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.

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.

scholarly journals Bioinformatic identification of genomic instability-associated lncRNAs signatures for improving the clinical outcome of cervical cancer by a prognostic model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Zhang ◽  
Nan Ding ◽  
Yongxing He ◽  
Chengbin Tao ◽  
Zhongzhen Liang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Genomic Instability ◽  
Prognostic Model ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Training Set

AbstractThe research is executed to analyze the connection between genomic instability-associated long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We set a prognostic model up and explored different risk groups' features. The clinical datasets and gene expression profiles of 307 patients have been downloaded from The Cancer Genome Atlas database. We established a prognostic model that combined somatic mutation profiles and lncRNA expression profiles in a tumor genome and identified 35 genomic instability-associated lncRNAs in cervical cancer as a case study. We then stratified patients into low-risk and high-risk groups and were further checked in multiple independent patient cohorts. Patients were separated into two sets: the testing set and the training set. The prognostic model was built using three genomic instability-associated lncRNAs (AC107464.2, MIR100HG, and AP001527.2). Patients in the training set were divided into the high-risk group with shorter overall survival and the low-risk group with longer overall survival (p < 0.001); in the meantime, similar comparable results were found in the testing set (p = 0.046), whole set (p < 0.001). There are also significant differences in patients with histological grades, FIGO stages, and different ages (p < 0.05). The prognostic model focused on genomic instability-associated lncRNAs could predict the prognosis of cervical cancer patients, paving the way for further research into the function and resource of lncRNAs, as well as a key approach to customizing individual care decision-making.

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.

scholarly journals Bone Marrow Mastocytosis Is Independently Associated with Inferior Survival in Chronic Myelomonocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2956-2956
Author(s):  
Andrew T Kuykendall ◽  
Anthony Hunter ◽  
Ling Zhang ◽  
Eric Padron ◽  
Chetasi Talati ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Overall Survival ◽  
High Risk ◽  
Prognostic Model ◽  
Systemic Mastocytosis ◽  
Risk Groups ◽  
Prognostic Impact ◽  
Kaplan Meier ◽  
Myelomonocytic Leukemia ◽  
Baseline Demographic

Introduction: Systemic mastocytosis with associated hematologic neoplasm (SM-AHN) is defined by the presence of a concomitant hematologic malignancy with chronic myelomonocytic leukemia (CMML) being a particularly common partner. The overall survival of patients with SM-AHN is inferior to those with SM alone, even when matched for relevant prognostic covariates. However, the prognostic impact of mastocytosis in patients with CMML is unknown. Methods: CMML patients with concomitant mastocytosis were identified from the Moffitt Cancer Center CMML database. We assessed baseline demographic, clinical, and molecular findings and used Kaplan-Meier method to estimate overall survival (OS). The log-rank test was used to compare Kaplan-Meier curves, We then compared the SM-CMML cohort to a well-established institutional database of CMML patients. Baseline demographic and clinical variables were analyzed using GraphPad Prism and SPSS was used for Cox Regression Analysis. Results: Between 5/2004 and 5/2019 22 of 645 CMML patients (3.4%) were identified to have concomitant mastocytosis. The median follow-up for the 22 patients with SM-CMML was 51 months. Nine (41%) patients were diagnosed with de novo CMML prior to SM. In these cases, secondary SM-CMML occurred at a median time of 7 months after CMML diagnosis. Ten patients (45%) were diagnosed with CMML and SM concurrently and 3 (14%) were diagnosed with SM prior to CMML. Among 17 patients tested for KIT mutations, 12 were found to harbor a mutation. The remaining five patients did not undergo high-sensitivity KIT testing on a bone marrow aspirate. Eleven patients had extended gene sequencing performed with the most common additional mutations involving TET2 (45%), SRSF2 (55%), ASXL1 (27%), RAS (27%), DNMT3A (27%), and RUNX1 (27%). The median overall survival (OS) was estimated to be 38.6 months. Next, we compared this cohort of SM-CMML patients to a large, established database of CMML patients (excluding those with concomitant SM). Age at diagnosis, baseline white blood cell count, hemoglobin, and platelet count were well matched between the two groups. Applying the Mayo CMML Prognostic Model to the cohort of SM-CMML patients demonstrated that 32%, 41%, and 27% were low, intermediate and high risk, respectively. In the CMML cohort, 13%, 35%, and 51% were low, intermediate and high, risk respectively, suggesting the SM-CMML was more common in the lower-risk group (p=0.025). The median OS was similar between the two cohorts (median OS 31.3 vs 38.6 months, p = 0.43). However, multivariate analysis including Mayo Prognostic Scoring System, age > 65, and SM component revealed all three variables to be independently associated with survival (HR 1.8, p < 0.001; HR 1.7, p = 0.047; and HR 1.5, p 0.003, respectively). Assessing the impact of mastocytosis in low, intermediate, and high-risk groups separately, the inferior prognostic impact of mastocytosis was most prominent in high-risk patients (OS 19.6 mo vs. 5.4 mo; p = 0.049). Survival outcomes between SM-CMML and CMML were not statistically different in intermediate and low-risk groups (p = 0.47 and p = 0.19, respectively). Among 16 deaths in the SM-CMML cohort, cause of death was able to be assessed in 13 patients. Four (31%) patients died after transformation to acute myeloid leukemia (AML). These patients were either intermediate- or high-risk by Mayo Prognostic Model. Nine patients (69%) died due to multisystem organ failure due to progressive systemic mastocytosis without development of acute leukemia. Among these, 4 (44%) were low-risk, 3 (33%) were intermediate-risk, and 2 (22%) were high-risk. Conclusions: SM-CMML typically presented with lower-risk disease when graded by the Mayo CMML Prognostic Model. Compared head-to-head, OS was similar between SM-CMML and CMML; however multivariate analysis revealed the SM component to be a significant adverse prognostic factor. The presence of bone marrow mastocytosis is associated with inferior survival in high-risk CMML cases. Cause of death among SM-CMML patients was attributable to both progressive mastocytosis and transformation to AML. AML transformation was limited to intermediate- and high-risk group while progressive mastocytosis was seen across the risk spectrum. Future studies are warranted to determine if SM therapy can mitigate this outcome. Figure 1 Disclosures Kuykendall: Abbvie: Honoraria; Celgene: Honoraria; Incyte: Honoraria, Speakers Bureau; Janssen: Consultancy. Talati:Celgene: Honoraria; Agios: Honoraria; Jazz Pharmaceuticals: Honoraria, Speakers Bureau; Daiichi-Sankyo: Honoraria; Astellas: Honoraria, Speakers Bureau; Pfizer: Honoraria. Komrokji:DSI: Consultancy; Incyte: Consultancy; Agios: Consultancy; JAZZ: Consultancy; JAZZ: Speakers Bureau; Novartis: Speakers Bureau; pfizer: Consultancy; celgene: Consultancy.

scholarly journals Construction and Validation of a Prognostic Model for the Assessment of Postoperative Overall Survival of Patients with Metaplastic Breast Cancer: Based on a Retrospective Large Data Analysis and Chinese Multicenter Study

2021 ◽  
Author(s):  
Ge Wang ◽  
Xin Ren ◽  
Mengmeng Wang ◽  
Xiaomin Sun ◽  
Yongsheng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Survival Rate ◽  
Prognostic Model ◽  
Seer Database ◽  
Training Set ◽  
Metaplastic Breast Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Purpose: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods: Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans. Abstract Background: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods： Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

scholarly journals Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System

2022 ◽  
Vol 21 ◽  
pp. 153303382110662
Author(s):  
Zhiyi Fan ◽  
Changxing Chi ◽  
Yuexin Tong ◽  
Zhangheng Huang ◽  
Youxin Song ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Tumor Size ◽  
Training Set ◽  
N Stage ◽  
Independent Risk Factors ◽  
Testing Set

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

A prognostic model for predicting overall survival in metastatic castrate-resistant prostate cancer (mCRPC) men treated with second-line chemotherapy.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 5011-5011
Author(s):  
Susan Halabi ◽  
Chen-Yen Lin ◽  
Eric Jay Small ◽  
Andrew J. Armstrong ◽  
Ellen B. Kaplan ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Model ◽  
Risk Groups ◽  
Phase Iii ◽  
Predictive Score ◽  
Second Line ◽  
First Line ◽  
Second Line Chemotherapy ◽  
Line Chemotherapy ◽  
Testing Set

5011 Background: Although several prognostic models for overall survival (OS) have been developed and validated in men with chemotherapy naïve mCRPC, this work sought to develop and validate a prognostic model to predict OS in men who had progressed following first-line chemotherapy, and were receiving second line chemotherapy. Methods: Data from a phase III trial of cabazitaxel plus prednisone compared to mitoxantrone plus prednisone in mCRPC men who had developed progressive disease following first-line chemotherapy (TROPIC trial) were used. The TROPIC data was randomly split into training (n=507) and testing (n=248) sets. A separate data set consisting of 488 men previously treated with docetaxel who were randomly assigned to either satraplatin and prednisone or placebo and prednisone (SPARC trial), was used as a second testing set for external validation. Adaptive Lasso selected nine baseline prognostic factors of OS. A predictive score was computed from the estimated regression coefficients and used to classify patients into low (<-1.25) and high (≥-1.25) risk groups in the two testing sets. The model was assessed on the testing sets for its predictive accuracy using area under the curve (AUC). Results: The 9 prognostic variables in the final model included: ECOG performance status, time since last docetaxel use, measurable disease, presence of visceral disease, pain, duration of prior hormonal use, hemoglobin, prostate specific antigen and alkaline phosphatase. The median OS in the TROPIC testing set were 11 and 16 months in the high and low risks, respectively, with a hazard ratio (HR) 2.3 (p-value<0.0001). The median OS in SPARC were 11 and 20 months in the high and low risk groups, respectively (HR=2.0, p<0.0001). The AUC for this model was 0.73 (95 CI 0.68-0.72) and 0.70 (95 CI 0.72-0.74) on the two testing sets (TROPIC, and SPARC), respectively. Conclusions: A prognostic model of OS in the post-docetaxel second line chemotherapy mCRPC setting was developed and externally validated. This model can be used to select patients to participate in clinical trials on the basis of their prognosis. Prospective validation is needed.

scholarly journals Development and validation of an individualized immune prognostic model in stage I–III lung squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi-Fan Yang ◽  
Di Wu ◽  
Jian Wang ◽  
Li Ba ◽  
Chen Tian ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Stage I ◽  
Tissue Samples ◽  
Mutation Burden

AbstractLung squamous cell carcinoma (LUSC) possesses a poor prognosis even for stages I–III resected patients. Reliable prognostic biomarkers that can stratify and predict clinical outcomes for stage I–III resected LUSC patients are urgently needed. Based on gene expression of LUSC tissue samples from five public datasets, consisting of 687 cases, we developed an immune-related prognostic model (IPM) according to immune genes from ImmPort database. Then, we comprehensively analyzed the immune microenvironment and mutation burden that are significantly associated with this model. According to the IPM, patients were stratified into high- and low-risk groups with markedly distinct survival benefits. We found that patients with high immune risk possessed a higher proportion of immunosuppressive cells such as macrophages M0, and presented higher expression of CD47, CD73, SIRPA, and TIM-3. Moreover, When further stratified based on the tumor mutation burden (TMB) and risk score, patients with high TMB and low immune risk had a remarkable prolonged overall survival compared to patients with low TMB and high immune risk. Finally, a nomogram combing the IPM with clinical factors was established to provide a more precise evaluation of prognosis. The proposed immune relevant model is a promising biomarker for predicting overall survival in stage I–III LUSC. Thus, it may shed light on identifying patient subset at high risk of adverse prognosis from an immunological perspective.

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.

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