Identifying patients with non-small cell lung cancer (NSCLC) unlikely to benefit from erlotinib: An exploratory analysis of National Cancer of Institute of Canada Clinical Trials Group BR.21

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 7161-7161 ◽  
Author(s):  
M. Florescu ◽  
B. Hasan ◽  
F. A. Shepherd ◽  
L. Seymour ◽  
K. Ding ◽  
...  
Keyword(s):  
Weight Loss ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Index ◽  
Early Death ◽  
Risk Groups ◽  
Smoking History ◽  
Factors Associated ◽  
Significant Survival ◽  
High Benefit

7161 Background: Despite a 9% response rate, BR.21 demonstrated significant survival benefit for patients receiving erlotinib as 2nd/3rd line therapy for NSCLC. We undertook to characterize, by exploratory subset analysis, patients less likely to benefit from erlotinib. To identify factors for consideration, we first identified baseline characteristics associated with early progression by eight wks and early death by 3 mos. Methods: Using stratification factors and potential prognostic factors from BR.21, the Cox regression model with stepwise selection was used to establish a prognostic model to separate erlotinib patients into 4 risk categories based on the 10th, 50th & 90th percentiles of prognostic index scores. 7 variables (smoking history, PS, weight loss, anemia, high LDH, response to prior chemo and time from diagnosis to randomization) were used in the final model. The hypothesis was that the characteristics of the treated patients in the highest risk group would also be predictive of lack of benefit from erlotinib when erlotinib and placebo patients with the same characteristics were compared. Results: Factors associated with PD by 8 wks were: PS2–3 (p = 0.009), weight loss (p = 0.0004), anemia (p = 0.008), PD to prior chemo (p = 0.006), non-Asian (p = 0.047), EGFR IHC-negative (p = 0.005), Factors associated with survival < 3 mos were: PS2–3 (p < 0.0001), weight loss (p < 0.0001), anemia (p < 0.0001), PD to prior chemo (p < 0.0001), non-Asian (p = 0.008), high LDH (p < 0.0001), time to randomization <12 mos (p = 0.0003). Comparison of overall survival for the 4 risk groups derived from prognostic index score as follows: high benefit (HR = 0.41, p = 0.007), 2 intermediate benefit (HR 0.79, p = 0.09; HR 0.80; p = 0.09); no benefit (HR 1.23; p = 0.42). Median survivals for erlotinib (placebo) patients in each group were 17.3 (8.3), 9.7 (7.5), 4.1 (3.7), 1.9 (2.7) mos. Conclusions: By establishing a prognostic model, we identified a small group of patients who are unlikely to benefit from 2nd/3rd line erlotinib therapy. This model requires prospective validation to confirm that it is both prognostic and predictive of outcome from treatment. [Table: see text]

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.

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.

5-factor prognostic model for survival of patients with metastatic urothelial carcinoma receiving three different post-platinum PD-L1 inhibitors.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4552-4552 ◽  
Author(s):  
Guru Sonpavde ◽  
Juliane Manitz ◽  
Chen Gao ◽  
Daniel Hennessy ◽  
Doris Makari ◽  
...  
Keyword(s):  
Phase I ◽  
Urothelial Carcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Validation Dataset ◽  
Neutrophil Lymphocyte Ratio ◽  
Metastatic Urothelial Carcinoma ◽  
Discovery Dataset ◽  
Ecog Ps

4552 Background: A prognostic model for overall survival (OS) of metastatic urothelial carcinoma (mUC) was previously reported in the setting of post-platinum atezolizumab (Pond GR, GU ASCO 2018). This model was limited by employing only atezolizumab treated patients (pts), small size of the validation dataset and unclear applicability to other PD-1/L1 inhibitors. Hence, we constructed a robust prognostic model utilizing the combined atezolizumab cohort as the discovery dataset and used 2 separate validation datasets comprised of post-platinum avelumab or durvalumab treated pts. Methods: The discovery dataset consisted of pt level data from 2 phase I/II trials (IMvigor210 and PCD4989g) evaluating atezolizumab (n = 405). Pts enrolled on 2 separate phase I/II trials, EMR 100070-001 that evaluated post-platinum avelumab (n = 242) and CD1108 that evaluated durvalumab (n = 189) comprised the validation datasets. Cox regression analyses evaluated the association of candidate prognostic factors with OS. Factors were dichotomized and laboratory values were normalized by logarithmic transformation. Stepwise selection was employed to propose an optimal model using the discovery dataset. Discrimination and calibration were assessed in the avelumab and durvalumab datasets following the validation procedure by Royston and Altman (2013). Results: The 5 factors included in the optimal prognostic model in the discovery dataset were ECOG-PS (1 vs. 0; HR 1.80; 95% CI [1.36-2.36]), presence/absence of liver metastasis (HR 1.55; 95% CI [1.20-2.00]), number of platelets (HR 2.22; 95% CI [1.54-3.18]), neutrophil-lymphocyte ratio (NLR; HR 1.94; 95% CI [1.57-2.40]) and lactate dehydrogenase (LDH; HR 1.60; 95% CI [1.28-1.99]). There was robust discrimination of survival between low, intermediate and high-risk groups based on 0-1, 2-3 and 4 factors. The concordance of survival was 0.692 in the discovery and 0.671 and 0.775 in the avelumab and durvalumab validation datasets, respectively. Acceptable or good calibration of expected 1-year survival rate was observed. Conclusions: A 5-factor prognostic model is prognostic for survival across 3 different PD-L1 inhibitors (atezolizumab, avelumab, durvalumab) in this large study totaling 836 pts overall in the setting of post-platinum therapy for mUC. This model may assist in prognostic stratification and interpreting nonrandomized trials of post-platinum PD1/L1 inhibitors.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

Development and validation of prognostic models in patients with metastatic renal cell carcinoma (mRCC) treated with sunitinib: A Greek-French collaboration.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15040-e15040
Author(s):  
Vasilios Karavasilis ◽  
Kimon Tzanis ◽  
Christina Bamia ◽  
Reza-Thierry Elaidi ◽  
Efthimios Kostouros ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
Cox Regression ◽  
External Validation ◽  
Prognostic Index ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Models ◽  
Clinical Factors ◽  
Complicated Model

e15040 Background: The use of tyrosine kinase inhibitors (TKIs) in mRCC has improved prognosis but the individual outcome remains largely unpredictable. The MSKCC model, used to identify risk groups, was developed in cytokine-treated patients and has not been externally validated in the TKI era. It contains 3 laboratory factors (total 5), making its application to retrospective series somewhat problematic. Subsequently, a more complicated model, using 4 laboratory factors (total 6) has been described. The Hellenic Cooperative Oncology Group recently described a simpler model with only 3 clinical factors. We are describing the application and external validation of this model. Methods: 128 Greek patients with mRCC treated with 1st line sunitinib were included. All had had nephrectomy. Previous interferon was allowed. Cox regression was used to develop a predictive model for overall survival (OS). Our model was compared to that of MSKCC and Heng’s using ROC curves and Harrell’s Concordance Index. Risk groups were defined by the calculated prognostic index and by clinical factors. External validation was done using a sample of 226 French patients. The Royston and Sauerbrei D statistic was used as a measure of discrimination of the survival model. Results: Time from diagnosis of RCC to start of sunitinib (<12), PS (>1) and number of metastatic sites (>1) were independent adverse prognostic factors in the Greek dataset. The co-efficients for each factor were: 0.51, 0.97, 0.61, respectively. The 3 risk groups were defined by the 25th and 75th percentiles of the prognostic index values (Table 1). The model was of equal prognostic value to the MSKCC (p=.272) and Heng’s (p=.075). French had better survival than Greek patients especially in the high risk group (for all models). Validation of our model in the French data showed that it was applicable (R2 D: 0.14, SE: 0.09), especially for the low/medium risk groups. Conclusions: Our model is the only one externally validated in TKI-treated patients. It may be considered as a simpler alternative to those currently applied. [Table: see text]

Poor-prognosis nasopharyngeal carcinoma as defined by a molecularly distinct subgroup and prediction by a miRNA expression signature.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6056-6056
Author(s):  
Lan Zhao ◽  
Feng Gao ◽  
Wang Wei ◽  
Xin Duan ◽  
Yuchen Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Outcomes ◽  
Distant Metastasis ◽  
Mirna Expression ◽  
Poor Prognosis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Training Cohort

6056 Background: Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. Our aim in this study is to dissect the molecular heterogeneity of NPC, followed by construction of a prognostic model for prediction of distant metastasis. Methods: For molecular subtyping of NPC using miRNA expression data, we selected 86 stage II (AJCC 7th Edition) NPC patients from GSE32960 as training cohort. The remaining 226 NPC patients from GSE32960 and 246 NPC patients from GSE70970 were used as two validation cohorts. Consensus clustering was employed for unsupervised classification of the training cohort. Classifier was built using support vector machine (SVM), and was validated in the two validation cohorts. Univariate and multivariate Cox regression analyses were employed for feature selection and constructing a prognostic model for predicting high-risk distant metastasis, respectively. Results: We identified three NPC subtypes (NPC1, 2, and 3) that are molecularly distinct and clinically relevant. NPC1 (~45%) is enriched for cell cycle related pathways, and patients classified to NPC1 have an intermediate survival; NPC3 (~19%) is enriched for immune related pathways, and has good clinical outcomes. More importantly, NPC2 (~36%) is associated with poor prognosis, and is characterized by upregulation of epithelial-mesenchymal transition (EMT). Out of the total 25 differentially expressed miRNAs in NPC2, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (p < 0.05), as determined by univariate Cox regression analysis. For identification of high-risk distant metastasis, we built a multivariate Cox regression model using the selected 4 miRNAs. Our model can robustly stratify NPC patients into high- and low- risk groups both in GSE32960 (HR 3.1, 95% CI 1.8-5.4, p = 1.2e-05) and GSE70970 (HR 2.2, 95% CI 1.1-4.5, p = 0.022) cohorts. Conclusions: We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis.

Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

2021 ◽  
Vol 21 ◽  
Author(s):  
Dawei Zhou ◽  
Junchen Wan ◽  
Jiang Luo ◽  
Yuhao Tao
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Primary Liver Cancer ◽  
Risk Groups ◽  
Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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.

The Kyoto Prognostic Index for Patients with Diffuse Large B-Cell Lymphoma in the Rituximab Era

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3877-3877
Author(s):  
Tsutomu Kobayashi ◽  
Junya Kuroda ◽  
Isao Yokota ◽  
Kazuna Tanba ◽  
Tomohiko Taki ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Cell Lymphoma ◽  
Prognostic Index ◽  
B Cell Lymphoma ◽  
Risk Groups ◽  
Red Cross ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background The treatment outcome of diffuse large B cell lymphoma (DLBCL) has been greatly improved by rituximab (R) incorporating R-CHOP-based immunochemotherapy. The purpose of this study was to design a new prognostic model which can accurately predict the treatment outcome of DLBCL by R-CHOP (-like) immunochemotherapy, especially for discriminating very high risk patients with rapid disease progression and a short survival period from other large proportion of patients with favourable treatment outcome. Patients and Methods We retrospectively analysed the clinical records of patients who were histologically diagnosed as DLBCL and treated with either R-CHOP or R-CHOP-like therapy at the Kyoto Prefectural University of Medicine and Japanese Red Cross Kyoto Daiichi Hospital from January 2006 to December 2013 and at the Japanese Red Cross Kyoto Daini Hospital from January 2006 to April 2014. Patients were randomly divided into two groups for each institution; 70% for the training sample to construct a new prognostic model and 30% for validation of predictive performance. To evaluate the qualities of discrimination and prediction of risk groups by individual indices, we examined the c-index and the relative Brier score reduction (RBSR) in the validation cohort. The revised-International Prognostic Index (R-IPI) and the NCCN-IPI were also evaluated as the references. Results With a median follow-up time of 32.2 months, the 3-year overall survival (OS) and progression-free survival (PFS) of all patients were 78.5% and 67.4%, respectively. In the study cohort of 323 randomly selected patients, multivariate analyses revealed that the serum LDH level, ECOG performance status ≥2, serum albumin level <3.5mg/dL, and extranodal lymphoma involvement (bone marrow, skin, bone and/or lung/pleura) significantly associated with OS. In contrast, the multivariate analysis did not reveal that age, the disease stage according to the Ann Arbor staging system, or C-reactive protein associated with OS. A novel prognostic model, designated here as the Kyoto Prognostic Index (KPI), consisting of the four prognostic factors for OS, was constructed by classifying patients into four risk groups: low (L), low-intermediate (L-I), high-intermediate (H-I), and high (H). Based on the KPI, the 3-year OS and PFS were 96.4% and 84.4% in the L group, 84.7% and 70.2% in the L-I group, 63.8% and 53.4% in the H-I group, and 33.3% and 24.1% in the H group, respectively. Importantly, the KPI better discriminated the highest risk subgroup than the R-IPI (3-year OS and PFS: 64.8% and 50.8%) and the NCCN-IPI (3-year OS and PFS: 40.3% and 24.3%), and these findings were successfully reproduced in the validation cohort of 142 patients. The OS and PFS by the KPI were well correlated with c-indices of 0.740 and 0.703, respectively, thus indicating the proposed model with the optimal capability for distinguishing the survival periods among different risk groups, while the c-indices of OS and PFS as determined by the R-IPI were 0.642 and 0.668, and those as determined by the NCCN-IPI were 0.736 and 0.749. The RBSR of OS and PFS by the KPI were 30.5% and 18.3%, compared with that determined by the R-IPI of 13.5% and 12.2%, and those as determined by the NCCN-IPI of 25.1% and 17.2%, thus, indicating that our model can predict the mortality of patients more accurately compared with R-IPI or NCCN-IPI. Conclusion The KPI is a robust and feasible prognostic model for DLBCL in the current R era. Compared with the conventional prognostic models, such as the R-IPI and the NCCN-IPI, it can better discriminate especially the high risk subgroup of DLBCL and also PFS as well as OS in patients treated with R-CHOP (-like) immunochemotherapy independently of age of disease onset. Thus, the KPI may be more useful for treatment planning when compared with that of other indices. Prospective studies are needed to confirm the value of the KPI as a new prognostic model for DLBCL. Disclosures No relevant conflicts of interest to declare.

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