Dataset Information

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

ABSTRACT: As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportional hazard model. Prognostic nomogram was performed and its predictive performance was validated via the modeling and validating cohort data. Additionally, propensity score matching (PSM) was also applied to evaluate whether surgery affected the OS of this study population. 5411 eligible DPLC patients were included in this study cohort, with 41.0% of 3-year OS rate and 27.7% of 5-year OS rate. Age, sex, race, grade, stage, lymph node (LN) metastasis, histological type, primary site, and surgery were considered to be prognostic factors of OS. The C-indexes of the established nomogram were 0.70 (95% CI (0.69, 0.71)) in the modeling group and 0.70 (95% CI (0.68, 0.72)) in the validation group, which showed an ideal model discrimination ability. AUC and calibration plots of 3- and 5-year OS also proved the good performance of the established nomogram. After 1?:?1 PSM, surgery can potentially reduce the risk of OS (HR?=?0.63, 95% CI: 0.56-0.72) of DPLC. The prognostic nomogram with reliable performance was developed to predict 3- and 5-year OS rates, which could assist clinicians to make more reasonable survival prediction for DPLC patients. For patients without absolute surgical contraindications, surgery should be actively considered.

SUBMITTER: Song CK

PROVIDER: S-EPMC7049836 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

Song Cong-Kuan CK Guo Zi-Xin ZX Shen Xiao-Yan XY Wang Yu-Jin YJ Wang Qing-Wen QW Yu Dong-Hu DH Chen Chen C Liu Xiao-Ping XP Huang Jing-Yu JY Li Sheng S Hu Weidong W

BioMed research international 20200219

As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportiona ...[more]

PMID: 32149127

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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.

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Dataset Information

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

Publications

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

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