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Risk Stratification for Second Primary Lung Cancer.

Summer S Han1, Gabriel A Rivera1, Martin C Tammemägi1

  • 1Summer S. Han, Sylvia K. Plevritis, and Heather A. Wakelee, Stanford University School of Medicine; Summer S. Han, Sylvia K. Plevritis, Scarlett L. Gomez, Iona Cheng, and Heather A. Wakelee, Stanford Cancer Institute; Heather A. Wakelee and Gabriel A. Rivera, Stanford University Department of Medicine, Division of Oncology, Stanford; Gabriel A. Rivera, Kaiser Permanente Fresno Medical Center, Fresno; Scarlett L. Gomez and Iona Cheng, Cancer Prevention Institute of California, Fremont, CA; and Martin C. Tammemägi, Brock University, St Catharines, Ontario, Canada.

Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
|June 24, 2017
PubMed
Summary
This summary is machine-generated.

This study developed a risk model to predict the 10-year risk of second primary lung cancer (SPLC) in lung cancer survivors. The model can help identify high-risk individuals for computed tomography screening.

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Area of Science:

  • Oncology
  • Epidemiology
  • Biostatistics

Background:

  • Lung cancer survivors have an increased risk of developing a second primary lung cancer (SPLC).
  • Accurate risk prediction is crucial for targeted screening and early detection of SPLC in this population.

Purpose of the Study:

  • To estimate the 10-year risk of SPLC in initial primary lung cancer (IPLC) survivors.
  • To evaluate the clinical utility of a risk prediction model for SPLC screening eligibility.

Main Methods:

  • Utilized SEER data for a cohort of 20,032 IPLC survivors (1988-2003) with ≥5 years survival.
  • Employed a proportional subdistribution hazards model to estimate 10-year SPLC risk, considering competing risks.
  • Included predictors: age, sex, race, treatment, histology, stage, and extent of disease.

Main Results:

  • Median 10-year SPLC risk was 8.36%, with substantial variation (0.56%-14.3%) based on stratification.
  • Risk stratification by deciles showed significantly higher SPLC incidence in the highest decile (12.5%) vs. lowest (2.9%).
  • Decision curve analysis identified risk thresholds (1%-11.5%) where the model offered clinical net benefit for screening.

Conclusions:

  • Risk stratification for SPLC is potentially valuable for identifying lung cancer survivors eligible for computed tomography screening.
  • Enhancing the model with environmental and genetic data could improve SPLC risk prediction and stratification.