Dataset Information

Development and Validation of a Nomogram Prognostic Model for SCLC Patients.

ABSTRACT: INTRODUCTION:SCLC accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB). METHODS:Clinical data for 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated area under the (time-dependent receiver operating characteristic) curve (AUC). RESULTS:The following variables were contained in the final prognostic model: age, sex, race, ethnicity, Charlson/Deyo score, TNM stage (assigned according to the American Joint Committee on Cancer [AJCC] eighth edition), treatment type (combination of surgery, radiation therapy, and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 ± 0.004 and an integrated area under the curve of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC eighth edition TNM-staging system. We implemented the proposed nomogram and four previously published nomograms in an online webserver. CONCLUSIONS:We developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including models using AJCC staging.

SUBMITTER: Wang S

PROVIDER: S-EPMC7678404 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Development and Validation of a Nomogram Prognostic Model for SCLC Patients.

Wang Shidan S Yang Lin L Ci Bo B Maclean Matthew M Gerber David E DE Xiao Guanghua G Xie Yang Y

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20180611 9

<h4>Introduction</h4>SCLC accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB).<h4>Methods</h4>Clinical data for 24,680 SCLC patients diagnosed from 2004 to 2011 were used t ...[more]

PMID: 29902534

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Project description:IntroductionThis study aimed at generating a new simplified prognostic score (SPS) using common clinical and biological variables to discriminate a limited number of subgroups of patients with SCLC differing by their overall survival (OS).MethodsThe SPS was developed exploring the Montpellier University Hospital retrospective database of 401 patients over a 16-year period. All patients had received etoposide - platinum-based chemotherapy as first-line treatment. The SPS development took into account significant determinants of OS in the Cox model, weighted by their regression β coefficients. Validation of the consequent SPS has been done separately in a combined population of 213 patients accrued from two different published trials (NCT03059667 and NCT00930891).ResultsThe significant independent determinants of OS included the following: (1) American Joint Committee on Cancer TNM stage IV (hazard ratio [HR]: 2.52; 95% confidence interval [CI]: 1.91-3.33); (2) Eastern Cooperative Oncology Group performance status greater than 1 (HR: 2.27; 95% CI: 1.79-2.87); (3) the presence of liver metastases (HR: 1.66; 95% CI: 1.29-2.15); and (4) neutrophil-to-lymphocyte ratio greater than 4 (HR: 1.39; 95% CI: 1.11-1.92). The SPS generated with these four variables, segregated three groups (good, intermediate, and poor prognosis) with respective median OS of 26.9 months (95% CI: 20.1-38.9), 11.5 months (95% CI: 9.8-13.0), and 6.8 months (95% CI: 5.8-8.3; log-rank p < 10-4). Harrell's C statistic estimate was 0.68 ± 0.012, suggesting goodness of calibration. In the validation cohort, the SPS segregated the aforementioned three subgroups in a nearly similar manner, with respective median OS: 27.2, 12.3, and 8.6 months (log-rank p < 10-3; Harrell's C statistic: 0.58 ± 0.02).ConclusionsThe SPS is easy to calculate in real-life practice and efficiently discriminates three populations with different prognoses. This study deserves further validation of this score in patients with SCLC receiving immunochemotherapy.

Project description: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.

Project description:PurposeSynchronous peritoneal metastasis (S-PM) is considered a poor prognostic factor for colorectal cancer (CRC) and there is no nomogram to predict the survival of these patients. In this study, we aimed to use a multicenter data to identify the factors associated with S-PM of CRC to construct a nomogram for predicting the overall survival (OS) of these patients.MethodsCRC patients with S-PM from two medical centers were enrolled between September 2007 and June 2017. Multivariate analysis was used to identify independent factors associated with OS for the nomogram to predict the 1-, 2-, and 3-year OS rates in the development group. The concordance index (C-index), calibration plot, relative operating characteristic (ROC) curve with area under the curve (AUC) were calculated to evaluate the performance of the nomogram in both the development and an external validation group.Results277 CRC patients with S-PM in the development group and 68 patients in the validation group were eligible for this study. In multivariate analysis of development group, age, carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), cytoreductive surgery (CRS), hyperthermic intraperitoneal chemotherapy (HIPEC), and chemotherapy were independent variables for OS, based on which the nomogram was built. The C-index of the nomogram in the development and validation group was 0.701 (95% Cl, 0.666-0.736) and 0.716 (95% Cl, 0.622-0.810); demonstrating good discriminative ability. The calibration plots showed satisfactory consistency between actual observation and nomogram-predicted OS probabilities in the development and external validation group. The nomogram showed good predictive accuracy for 1-, 2-, and 3-year OS rates in both groups with AUC >0.70. An online dynamic webserver was also developed for increasing the ease of the nomogram.ConclusionsWe developed and validated a predictive nomogram with good discriminative and high accuracy to predict the OS in CRC patients with S-PM.

Project description:BackgroundGastric cancer (GC) is one of the most malignant diseases and threatens the health of individuals across the globe. Hitherto, the identification of prognosis risk stratification on GC has mainly depended on the TNM staging, but owing to its inaccuracy and incompleteness, the prognostic value it offers remains controversial in the current clinical setting. Thus, an effective prognostic model for GC after radical gastrectomy is still needed.MethodsPatients with pathologically confirmed GC who underwent radical gastrectomy from 2 different centers were retrospectively enrolled into a training and the validation cohort, respectively. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select variables among multiple factors, including clinical characteristics, pathological parameters, and surgery- and treatment-related indicators. The multivariate Cox regression method was used to establish the model to predict 1-, 2-, and 3-year survival. Both internal and external validations of the nomogram were then completed in terms of discrimination, calibration, and clinical utility. Finally, prognostic risk stratification of GC was conducted with X-tile software.ResultsA total of 1,424 patients with GC were eligible in this study, including 1,010 in the training cohort and 414 in the validation cohort. Seven indicators were selected by LASSO to develop the nomogram, including the number of positive lymph nodes, tumor size, adjacent organ invasion, vascular invasion, the level of carbohydrate antigen 125 (CA 125), depth of invasion, and human epidermal growth factor receptor 2 (HER2) status. The nomogram demonstrated a robust predictive capacity with favorable accuracy, discrimination, and clinical utility both in the internal and external validations. Moreover, we divided the population into 3 risk groups of survival according to the cutoff points generated by X-tile, and in this way, the nomogram was further improved into a risk-stratified prognosis model.ConclusionsWe have developed a prognostic risk stratification nomogram for GC patients after radical gastrectomy with 7 available indicators that may guide clinical practice and help facilitate tailored decision-making, thus avoiding overtreatment or undertreatment and improving communication between clinicians and patients.

Dataset Information

Development and Validation of a Nomogram Prognostic Model for SCLC Patients.

Publications

Development and Validation of a Nomogram Prognostic Model for SCLC Patients.

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