Dataset Information

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

ABSTRACT: Importance:Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. Objective:The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort. Design:1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots. Setting:General community. Main outcome:A nomogram model was developed to predict overall survival in NSCLC patients. Results:We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access. Conclusions:This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

SUBMITTER: Wang T

PROVIDER: S-EPMC6452590 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

Wang Tao T Lu Rong R Lai Sunny S Schiller Joan H JH Zhou Fang Liz FL Ci Bo B Wang Stacy S Gao Xiaohan X Yao Bo B Gerber David E DE Johnson David H DH Xiao Guanghua G Xie Yang Y

Cancer informatics 20190405

<h4>Importance</h4>Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials.<h4>Objective</h4>The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort.<h4>Design</h4>1817 patients with advanced NSCLC from the c ...[more]

PMID: 31057324

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Project description:Lung cancer is a leading cause of cancer-related death, and >80% of lung cancer diagnoses are non-small-cell lung cancer (NSCLC). However, when using current staging and prognostic indices, the prognosis can vary significantly. In the present study, we calculated a prognostic index for predicting overall survival (OS) in NSCLC patients. The data of 545 NSCLC patients were retrospectively reviewed. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of clinicopathological factors. Age (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.02-1.54), TNM stage (III, HR = 1.64, 95% CI = 1.08-2.48; IV, HR = 2.33, 95% CI = 1.48-3.69), lung lobectomy (HR = 1.96, 95% CI = 1.45-2.66), chemotherapy (HR = 1.42, 95% CI = 1.15-1.74), and pretreatment hemoglobin level (HR = 1.61, 95% CI = 1.28-2.02) were independent prognosticators. A prognostic index for NSCLC (PInscl, 0-6 points) was calculated based on age (≥65 years, 1 point), tumor-node-metastasis (TNM) stage (III, 1 point; IV, 2 points), lung lobectomy (no, 1 point), chemotherapy (no, 1 point), and pretreatment hemoglobin level (low, 1 point). In comparison with the "PInscl = 0" subgroup (survival time = 2.71 ± 1.86 years), the "PInscl = 2" subgroup (survival time = 1.86 ± 1.24 years), "PInscl = 3" subgroup (survival time = 1.45 ± 1.07 years), "PInscl = 4" subgroup (survival time = 1.17 ± 1.06 years), "PInscl = 5" subgroup (survival time = 0.81 ± 0.78 years), and "PInscl = 6" subgroup (survival time = 0.65 ± 0.56 years) exhibited significantly shorter survival times. Kaplan-Meier survival analysis showed that patients with higher PInscl scores had poorer OS than those with lower scores (log-rank test: χ2 = 155.82, P < 0.0001). The area under the curve of PInscl for predicting the 1-year OS was 0.73 (95 % CI = 0.69-0.77, P < 0.001), and the PInscl had a better diagnostic performance than the Karnofsky performance status or TNM stage (P < 0.01). In conclusion, the PInscl, which is calculated from age, TNM stage, lung lobectomy, chemotherapy, and pretreatment hemoglobin level, significantly predicted OS in NSCLC patients.

Project description:BackgroundThe aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC).MethodsA total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts.ResultsEleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients' OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843-0.879 vs. C-index, 0.851, 95% CI: 0.832-0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841-0.896 vs. C-index, 0.854, 95% CI: 0.824-0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation.ConclusionThe established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.

Project description:BackgroundDespite the well-known role of immunoscore, as a prognostic tool, that appeared to be superior to tumor-node-metastasis (TNM) staging system, no prognostic scoring system based on immunohistochemistry (IHC) staining digital image analysis has been established in non-small cell lung cancer (NSCLC). Hence, we aimed to develop and validate an immune-based prognostic risk score (IMPRS) that could markedly improve individualized prediction of postsurgical survival in patients with resected NSCLC.MethodsIn this retrospective study, complete resection of NSCLC (stage I-IIIA) was performed for two independent patient cohorts (discovery cohort, n=168; validation cohort, n=115). Initially, paraffin-embedded resected specimens were stained by immunohistochemistry (IHC) of three immune cell types (CD3+, CD4+, and CD8+ T cells), and a total of 5,580 IHC-immune features were extracted from IHC digital images for each patient by using fully automated pipeline. Then, an IHC-immune signature was constructed with selected features using the LASSO Cox analysis, and the association of signature with patients' overall survival (OS) was analyzed by Kaplan-Meier method. Finally, IMPRS was established by incorporating IHC-immune signature and independent clinicopathological variables in multivariable Cox regression analysis. Furthermore, an external validation cohort was included to validate this prognostic risk score.ResultsEight key IHC-immune features were selected for the construction of IHC-immune signature, which showed significant associations with OS in all cohorts [discovery: hazard ratio (HR)=11.518, 95%CI, 5.444-24.368; validation: HR=2.664, 95%CI, 1.029-6.896]. Multivariate analyses revealed IHC-immune signature as an independent prognostic factor, and age, T stage, and N stage were also identified and entered into IMPRS (all p<0.001). IMPRS had good discrimination ability for predicting OS (C-index, 0.869; 95%CI, 0.861-0.877), confirmed using external validation cohort (0.731, 0.717-0.745). Interestingly, IMPRS had better prognostic value than clinicopathological-based model and TNM staging system termed as C-index (clinicopathological-based model: 0.674; TNM staging: 0.646, all p<0.05). More importantly, decision curve analysis showed that IMPRS had adequate performance for predicting OS in resected NSCLC patients.ConclusionsOur findings indicate that the IMPRS that we constructed can provide more accurate prognosis for individual prediction of OS for patients with resected NSCLC, which can help in guiding personalized therapy and improving outcomes for patients.

Dataset Information

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

Publications

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

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