A New Prognostic Model for Extranodal Natural Killer/T Cell Lymphoma, Nasal Type

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1769-1769
Author(s):  
Qingqing Cai ◽  
Xiaolin Luo ◽  
Ken H. Young ◽  
Huiqiang Huang ◽  
Guanrong Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Prognostic Index ◽  
Roc Curves ◽  
Risk Groups ◽  
Survival Outcomes ◽  
Nasal Type

Abstract Background Extranodal natural killer (NK)/T–cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis. A better risk stratification is beneficial for clinical management in affected patients. Our recent study has shown that fasting blood glucose (FBG) was a novel, prognostic factor, (Cai et al, British Journal of Cancer, 108: 380–386,2013). This finding has not been integrated in the previous prognostic models for ENKTL Therefore, we aimed to design a new prognostic model, including FBG, for ENKTL which supports to identify high–risk patients eligible for advanced or more aggressive therapy. Patients and methods 158 newly diagnosed patients with ENKTL were analyzed between January 2003 and January 2011 at Sun Yat–sen University Cancer Center, China. Overall survival (OS) and progression free survival (PFS) were estimated using the Kaplan–Meier method. The significance of differences between survival was tested using the Log–rank test. Significant variables in the univariate analysis were selected as variables for the multivariate analysis of survival. The latter was performed by the Cox regression mode. We constructed receiver operating characteristic (ROC) curves and compared the areas under the ROC curves of total protein (TP), FBG, Korean Prognostic Index (KPI) and their combinations in comparison to the survival outcome. Results Of 158 patients, 156 patients had complete clinical information for the parameters of the International Prognostic Index (IPI) model and KPI model. The estimated 5–year overall survival rate in 158 patients was 59.2%. Independent prognostic factors included TP < 60 g/L, FBG > 100 mg/dL, KPI score ≥ 2. A new prognostic model was constructed by combining these prognostic factors: Group 1 (64 cases, 41.0%), no adverse factors; Group 2 (58 cases, 37.2%), one adverse factor; and Group 3 (34 cases, 21.8%), two or three adverse factors. The 5–year overall survival of these groups were 88.9%, 35.6% and 12.7%, respectively (p < 0.001). The survival curves according to the new prognostic model are shown in Fig. 1. The new model categorized three groups with significantly different survival outcomes. The new prognostic model was also efficient in discriminating the patients with low to low–intermediate risk IPI group and high–intermediate to high risk IPI group into three subgroups with different survival outcomes (p < 0.001). The KPI model balanced the distribution of patients into different risk groups better than IPI prognostic model (score 0: 12 cases, 7.7%; score 1: 38 cases, 24.4%; score 2: 42 cases, 26.9%; score 3–4: 64 cases, 41.0%), and it was able to differentiate patients with different survival outcomes (p < 0.001). In addition, the new prognostic model had a better prognostic value than did KPI model alone (p < 0.001), suggesting that TP and FBG reinforced the prognostic ability of KPI model (Table 1). Conclusions The new prognostic model we proposed for ENKTL, including the new prognostic indicator total protein and FBG, demonstrated balanced distribution of patients into different risk groups with better prognostic discrimination as compared to KPI model alone. Disclosures: No relevant conflicts of interest to declare.

Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer (mCRPC) from the phase 3 prevail clinical trial.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 5022-5022
Author(s):  
Andrew J. Armstrong ◽  
Ping Lin ◽  
Celestia S. Higano ◽  
Cora N. Sternberg ◽  
Guru Sonpavde ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Groups ◽  
Multivariable Model ◽  
Training Set ◽  
Phase 3 ◽  
Testing Set

5022 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naive men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naive men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times. Clinical trial information: NCT01212991.

Development and validation of a prognostic model for overall survival in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (mCRPC) from the phase III PREVAIL clinical trial.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 138-138
Author(s):  
Andrew J. Armstrong ◽  
Ping Lin ◽  
Celestia S. Higano ◽  
Cora N. Sternberg ◽  
Guru Sonpavde ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Prognostic Model ◽  
Specific Antigen ◽  
Risk Groups ◽  
Phase Iii ◽  
Multivariable Model ◽  
Training Set ◽  
Testing Set

138 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naïve men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naïve men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times.

scholarly journals Development of a Novel Clinical Prognostic Model for Patients With Angioimmunoblastic T-Cell Lymphoma

2020 ◽  
Vol 19 ◽  
pp. 153303382096423
Author(s):  
Chen Huang ◽  
Huichao Zhang ◽  
Yuhuan Gao ◽  
Lanping Diao ◽  
Lihong Liu
Keyword(s):  
Prognostic Factors ◽  
T Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Prognostic Index ◽  
Risk Groups ◽  
T Cell Lymphoma ◽  
Coombs Test ◽  
Ki 67 ◽  
Angioimmunoblastic T Cell Lymphoma

In this study we aimed to identify a set of prognostic factors for angioimmunoblastic T-cell lymphoma (AITL) and establish a novel prognostic model. The clinical data of 64 AITL patients enrolled to the Fourth Hospital of Hebei Medical University (from 2012 Jan to 2017 May) were retrospectively analyzed. The estimated 5-year overall survival and progression-free survival of this cohort of patients were 45.8% and 30.8%, respectively. Univariate analysis showed that age > 60 years, performance status ≥2, Ann Arbor stage III/IV, lactate dehydrogenase > 250 U/L, serum albumin (ALB) < 30 g/l, Coombs test positive, and Ki-67 rate ≥ 70% were significantly associated with poor prognosis. Multivariate analysis demonstrated that age > 60 years, ALB < 30 g/l, Ki-67 rate ≥ 70%, and Coombs test positive were independent prognosis factors for AITL. Here a new prognostic model, named as AITLI, was constructed using the top 5 significant prognostic factors for AITL prognostic prediction. The AITL patients were stratified into 3 risk groups: low, intermediate, and high risk groups. The new prognostic model AITLI showed better performance in predicting prognosis than the International Prognostic Index (IPI) and the prognostic index for PTCL, not otherwise specified (PIT) that were wisely used to predict the outcome for patients with other subtypes of lymphoma.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Mantle Cell Lymphoma: Prognostic Capacity of the Follicular Lymphoma International Prognostic Index.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1910-1910
Author(s):  
Michael B. Moller ◽  
Niels T. Pedersen ◽  
Bjarne E. Christensen
Keyword(s):  
Overall Survival ◽  
Multivariate Analysis ◽  
Mantle Cell Lymphoma ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
Risk Groups ◽  
High Risk Group ◽  
Predictive Capacity ◽  
Mantle Cell

Abstract Background: The International Prognostic Index (IPI) is the most commonly used prognostic model in mantle cell lymphoma. However, the prognostic value of IPI is limited. The recently published Follicular Lymphoma International Prognostic Index (FLIPI) is built on variables (age, stage, lactic dehydrogenase, anemia, and nodal disease) which also are pertinent to mantle cell lymphoma. This study was conducted to evaluate the prognostic value of FLIPI in patients with mantle cell lymphoma. Patients and Methods: A population-based series of 93 patients with mantle cell lymphoma diagnosed in a 7-year period were studied. End points of the study were response to therapy, overall survival, and failure-free survival according to IPI and FLIPI. Results: Applied to the whole series, FLIPI identified 3 risk groups with markedly different outcome with 5-year overall survival rates of 65%, 42%, and 8%, respectively (P < .0001; log-rank 28.13; figure below). Notably, the high-risk group comprised 53% of patients. In contrast, IPI only allocated 16% of cases to the high-risk group and had a lower overall predictive capacity (log-rank 24.8). When both FLIPI and IPI were included in a multivariate analysis, only FLIPI was related to survival. In patients treated with CHOP-based regimens (n = 45) FLIPI also had superior predictive capacity compared to IPI (log-rank, 18.51 versus 11.37), and again only FLIPI retained significance in multivariate analysis. Multivariate analysis of failure-free survival also identified FLIPI, and not IPI, as independently significant. Conclusion: FLIPI is the superior prognostic model as compared to IPI and should be the preferred clinical prognostic index in mantle cell lymphoma. Overall survival according to FLIPI risk groups Overall survival according to FLIPI risk groups

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.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2656-2656
Author(s):  
Zheng Zhou ◽  
Alfred W. Rademaker ◽  
Leo I. Gordon ◽  
Ann S. LaCasce ◽  
Ann Vanderplas ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Overall Survival ◽  
High Risk ◽  
International Prognostic Index ◽  
Model Development ◽  
Prognostic Index ◽  
Risk Groups ◽  
Ratio Test ◽  
Validation Sample ◽  
Gi Tract

Abstract Abstract 2656 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. Disclosures: No relevant conflicts of interest to declare.

Treatment Outcome and Prognostic Model for Stage IE/IIE Extranodal Natural Killer/T-Cell Lymphoma, Nasal Type: A Retrospective Study in a Cohort of 305 Patients with Long Term Follow-up

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2718-2718
Author(s):  
Yuankai Shi ◽  
Bo Jia ◽  
Xiaohui He ◽  
Youwu Shi ◽  
Mei Dong ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
T Cell ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Natural Killer T Cell ◽  
Combined Chemoradiotherapy ◽  
Radiotherapy Alone ◽  
Killer T Cell

Abstract Background Extranodal natural killer/T-cell lymphoma, nasal type (ENKL) is a rare and distinct subtype of non-hodgkin lymphoma (NHL). The frequency was higher in Asia than in western countries and it has become the most common subtype of peripheral T-cell lymphomas in China. The majority of ENKL patients present with early stage. Optimal treatment modalities and prognostic factors for localized ENKL have not been fully defined. This study aimed to evaluate the optimal treatment strategy and prognostic factors for localized ENKL patients. Methods Between 2003 and 2013, three hundred and five patients with stage IE/IIE ENKL were comprehensively analyzed in this study. A total of 180 patients received combined chemoradiotherapy, with 111 patients received radiotherapy alone and 14 patients recieved chemotherapy alone. Chemotherapy regimens include GDP (gemcitabine, cisplatin, and dexamethasone), CHOP (epirubicin, cyclophosphamide, vincristine, and prednisolone) and other regimens. A total dose of 50 Gy to the primary tumor was considered as radical dose for ENKL, and additional 5 to 10 Gy was administered as a boost to the residual disease. Results The complete response (CR) rate for patients received chemoradiotherapy (n=175) was significantly higher than that for patients received radiotherapy alone (n=102) (89.1 % vs.77.5 %, P = 0.009) or chemotherapy alone (n=14) (89.1 % vs.21.4 %, P< 0.001). The median follow up time for all 305 patients was 38.7 (1.1 to 393) months. For 228 stage IE paranasal extension or IIE patients, 3-year overall survival (OS) in combined chemoradiotherapy (n=154), radiotherapy alone (n=60) and chemotherapy alone (n=14) groups were 85.7%, 73.3% and 57.1% respectively (chemoradiotherapy vs. radiotherapy, P=0.003; chemoradiotherapy vs. chemotherapy, P<0.001). For patients received combined chemoradiotherapy, GDP regimen (n=54) (included 10 patients with pegaspargase) could significantly improve 3-year progression-free survival (PFS) compared with CHOP-like (n=110) (included 10 patients with asparaginase) (88.9% vs. 70.9%, P =0.015).Patients received radiotherapy first followed by chemotherapy (n=84) was associated with superior 3-year PFS compared with patients initially received chemotherapy (n=96) (81.0% vs. 69.8%, P=0.034). But for 54 patients received GDP regimen, induction chemotherapy (n=17) could increase 3-year PFS (100.0% vs. 83.8%, P=0.112) and OS (100.0% vs. 86.5%, P=0.180). We identified 3 risk groups based on 3 prognostic factors (stage II, LDH elevated and paranasal extension) with different survival outcomes. The 3-year OS rates were 93.5%, 85.0% and 62.2% respectively for patients with no risk factors, 1 or 2 factors and 3 factors (P<0.001). Conclusions Combined chemoradiotherapy is the most optimal therapy strategy for stage IE paranasal extension or IIE ENKL patients. GDP or combined with pegaspargase regimen shows promising efficacy, significant superior to the traditional CHOP regimen. The sequence of chemotherapy and radiotherapy for patients received novel chemotherapy regimens still needs further assessment in phase 3 clinical trials. We identified 3 risk groups based on 3 prognostic factors (stage II, LDH elevated and paranasal extension) with different survival outcomes and this novel prognostic model may better predict prognosis than previous International Prognostic Index (IPI) and Korean Prognostic Index (KPI) score for ENKL patients with limited stage. Disclosures No relevant conflicts of interest to declare.

Red Cell Distribution Width (RDW) at Diagnosis of Diffuse Large B-Cell Lymphoma (DLBCL) Is Associated to Higher Mortality and Worse Overall Survival

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5398-5398
Author(s):  
Leyre Bento ◽  
Juan Sarmentero ◽  
Ana Ortuño ◽  
Marta García-Recio ◽  
Bernardo Lopez ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Roc Curves ◽  
Red Cell Distribution Width ◽  
Red Cell ◽  
Cell Distribution ◽  
Distribution Width ◽  
Ecog Ps

Abstract Red cell distribution width (RDW) is an indicator of the variability in the size of circulating erythrocytes (anisocytosis); different conditions can increase the RDW levels; such as hemolysis, ineffective erythropoiesis and blood transfusions. Recently, different studies have shown an association between increased levels of RDW and inflammation in different diseases, being proposed as a surrogate marker of inflammation and a strong predictor of adverse outcome. The proposed mechanism of this association departs from the finding that Inflammatory cytokines like TNFand IL-6 (part of the classic inflammatory cascade), have been found to inhibit erythropoietin-induced erythrocyte maturation, which is reflected in the RDW increase. Some reports have found a relationship between RDW and mortality related to age or several malignant or non-malignant conditions. However, there is no information about the role of RDW in overall survival (OS) of patients with DLBCL. We aim to evaluate the prognostic role of RDW levels in DLBCL patients at diagnosis. METHODS We retrospectively evaluated 83 patients with DLBCL homogenously treated in frontline with R-CHOP from 2002 to 2013 in the Son Espases University Hospital. To avoid selection bias patients were obtained from Pharmacy and Pathology Departments registries. Main clinical and prognostic factors at diagnosis were obtained from medical records. Cheson criteria were used for response assessment. The RDW was collected from the hemogram at diagnosis. The IBM SPSS STADISTICS program was used for all statistical analyses. PFS (time to progression/relapse) and overall survival (OS) (time to death) were measured from the date of ABVD onset, and were estimated according to the Kaplan-Meier method. We performed the comparisons between those interest variables with the log-rank test. A comparison between categorical variables was made with the chi-square of Fisher's exact test, as appropriate. All reported P-values were two-sided, and statistical significance was defined at P<0.05. For selecting cutoff values in RDW we used ROC curves. RESULTS: Main characteristics of patients were as follows: median age was 62 (20-86) years, 24% had ECOG PS>1, 64% advanced III-IV Ann Arbor (AA) stage, 39% B-symptoms, 51% adjusted-International Prognostic Index (a-IPI) and 39% belong to the high risk (3-5) subgroups of R-IPI Median RDW was 14.6 (11.1-21.1). Using ROC curves we selected the cutoff 14.05 for the death event. We evaluated the association of increased RDW with main prognostic factors at diagnosis. RDW >14.05 at diagnosis was associated with a more advanced age, worse ECOG PS, a more advanced AA stage, higher incidence of B symptoms and IPI>2. However, RDW was not related to disease control in terms of response to therapy (p=0.39) or relapse/progression (p=0.21) rates. Inversely, RDW>14.05 was in fact associated to a higher mortality (47%) compared to only 17% in patients with RDW≤14.05 (p=0.008). Median follow-up was 77 (20-137) months. Univariate survival analysis showed age>60 years (p=0.001), ECOG PS>1 (p=0.036), high risk R-IPI (p=0.005), a higher than 15% reduction in relative dose-intensity (RDI) (p=0.026) and RDW>14.05 (p=0.008) were significantly related to worse OS. By contrast, RDW did not significantly influence progression-free survival (p=0.19). CONCLUSIONS: Higher RDW at diagnosis in this series of DLBCL patients was related with older age, worse ECOG PS and more advanced disease but this was not translated into a worse control of disease in terms of only a small non statistically significant impact in response or PFS. By contrast higher RDW was linked to a significantly higher mortality and worse OS possibly related to a higher proinflammatory basal status and comorbidities. Patients with higher RDW may be at risk of reduction in RDI. These findings could justify including RDW in scores of comorbidities in DLBCL as well as in other malignant and non-malignant conditions. Disclosures No relevant conflicts of interest to declare.

MicroRNA Signature Obtained From the Comparison of Aggressive With Indolent Non-Hodgkin Lymphomas: Potential Prognostic Value in Mantle-Cell Lymphoma

2013 ◽  
Vol 31 (23) ◽  
pp. 2903-2911 ◽  
Author(s):  
Rashmi S. Goswami ◽  
Eshetu G. Atenafu ◽  
Yali Xuan ◽  
Levi Waldron ◽  
Patricia P. Reis ◽  
...  
Keyword(s):  
Overall Survival ◽  
Mantle Cell Lymphoma ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
Differentially Expressed ◽  
Training Set ◽  
Ki 67 ◽  
Mantle Cell

Purpose Mantle-cell lymphoma (MCL) has a variable natural history but is incurable with current therapies. MicroRNAs (miRs) are useful in prognostic assessment of cancer. We determined an miR signature defining aggressiveness in B-cell non-Hodgkin lymphomas (NHL) and assessed whether this signature aids in MCL prognosis. Methods We assessed miR expression in a training set of 43 NHL cases. The miR signature was validated in 44 additional cases and examined on a training set of 119 MCL cases from four institutions in Canada. miRs significantly associated with overall survival were examined in an independent cohort of 114 MCL cases to determine association with patient outcome. miR expression was combined with current clinical prognostic factors to develop an enhanced prognostic model in patients with MCL. Results Fourteen miRs were differentially expressed between aggressive and indolent NHL; 11 of 14 were validated in an independent set of NHL (excluding MCL). miR-127-3p and miR-615-3p were significantly associated with overall survival in the MCL training set. Their expression was validated in an independent MCL patient set. In comparison with Ki-67, expression of these miRs was more significantly associated with overall survival among patients with MCL. miR-127-3p was combined with Ki-67 to create a new prognostic model for MCL. A similar model was created with miR-615-3p and Mantle Cell Lymphoma International Prognostic Index scores. Conclusion Eleven miRs are differentially expressed between aggressive and indolent NHL. Two novel miRs were associated with overall survival in MCL and were combined with clinical prognostic models to generate novel prognostic data for patients with MCL.

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