group from the IPI model (defined as IPI &amp;gt; 2) had significantly worse prognosis vs the low-risk group, but the performance was slightly worse than our model (Figure C). PFS probability estimates at 2 and 5 years for predicted high-risk patients was 72.7% [70.0-76.6] and 59.8% [54.8-65.2] (vs 74% [70.0-78.2] and 60.4% [55.1-66.2] for the IPI model). After training and testing in the DLBCL population, we evaluated the prognostic performance of our model in an independent set of FL patients. We found that high-risk FL patients had a significantly worse prognosis than the low-risk group (Figure D). PFS probability estimates at 2 and 5 years for predicted high-risk patients was 77.4% [69.8-85.8] and 48.9% [39.5-60.5] (vs. 80% [0.748-0.856] and 58.3% [51.6-65.9] in the full group). Conclusions: Radiomics features are prognostic in DLBCL and provide a modest improvement in prognostic performance when combined with traditional IPI scores, clinical features, and lab values (vs IPI alone). Our prognostic signature, developed in DLBCL, has significant prognostic performance in an independent dataset of patients with FL. While these results are promising, our FL validation dataset was relatively small and further evidence is required to confirm our findings. Disclosures Li: Genentech, Inc.: Current Employment; F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Jemaa:F. Hoffmann-La Roche: Current equity holder in publicly-traded company; Genentech, Inc.: Current Employment. Carano:F. Hoffmann-La Roche: Current equity holder in publicly-traded company; Genentech, Inc.: Current Employment. Bengtsson:Genentech, Inc.: Current Employment; F. Hoffmann-La Roche: Current equity holder in publicly-traded company. Paulson:F. Hoffmann-La Roche: Current equity holder in private company, Current equity holder in publicly-traded company; Genentech, Inc.: Current Employment. Jansen:F. Hoffmann-La Roche: Current Employment; Molecular Health GmbH: Ended employment in the past 24 months; F. Hoffmann-La Roche, Abbvie, Alphabet, other (non-healthcare), indexed funds and ETFs: Current equity holder in publicly-traded company. Nielsen:F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Hibar:Genentech, Inc.: Current Employment; F. Hoffmann-La Roche: Current equity holder in publicly-traded company.

KMT2D and TP53 mutation status improve the prognostic value of the International Prognostic Index (IPI) stratification in ENKTL patients.

Extranodal NK/T-cell lymphoma nasal type (ENKTL) is a subtype of T cell lymphoma with poor prognosis. In this study, we designed a new prognostic model specifically for ENKTL to improve the risk stratification. In 29 ENKTL patients, we screened mutations in 9 ENKTL-associated genes using next-generation sequencing (NGS). We have found that mutated KMT2D was associated with the inferior overall survival (OS) and progression free survival (PFS) and KMT2D or TP53 mutations were associated with a higher mortality rate. Moreover, the difference in PFS among different stratifications was not significant using the International Prognostic Index (IPI) alone but was significant after the mutation status of KMT2D and TP53 were incorporated into the IPI model, forming a harmonious risk stratification reflecting the clinical features and genetic information of ENKTL. In summary, we demonstrate that the prognostic value of the IPI system can be enhanced by integrating the status of genetic mutations.

The prognostic value of the international prognostic index, the national comprehensive cancer network IPI and the age-adjusted IPI in diffuse large B cell lymphoma

目的比较IPI、美国国家综合癌症网络IPI（NCCN-IPI）及年龄调整的IPI（aa-IPI）三种预后分层模型在弥漫大B细胞淋巴瘤（DLBCL）患者中的预后分层能力。方法回顾性分析2003年1月至2012年12月311例初治DLBCL患者资料，根据治疗方案将患者分为CHOP（环磷酰胺、多柔比星、长春新碱、泼尼松）方案及利妥昔单抗（R）-CHOP方案组，分别采用IPI、NCCN-IPI及aa-IPI对DLBCL患者进行危险度分层。采用Harrell's C统计评价不同模型的预后分层能力。结果①311例患者中接受CHOP方案治疗者128例，接受R-CHOP方案治疗者183例。②CHOP方案组患者的5年总生存（OS）率结果显示，aa-IPI（71.0%对25.0%，P<0.001）、NCCN-IPI（59.7%对26.8%，P<0.001）和IPI（47.6%对36.6%，P=0.003）均能区分中低危和中高危组患者，但前两者区分效果似乎更佳；而R-CHOP方案组，NCCN-IPI拥有更好的预后分层能力，其低危、中低危、中高危患者的5年OS率分别为96.0%、83.0%、66.5%（P=0.009）。③Harrell's C分析结果显示，IPI、NCCN-IPI及aa-IPI预测接受CHOP方案者OS的一致性指数（C-index）分别为0.546、0.667、0.698；预测接受R-CHOP方案者OS的C-index分别为0.611、0.654、0.695。④在≤60岁患者中，IPI、NCCN-IPI、aa-IPI预测接受CHOP方案者OS的C-index分别为0.534、0.675、0.698；预测接受R-CHOP方案者OS的C-index分别为0.584、0.648、0.695。结论NCCN-IPI对于接受利妥昔单抗联合化疗的DLBCL患者有较好的预后分层能力，而aa-IPI对于较年轻的DLBCL患者预后分层能力可能更佳。

Clinical characteristics and prognosis of primary bone lymphoma

Objective To investigate the clinical characteristics, efficacy and prognostic factors of primary bone lymphoma (PBL). Methods The clinicopathologic characteristics and prognostic factors of 19 PBL patients from January 2012 to February 2016 were retrospectively analyzed. Results Nineteen PBL patients included 11 males, 8 females, and the median age was 51 years old. It occurred mainly in cuboid (5 cases) and spine (5 cases). 8 patients received chemoradiotherapy and 11 patients received chemoradiotherapy only. The overall response rate was 89.5%, including 12 cases with complete remission (CR) and 5 cases with partial remission. The 2-year overall survival (OS) and the 2-year progression-free survival (PFS) rates of 19 patients with completed follow-up were 61.0% and 48.3%, respectively. Further analysis of 15 patients with primary bone diffuse large B-cell lymphoma (PB-DLBCL) subgroup showed that their 2-year OS and PFS rates were 59.2% and 45.5% respectively. Univariate analysis indicated that the international prognostic index (IPI) score (≤2), no soft tissue invasion and CR in initial treatment could be treated as the favorable prognostic factors of both OS and PFS; Eastern Cooperative Oncology Group (ECOG) score (≤2) could improve OS; the addition of rituximab could improve PFS. Multivariate analysis indicated that the IPI score was the only independent factor of OS and soft tissue invasion was the only independent factor of PFS. Conclusions For PB-DLBCL patients, low IPI score (≤2) and local soft tissue without invasion indicate better prognosis, furthermore, a more appropriate IPI model should be explored based on local soft tissue invasion. Key words: Lymphoma, bone; Lymphoma, large B-cell, diffuse; Prognosis

Improved prognostic stratification using NCCN- and GELTAMO-international prognostic index in patients with diffuse large B-cell lymphoma.

The National Comprehensive Cancer Network (NCCN)-International Prognostic Index (IPI) and GELTAMO (Grupo Español de Linfomas/Trasplante Autólogo de Médula Ósea)-IPI were developed to enable better risk prediction of patients with diffuse large B-cell lymphoma (DLBCL). The present study compared the effectiveness of risk prediction between IPI, NCCN-IPI, and GELTAMO-IPI in patients with DLBCL particularly in terms of determining high-risk patients. Among 439 patients who were enrolled to a prospective DLBCL cohort treated with R-CHOP immunochemotherapy, risk groups were classified according to the three IPIs and the prognostic significance of individual IPI factors and IPI models were analyzed and compared. All three IPI effectively separated the analyzed patients into four risk groups according to overall survival (OS). Estimated 5-year OS of patients classified as high-risk according to the IPI was 45.7%, suggesting that the IPI is limited in the selection of patients who are expected to have a poor outcome. In contrast, the 5-year OS of patients stratified as high-risk according to NCCN- and GELTAMO-IPI was 31.4% and 21.9%, respectively. The results indicate that NCCN- and GELTAMO-IPI are better than the IPI in predicting patients with poor prognosis, suggesting the superiority of enhanced, next-generation IPIs for DLBCL.

Role of Immune Microenvironmental Factors for Improving the IPI-related Risk Stratification of Aggressive B Cell Lymphoma

ObjectiveTo investigate the risk stratification of aggressive B cell lymphoma using the immune microenvironment and clinical factors. MethodsA total of 127 patients with aggressive B cell lymphoma between 2014 and 2015 were enrolled in this study. CD4, Foxp3, CD8, CD68, CD163, PD-1, and PD-L1 expression levels were evaluated in paraffin-embedded lymphoma tissues to identify their roles in the risk stratification. Eleven factors were identified for further evaluation using analysis of variance, chi-square, and multinomial logistic regression analysis. ResultsSignificant differences in 11 factors (age, Ann Arbor stage, B symptom, ECOG performance status, infiltrating CD8+ T cells, PD-L1 expression, absolute blood monocyte count, serum lactate dehydrogenase, serum iron, serum albumin, and serum β2-microglobulin) were observed among patient groups stratified by at least two risk stratification methods [International Prognostic Index (IPI), revised IPI, and NCCN-IPI models] (P < 0.05). Concordance rates were high (81.4%-100.0%) when these factors were used for the risk stratification. No difference in the risk stratification results was observed with or without the Ann Arbor stage data. ConclusionWe developed a convenient and inexpensive tool for use in risk stratification of aggressive B cell lymphomas, although further studies on the role of immune microenvironmental factors are needed.

Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type

Objectives: The role of body mass index (BMI) in lymphoma survival outcomes is controversial. The prognostic significance of BMI in extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is unclear. We evaluated the prognostic role of BMI in patients with ENKTL.Methods: We retrospectively analyzed 742 patients with newly diagnosed ENKTL. The prognostic value of BMI was compared between patients with low BMIs (< 20.0 kg/m2) and patients with high BMIs (≥ 20.0 kg/m2). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) was also evaluated and compared with that of the BMI classification.Results: Patients with low BMIs tended to exhibit higher Eastern Cooperative Oncology Group performance status (ECOG PS) scores (≥ 2) (P = 0.001), more frequent B symptoms (P < 0.001), lower albumin levels (P < 0.001), higher KPI scores (P = 0.03), and lower rates of complete remission (P < 0.001) than patients with high BMIs, as well as inferior progression-free survival (PFS, P = 0.003), and inferior overall survival (OS, P = 0.001). Multivariate analysis demonstrated that age > 60 years, mass > 5 cm, stage III/IV, elevated LDH levels, albumin levels < 35 g/L and low BMIs were independent adverse predictors of OS. The BMI classification was found to be superior to the IPI with respect to predicting patient outcomes among low-risk patients and the KPI with respect to distinguishing between intermediate-low- and high-intermediate-risk patients.Conclusions: Higher BMI at the time of diagnosis is associated with improved overall survival in ENKTL. Using the BMI classification may improve the IPI and KPI prognostic models.

Prognostic Impact of Absolute Lymphocyte Count/Absolute Monocyte Count Ratio and Prognostic Score in Patients with Nasal Type, Extranodal Natural Killer/T Cell Lymphoma

Nasal type, extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is a heterogeneous disorder with poor prognosis, requiring risk stratification in this population. We designed to investigate the prognostic significance of absolute lymphocyte count (ALC), absolute monocyte count (AMC), ALC/AMC ratio and ALC/AMC prognostic score (low risk, normal ALC and AMC; intermediate risk, low ALC or high AMC; high risk, low ALC and high AMC) and to determine whether ALC/AMC ratio or prognostic score is a better predictor of prognosis in ENKTL. A total of 264 patients with newly diagnosed ENKTL were retrospectively analyzed in present study. Receiver Operating Curve analysis showed that optimal cut-off values of ALC, AMC and ALC/AMC ratio were 1.0×109/L, 0.5×109/L and 2.85, respectively. In multivariate analysis all tested factors including ALC, AMC, ALC/AMC ratio and prognostic score were independent risk factors. After a median follow-up of 30 months (range 1-87), an estimated 3-year overall survival (OS) in the 264 patients was 75.4%. Patients with ALC/AMC ratio≥2.85 had a better OS and progression-free survival (PFS) than those with ALC/AMC ratio<2.85 at diagnosis (3-year OS rate: 83.4% versus 61.9%, P < 0.001; 3-year PFS rate: 76.5% versus 53.0%, P< 0.001). Significant difference has been noticed in the patients according to ALC/AMC prognostic score in 3-year OS (low risk vs. intermediate risk, 88.3% vs. 66.5%, p = 0.001; low risk vs. high risk, 88.3% vs. 0%, p < 0.001; intermediate risk vs. high risk, 66.5% vs. 0%, p = 0.001) and in 3-year PFS(low risk vs. intermediate risk, 80.7% vs. 58.3%, p = 0.002; low risk vs. high risk, 80.7% vs. 0%, p < 0.001; intermediate risk vs. high risk, 58.3% vs. 0%, p = 0.003) . The International Prognostic Index (IPI) and Korean Prognostic Index (KPI) were used for predicting these patients’ prognosis, the result showed that the discrimination was not power. When applying ALC/AMC ratio and prognostic score to the IPI and KPI model, the latter showed a better discrimination as compared to the former. DisclosuresNo relevant conflicts of interest to declare.

A Novel and Significant Predictor in Extranodal Natural Killer/T-Cell Lymphoma, Nasal Type: The Prognostic Nutritional Index (PNI)

Background: Extranodal NK/T-cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis. The prognostic nutritional index (PNI) is reported to be associated with survival in several types of tumors. The prognostic value of PNI in lymphoma remains unclear. The aim of the present study is to evaluate the prognostic significance of PNI in patients with ENKTL.Methods: 157 patients with newly diagnosed ENKTL were retrospectively evaluated between August 2000 and October 2011 at the Sun Yat-sen University Cancer Center. Patients in whom the combined albumin (g /l) +5x total lymphocyte count x 109/l ≥45 were allocated a PNI score of 0. Patients in whom this total was < 45 were allocated a score of 1.Overall survival (OS) and progression free survival (PFS) were estimated using the Kaplan-Meier method. The significance of differences between survival curves was tested using the log-rank test. Significant variables in the univariate analysis were considered variables for the multivariate survival analysis, which was performed using Cox regression mode.Results: Four-nine patients (31.2%) had an abnormal PNI (PNI=1). The characteristics of both groups are given in Table 1.After a median follow-up duration of 31.0 months, an estimated 5-year OS and PFS rate in 157 patients was 58.4% and 39.4%, respectively. Patients with pretreatment PNI score=1 had lower complete remission rates (P= 0.018) and worse OS (5-year OS: 71.7% vs 21.8.0%,P<0.001) and PFS (P<0.001) compared with PNI score=0 patients. The survival curves according to PNI are shown in Fig. 1.Using the International Prognostic Index (IPI) and peripheral T-cell lymphoma (PIT) scoring systems, more than 70% of all cases were in the low-risk category (with no or one adverse factor), but these two prognostic models failed to differentiate between patients with different outcomes in the low-risk group. PNI could differentiate low-risk patients using IPI and PIT scoring (all P< 0.05).The Korean Prognostic Index (KPI) model balanced distribution of patients into different risk groups better than the IPI and PIT models. However, the KPI model also failed to significantly differentiate between patients with different outcomes in low and low intermediate risk group (P=0.859). PNI also helped to differentiate between patients with different prognosis in low and low intermediate risk group (P=0.000).However, the KPI prognostic model failed to show significant prognostic value among patients with PNI=0 (P=0.646) or among the patients with PNI=1 (P=0.115). The survival curves according to KPI and PNI are shown in Fig. 2.Conclusions: PNI is an independent predictor of survival in ENKTL and is superior to IPI, PIT and KPI.Table 1Baseline Characteristics of Patients by PNI LevelCharacteristicsPNI score =0PNI score =1P10849Age (median [range], yr)42(9-77)45(12-76)0.031<6096(88.9)37(75.5)>6012(11.1)12(24.5)Gender0.444Female33(30.6)18(36.7)Male75(69.4)31(63.3)ECOG PS<0.0010-1107(99.1)40(81.6)2-31(0.9)9(18.4)B symptoms (yes)45(41.7)36(73.5)<0.001LDH >245 U/L19(17.6)29(59.2)<0.001Mass ≥5 cm12(11.1)6(12.2)0.836Extranodal sites ≥230(27.8)27(55.1)0.001Regional LN involvement60(55.6)40(81.6)0.002Bone marrow involvement0(0.0)3(6.1)0.009Primary sites0.006Nasal/nasopharynx area93(86.1)33(67.3)Extra-nasal/nasopharynx15(13.9)16(32.7)Ann Arbor stage0.001I/II93(86.1)31(63.3)III/IV15(13.9)18(36.7)IPI score<0.0010-191 (84.3)24(49.0%)2-517(15.7)25(51.0%)PIT score<0.0010-1106(98.1)36(73.5)2-42(1.9)13(26.5)KPI score<0.0010-169(63.9)9(18.4)2-439(36.1)40(81.6) [Display omitted] [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Proposal of New Prognostic Index for Patients with Diffuse Large B-Cell Lymphoma in the Rituximab Era

BackgroundThe International Prognostic Index (IPI) has been useful prognostic tool to predict prognosis of aggressive non-Hodgkin lymphoma in the last 20 years. Since the advent of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) chemotherapy for diffuse large B-cell lymphoma (DLBCL), its utility has been challenged and other prognostic index including revised IPI and National Comprehensive Cancer Network (NCCN)-IPI were proposed, which are not popularly used yet. We aimed to develop new prognostic model for DLBCL in rituximab era.MethodBetween March 2004 and June 2012, patients with DLBCL treated with R-CHOP were identified in the database of the Asan Medical Center (AMC) Lymphoma Registry. Primary end point was to devise a new prognostic index for DLBCL. Secondary end point was to validate the NCCN-IPI in our cohort. We tested new prognostic index model in the training set of AMC cohort consisted of randomly selected 80% of the sample (503 patients). The remaining 20% (118 patients) was used as an internal validations set.ResultsThe AMC cohort consisted of 621 patients. Median follow-up duration was 43.3 months (6.2-122.5 months). Baseline characteristics of AMC cohort are presented in table 1. Median age was 57 years (range, 16-85 years). Median ϐ-2 microglobulin (ϐ-2 MG) was 2.10 mg/L (range, 1.0-66.0 mg/L). The univariate analysis of baseline characteristics revealed that age (≦60 vs. >60 years), LDH (within normal vs. increased), ECOG performance (0 or 1 vs. ≧2), advanced stage (Ann Arbor stage I/II vs. III/IV), extra-nodal involvement (≦1 vs. >1), B symptoms (no vs. yes), and ϐ-2 MG (≦2.5 vs. >2.5) could predict overall survival (OS), whereas bulky disease and gender did not (p value 0.140, 0.621, respectively). In the multivariate analysis, age, LDH, ECOG performance status, and ϐ-2 MG were significantly associated with OS (p value 0.001, <0.001, 0.004, and 0.019, respectively), while stage, extra-nodal involvement, and B symptom did not (p value 0.057, 0.233, and 0.577, respectively). We developed a new prognostic model with these 4 significant factors in the multivariate analysis. One point is assigned for each of the risk factors without refined categorization. Four risk groups were composed as followings: low (0 point), low-intermediate (1 point), high-intermediate (2-3 points), and high (4 points). The new prognostic model showed better discriminative power compared with classic IPI (Figure 1A). Five-year OS of low- and high-risk subgroup in new scoring model and classic IPI model in AMC cohort were 95% and 32% versus 89% and 45%, respectively. Our model was validated in an internal validation set (Figure 1B). NCCN-IPI also could stratify four risk groups (Figure 1 A and B).ConclusionWe propose a new prognostic index model for DLBCL in rituximab era with age, LDH, ECOG performance and ϐ-2 MG, which has good discriminative power and convenient to apply. It warrants further validation using an independent cohort.Table 1Baseline CharacteristicsCharacteristicsTotalN=621 %Training setN=503 %Validation setN=118 %Age, yearsMedian, range≦ 60 years> 60 years57.0 377 24416-85 60.7 39.357.0 300 20316-84 59.6 40.457.0 77 4117-85 65.3 34.7SexMaleFemale343 27855.2 44.8273 23054.3 45.770 4859.3 40.7ECOG PS0 or 1≧ 2569 5291.6 8.4462 4191.8 8.2107 1190.7 9.3Serum lactate dehydrogenase levelsNormalElevated334 28753.8 46.2279 22455.5 44.455 6346.6 53.4Ann Arbor stageI and IIIII and IV293 32847.2 52.8236 26746.9 53.157 6148.3 51.7Number of extranodal sites<2≧ 2403 21864.9 35.1329 17465.4 34.674 4462.7 37.3B symptomsNoYes549 7288.4 11.6447 5688.9 11.1102 1686.4 13.6International prognostic indexLow/low-intermediateHigh-intermediate/high404 21765.1 34.9327 17665.0 35.077 4165.3 34.7¥Â -2 microglobulin, mg/LMedian, range≦ 2.5 mg/L> 2.5 mg/L2.1 422 1991.0-66.0 68.0 32.02.1 339 1641.0-29.6 67.4 32.62.1 83 351.0-66.0 70.3 28.7Table 2Multivariate Analysis for Factors Associated with Overall SurvivalFactorsHR95% CIP valueScoreAge, years≦ 60 years> 60 years1.000 2.0511.362-3.0900.0011Serum lactate dehydrogenase levelsNormalElevated1.000 3.1651.951-5.135<0.0011ECOG PS0 or 1≧ 21.000 2.0731.261-3.4070.0041ϐ -2 microglobulin, mg/L≦ 2.5 mg/L> 2.5 mg/L1.000 1.6911.0391-2.6220.0191 [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Scoring System Using Age, C-Reactive Protein, Hemoglobin, and Albumin (ACHA) For Predicting Outcomes Of Diffuse Large B-Cell Lymphoma In The Rituximab-Era

IntroductionOverall survival (OS) of B-cell lymphoma patients, including diffuse large B-cell lymphoma (DLBCL), has markedly improved since the development of rituximab. Rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) have been established as the standard initial therapy for DLBCL patients over past 10 years. However, investigation of risk factors for new prediction models for DLBCL patients in the rituximab-era has been limited. Thus, the International Prognostic Index (IPI) has been used over past 20 years for risk stratification of lymphomas, and although it retains some usefulness, the revised IPI model cannot identify the poor prognosis group with 5-year OS of < 40%. This study aimed to verify risk factors for survival and responses to R-CHOP or R-CHOP-like therapy in DLBCL patients, and to construct a new prediction model to supplant the IPI model. MethodsA historical cohort study comprised patients with de novoDLBCL who received R-CHOP or R-CHOP-like therapy as initial therapy at our institutions between January 2002 and July 2013. A retrospective analysis was performed to identify the pre-therapy risk factors for survival and lymphoma progression. Transformed DLBCL and intravascular large B-cell lymphoma patients were excluded. ResultsStudy comprised 254 patients (145 males, 109 females). Median age at diagnosis was 70.0 years (range 20–96 years). Response rates in patients to first regimen of therapy were as follows: 80.7%, CR (n = 205); 9.1%, PR (n = 23); 1.2%, SD (n = 3); and 9.1%, PD (n = 23). Ann Arbor clinical stage was as follows: 42 (16%) patients. stage I; 60 (24%), stage II; 47 (18%), stage III; and 105 (41%), stage IV. B symptom was observed in 40 (16%) patients and a bulky mass in 38 (15%). The revised IPI indicated 23 (9%) very good, 88 (35%) good, and 143 (56%) poor risk cases. In 235 patients, we categorized 80 cases as germinal center B-cell-like (GCB) type and 155 cases as non-GCB type using Hans's algorithm of immunohistochemistry. Autologous peripheral blood stem cell transplantation was performed in 18 patients. Initial therapy that included radiation was performed in 29 patients. 5-year OS was 59.3% and 5-year progression-free survival (PFS) was 48.7%. According to the revised IPI, 5-year OS was stratified as 100%, very good; 69.2%, good; and 46.4%, poor (P< 0.001). Based on univariate analysis using Log-rank tests, significant poor prognostic factors for OS were as follows: B symptom positivity (P = 0.003), hemoglobin< 12.0 g/dL (P< 0.001), albumin < 4.0 g/dL (lower limit of normal; P< 0.001), C-reactive protein (CRP) > 0.6 mg/dL (P< 0.001), soluble IL-2 receptor (sIL-2R) > 2000 U/mL (P< 0.001), and non-GCB type (P = 0.03) along with all IPI factors including age ≥ 61 (P< 0.001), Ann Arbor clinical stage ≥ 3 (P = 0.005), lactate dehydrogenase (LDH) > 220 IU/L (upper limit of normal; P< 0.001), ECOG performance status ≥ 2 (P< 0.001), and number of extranodal disease sites ≥ 2 (P = 0.02). In multivariable analysis for 235 patients using Cox proportional hazards model including 11 previously noted factors, age (P = 0.003), CRP (P = 0.04), hemoglobin (P< 0.001), and albumin (P = 0.02) were significant. Step-down procedure in multivariable analysis also identified these 4 factors, age [hazard ratio (HR) 2.84, P = 0.002], CRP (HR 2.10, P = 0.003), hemoglobin (HR 2.36, P< 0.001), and albumin (HR 2.16, P = 0.008), as significant in 254 patients. These 4 factors were used to define the ACHA (Age-CRP-Hemoglobin-Albumin) score. ACHA scores stratified 5-year OS as 100%, 82.1%, 64.3%, 51.4%, and 22.3%, and 5-year PFS as 93.9%, 71.2%, 51.5%, 37.3%, and 14.6% as 0 (n = 34), 1 (n = 46), 2 (n = 48), 3 (n = 66), and 4 (n = 60) of the ACHA scores, respectively (OS, P< 0.001: PFS, P< 0.001). ConclusionsAlthough predictive power of this scoring system should be validated using another data set, ACHA scoring factors at diagnosis stratified expected survivals. ACHA was constructed without Ann Arbor staging; thus, computed tomography scanning and bone marrow examination were not required for risk stratification. Moreover, this predictive scoring system could extract poor prognosis patients with 5-year OS < 30% and patients highly likely to be resistant to R-CHOP or R-CHOP-like regimen with 5-year PFS < 20%. New therapy regimens that improve OS for such patients with very poor predicted prognosis are required. Disclosures:No relevant conflicts of interest to declare.

An Enhanced International Prognostic Index (IPI) for Patients with Diffuse Large B-Cell Lymphoma (DLBCL) in the Rituximab Era Using the National Comprehensive Cancer Network (NCCN) Database.

AbstractAbstract 2656▪▪This icon denotes a clinically relevant abstract Introduction:The International Prognostic Index (IPI) was first developed in 1993 to risk stratify patients with aggressive Non-Hodgkin's lymphoma (NHL) for outcome prediction (Shipp, NEJM, 1993). Since the addition of rituximab to conventional CHOP chemotherapy for the treatment of DLBCL, there have been many efforts to validate the IPI as well as to improve upon the model's capacity to distinguish subgroups with discrete clinical outcomes, especially high-risk patients. Previous studies have focused on adding clinical or biologic prognostic factor(s) to the original model or regrouping the original IPI score (R-IPI; Sehn, Blood, 2007). We, therefore, built anew a modern IPI based solely on clinical factors available in the real world NCCN clinical database. Methods:Using the nationwide population-based NHL lymphoma database from NCCN, patients with newly diagnosed DLBCL enrolled between June 2000 and Dec. 2010 at 7 NCCN cancer centers were included with at least 1 year and up to 5 years of follow-up. Clinical characteristics including age, Ann Arbor stage, ECOG performance status, disease in extranodal sites (including positivity in bone marrow, CNS, liver/GI tract, lung, other sites and spleen), LDH, presence of bulky disease (>10 cm) as well as B symptoms were studied as potential predictors for model development using COX proportional hazards regression. IPI scores were assigned proportionally based on parameter estimates of the statistically significant predictors in the final COX model. Model selection and its comparison to the original IPI model were made based on Akaike Criteria (AIC) and the likelihood ratio test. Categorization of age and LDH was decided by testing the linearity assumption and Martingale residuals. Kaplan-Meier curves were plotted for stratified risk groups per the new and original IPI. Finally, both IPI models were compared using the initial randomly selected 15% validation sample. Results:There were 1,650 DLBCL patients with complete clinical information included for model development. The new IPI model consisted of similar component predictors but used a maximum of 8 scoring points by further categorizing age group into >40–60 (score of 1), >60–75 (score of 2) and >75 yrs (score of 3), and normalized LDH between >1–3 times (score of 1) and 33 times (score of 2) upper limit of normal. These categorizations minimized the Martingale residuals. Age effect was linear and 20-year increments fit the model best, whereas the effect of normalized LDH was not linear and reached plateau at a ratio of 3. Lymphomatous involvement either of bone marrow, CNS, Liver/GI tract or lung appeared as a stronger predictor (p<0.001) than number of extranodal sites (p=0.91). Four risk groups (Low, Low-intermediate, High-intermediate and High) were identified using the current IPI (Table 1) with enhanced discrimination power when compared with the original IPI and better global model fitting statistics, i.e. smaller AIC and significant likelihood ratio test (p<0.001). It was possible to identify a high risk group (score 3 6) with 5-year overall survival of 33% (95% CI: 22%–45%). Better model prediction was also shown in the validation sample. Conclusions:We were able to develop an enhanced IPI model for clinical prediction among previously untreated DLBCL cases by using patient level data from the NCCN NHL database. The NCCN-IPI demonstrates better risk stratification and identifies a poor risk subgroup with <50% 5-year overall survival in the current real-world clinical setting as compared to the original IPI model developed for aggressive lymphoma prior to the rituximab era.Table 1An Enhanced IPI Score and Comparison of IPI Models Using Current NCCN DLBCL DataNCCN Prognostic Index (NCCN IPI) for DLBCLScore*Complete Response5-y PFS5-y OS0–1 (19%)**93%91%96%2–3 (42%)88%74%82%4–5 (31%)75%51%64%3 6 (8%)65%30%33%Original International Prognostic Index (IPI) for Aggressive NHL0–1 (38%)91%85%90%2 (26%)82%66%77%3 (22%)75%52%62%4–5 (14%)73%39%54%* Calculation of Score:Factors (NCCN IPI)ScoreAge> 40 to £ 601> 60 to £ 752> 753LDH, normalized†>1 to £ 31> 32Stage III/IV1Extranodal Site††1Performance status 3 21†LDH value divided by institutional upper limit of normal††Extranodal sites in either bone marrow, CNS, liver/GI or lung**Percent of cohort [Display omitted] [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Low Serum Total Protein and Unfavorable Pathological Subtype Are Independent Unfavorable Factors in Peripheral T-Cell Lymphoma (PTCL) Treated in Prospective Clinical Tirals; Japan Clinical Oncology Group (JCOG) 0108A Study.

JCOG consecutively conducted 6 multicenter clinical trials for advanced aggressive lymphoma in 1990s, including 4 CHOP-based chemotherapies (JCOG9505, 9506, 9508 and 9809) and 2 second or third generation, multidrug combination chemotherapies (JCOG9002 and 9203). Among the whole 1,141 enrolled patients (pts), histopathological specimens from 1,084 pts (95%) were reviewed by 6 hematopathologists and diagnosed according to WHO classification. Factors affecting the overall survival (OS) of pts with each B- or T-cell lymphoma were analyzed. In all the 6 trials, major eligibility criteria were as follows: age between 15 and 69 except one trial for elderly pts, pathological subtypes of intermediate- or high-grade lymphoma by Working Formulation excluding adult T-cell leukemia/lymphoma, lymphoblastic lymphoma, and cutaneous T-cell lymphoma. ECOG performance status 4 was excluded. Numbers of the cases of major B-cell lymphoma subtypes were 642 diffuse large B-cell lymphoma(DLBCL), 104 follicular lymphoma(FL), 30 mantle cell lymphoma(MCL) and 24 marginal zone B-cell lymphoma(MZL). One hundred and thirty six cases of PTCL were analyzed, including 18 anaplastic large cell lymphoma (ALCL), 46 angioimmunoblastic T-cell lymphoma (AILT), 53 PTCL unspecified (PTCL-U), 17 extranodal NK/T-cell lymphoma, nasal type (NK/T). Five year OS rates of major subtypes were; 83% for MZL, 76% for FL, 59% for DLBCL, 59% for MCL, 61% for ALCL, 55% for AILT, 41% for NK/T and 38% for PTCL-U. In the whole B-cell lymphomas, 5 year OS rates were discriminated well by International Prognostic Index (IPI); i.e. low (L): 75.0%, low-int (LI): 60.3%, high-int (HI): 45.3%, high(H): 25.7%. On the contrary, those of PTCL could not be discriminated well according to IPI, i.e. L: 63.6% LI: 45.1%, HI: 30.8%, and H: 47.4%. Other factors influencing the OS of pts with PTCL were clarified by the univariate or multivariate analysis. In the univariate analysis of 136 PTCL cases, 4 factors related to unfavorable prognosis were identified as follows: low serum total protein level (TP<6.3g/dl) [hazard ratio (HR) 2.39, (95%CI: 1.50–3.80, p=0.0003)], involvement of gastrointestinal tract [HR 1.81 (95%CI: 1.00–3.28, p=0.049)], low serum albumin level (<3.7g/dl) [HR 1.68(95%CI: 1.08–2.61, p=0.02)], and pathological subtype (ALCL+AILT vs. PTCL-U+NK/T) [HR 0.59 (95%CI:0.38–0.90, p=0.02)]. In multivariate analysis, low TP(<6.3g/dl) [HR 2.20(95%CI:1.27–3.80, p=0.0048)] and pathological subtype (ALCL,AILT vs. PTCL-U,NK/T) [HR 0.59(95%CI:0.36–0.95, p=0.028) ] were significant. These results indicated that pts with PTCL showed worse OS than those with B-cell lymphoma. In B-cell lymphoma, IPI was confirmed as the important prognostic model, however, in PTCL, the significance of IPI is limited by other significant factors, i.e. low TP (< 6.3 g/dl) and unfavorable pathological subtype (PTCL-U and NK/T). In conclusion, in prospective multicenter trials settings, IPI model for aggressive B-cell lymphoma is not suitable for PTCL, and further studies on establishing novel prognostic models for PTCL are warranted.