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Incidence trends and risk prediction nomogram of metachronous second primary lung cancer in lung cancer survivors.

ABSTRACT: BACKGROUND:This study was designed to estimate the trends in 5-year incidence of metachronous second primary lung cancer(SPLC) and to establish a risk prediction model to identify candidates who were at high risk of developing metachronous SPLC. METHODS:Incidence data between 2004 and 2007 were obtained from SEER database, including 42453 participants who survived ? 2 years after the initial diagnosis of lung cancer. Joinpoint regression analysis was used to calculate the 5-year incidence rates of metachronous SPLC per 100 000 population. Related risk factors of the survivors who developed MSPLC during five years were identified through logistic regression analysis, followed by establishment of risk prediction nomogram. Discrimination (C-index), calibration and decision analysis were further performed to assess the validation and clinical net benefit of risk prediction nomogram. RESULTS:A total of 1412 survivors with lung cancer developed MSPLC during five years, with 3546 per 100 000 population of age-adjusted 5-year incidence. Age, histology, tumor stage, and radiation were recognized as risk factors of metachronous SPLC, as indicated by logistic regression analysis. The risk prediction nomogram of metachronous SPLC harbored moderate discrimination(C-index = 0.67) and good calibration, with the risk of 0.01 to 0.11.The decision curve analysis showed that clinical net benefit of this risk prediction nomogram in a range of risk thresholds (0.01 to 0.06) was higher compared to all-screening or no-screening strategies. CONCLUSIONS:Collectively, the cumulative risk of metachronous SPLC of the survivors increased over time. The risk prediction nomogram was available to select high-risk survivors who should regularly undergo computed tomography screening.

SUBMITTER: Hu ZG

PROVIDER: S-EPMC6296553 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Incidence trends and risk prediction nomogram of metachronous second primary lung cancer in lung cancer survivors.

Hu Zhi Gang ZG Li Wen Xin WX Ruan Yu Shu YS Zeng Fan Jun FJ

PloS one 20181217 12

<h4>Background</h4>This study was designed to estimate the trends in 5-year incidence of metachronous second primary lung cancer(SPLC) and to establish a risk prediction model to identify candidates who were at high risk of developing metachronous SPLC.<h4>Methods</h4>Incidence data between 2004 and 2007 were obtained from SEER database, including 42453 participants who survived ≥ 2 years after the initial diagnosis of lung cancer. Joinpoint regression analysis was used to calculate the 5-year i ...[more]

PMID: 30557376

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Project description:BackgroundWith advancing therapeutics, lung cancer (LC) survivors are rapidly increasing in number. Although mounting evidence suggests LC survivors have high risk of second primary lung cancer (SPLC), there is no validated prediction model available for clinical use to identify high-risk LC survivors for SPLC.MethodsUsing data from 6325 ever-smokers in the Multiethnic Cohort (MEC) study diagnosed with initial primary lung cancer (IPLC) in 1993-2017, we developed a prediction model for 10-year SPLC risk after IPLC diagnosis using cause-specific Cox regression. We evaluated the model's clinical utility using decision curve analysis and externally validated it using 2 population-based data-Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST)-that included 2963 and 2844 IPLC (101 and 93 SPLC cases), respectively.ResultsOver 14 063 person-years, 145 (2.3%) ever-smoking IPLC patients developed SPLC in MEC. Our prediction model demonstrated a high predictive accuracy (Brier score = 2.9, 95% confidence interval [CI] = 2.4 to 3.3) and discrimination (area under the receiver operating characteristics [AUC] = 81.9%, 95% CI = 78.2% to 85.5%) based on bootstrap validation in MEC. Stratification by the estimated risk quartiles showed that the observed SPLC incidence was statistically significantly higher in the 4th vs 1st quartile (9.5% vs 0.2%; P < .001). Decision curve analysis indicated that in a wide range of 10-year risk thresholds from 1% to 20%, the model yielded a larger net-benefit vs hypothetical all-screening or no-screening scenarios. External validation using PLCO and NLST showed an AUC of 78.8% (95% CI = 74.6% to 82.9%) and 72.7% (95% CI = 67.7% to 77.7%), respectively.ConclusionsWe developed and validated a SPLC prediction model based on large population-based cohorts. The proposed prediction model can help identify high-risk LC patients for SPLC and can be incorporated into clinical decision making for SPLC surveillance and screening.

Project description:BackgroundMetachronous second primary lung cancer (MSPLC) is not that rare but is seldom studied.ObjectiveWe aim to compare real-world survival outcomes between different surgery strategies and radiotherapy for MSPLC.MethodsThis retrospective study analyzed data collected from patients with MSPLC between 1988 and 2012 in the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) analyses and machine learning were performed to compare variables between patients with MSPLC. Survival curves were plotted using the Kaplan-Meier method and were compared using log-rank tests.ResultsA total of 2451 MSPLC patients were categorized into the following treatment groups: 864 (35.3%) received radiotherapy, 759 (31%) underwent surgery, 89 (3.6%) had surgery plus radiotherapy, and 739 (30.2%) had neither treatment. After PSM, 470 pairs each for radiotherapy and surgery were generated. The surgery group had significantly better survival than the radiotherapy group (P<.001) and the untreated group (563 pairs; P<.001). Further analysis revealed that both wedge resection (85 pairs; P=.004) and lobectomy (71 pairs; P=.002) outperformed radiotherapy in overall survival for MSPLC patients. Machine learning models (extreme gradient boosting, random forest classifier, adaptive boosting) demonstrated high predictive performance based on area under the curve (AUC) values. Least absolute shrinkage and selection operator (LASSO) regression analysis identified 9 significant variables impacting cancer-specific survival, emphasizing surgery's consistent influence across 1 year to 10 years. These variables encompassed age at diagnosis, sex, year of diagnosis, radiotherapy of initial primary lung cancer (IPLC), primary site, histology, surgery, chemotherapy, and radiotherapy of MPSLC. Competing risk analysis highlighted lower mortality for female MPSLC patients (hazard ratio [HR]=0.79, 95% CI 0.71-0.87) and recent IPLC diagnoses (HR=0.79, 95% CI 0.73-0.85), while radiotherapy for IPLC increased mortality (HR=1.31, 95% CI 1.16-1.50). Surgery alone had the lowest cancer-specific mortality (HR=0.83, 95% CI 0.81-0.85), with sublevel resection having the lowest mortality rate among the surgical approaches (HR=0.26, 95% CI 0.21-0.31). The findings provide valuable insights into the factors that influence cumulative cancer-specific mortality.ConclusionsSurgical resections such as wedge resection and lobectomy confer better survival than radiation therapy for MSPLC, but radiation can be a valid alternative for the treatment of MSPLC.

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Incidence trends and risk prediction nomogram of metachronous second primary lung cancer in lung cancer survivors.

Publications

Incidence trends and risk prediction nomogram of metachronous second primary lung cancer in lung cancer survivors.

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