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Lung Cancer Risk Prediction Models for Asian Ever-Smokers Publisher Pubmed

Summary: Better lung cancer screening for Asians? Study refits models like Shanghai—improves prediction in low smokers, quitters. May boost early detection in diverse populations. #LungCancer #RiskPrediction

Yang JJ1, 2, 3 ; Wen W1 ; Zahed H4 ; Zheng W1 ; Lan Q5 ; Abe SK6 ; Rahman MS6, 7 ; Islam MR6, 8 ; Saito E9 ; Gupta PC10 ; Tamakoshi A11 ; Koh WP12, 13 ; Gao YT14 ; Sakata R15 Show All Authors
Authors
  1. Yang JJ1, 2, 3
  2. Wen W1
  3. Zahed H4
  4. Zheng W1
  5. Lan Q5
  6. Abe SK6
  7. Rahman MS6, 7
  8. Islam MR6, 8
  9. Saito E9
  10. Gupta PC10
  11. Tamakoshi A11
  12. Koh WP12, 13
  13. Gao YT14
  14. Sakata R15
  15. Tsuji I16
  16. Malekzadeh R17
  17. Sugawara Y16
  18. Kim J18
  19. Ito H19, 20
  20. Nagata C21
  21. You SL22
  22. Park SK23
  23. Yuan JM24, 25
  24. Shin MH26
  25. Kweon SS27
  26. Yi SW28
  27. Pednekar MS10
  28. Kimura T11
  29. Cai H1
  30. Lu Y16
  31. Etemadi A29
  32. Kanemura S16
  33. Wada K21
  34. Chen CJ30
  35. Shin A23, 31
  36. Wang R24
  37. Ahn YO23
  38. Shin MH26
  39. Ohrr H32
  40. Sheikh M4
  41. Blechter B5
  42. Ahsan H33
  43. Boffetta P34, 35
  44. Chia KS36
  45. Matsuo K37, 38
  46. Qiao YL39
  47. Rothman N5
  48. Inoue M6
  49. Kang D23, 31
  50. Robbins HA4
  51. Shu XO1

Source: Journal of Thoracic Oncology Published:2024


Abstract

Introduction: Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians. Methods: In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the “Shanghai models” to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts. Results: Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67–0.74] for lung cancer death and 0.69 [0.67–0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90–1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69–0.74] for lung cancer death and 0.70 [0.67–0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters. Conclusions: The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia. © 2023
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