Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups

Xiaofei Wang, Lin Gu, Ying Zhang, Daniel J. Sargent, William Richards, Apar Kishor Ganti, Jeffery Crawford, Harvey Jay Cohen, Thomas Stinchcombe, Everett Vokes, Herbert Pang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Purpose: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. Patients and methods: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. Results: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference = 0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference = 0.015 and 0.033, respectively. Conclusion: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.

Original languageEnglish (US)
Pages (from-to)281-287
Number of pages7
JournalLung Cancer
Volume90
Issue number2
DOIs
StatePublished - Nov 1 2015

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Non-Small Cell Lung Carcinoma
Age Groups
Survival
Neoplasms

Keywords

  • Independent validation
  • Non-small-cell lung cancer (NSCLC)
  • Performance status
  • Prognostic models

ASJC Scopus subject areas

  • Oncology
  • Pulmonary and Respiratory Medicine
  • Cancer Research

Cite this

Wang, X., Gu, L., Zhang, Y., Sargent, D. J., Richards, W., Ganti, A. K., ... Pang, H. (2015). Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups. Lung Cancer, 90(2), 281-287. https://doi.org/10.1016/j.lungcan.2015.08.007

Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups. / Wang, Xiaofei; Gu, Lin; Zhang, Ying; Sargent, Daniel J.; Richards, William; Ganti, Apar Kishor; Crawford, Jeffery; Cohen, Harvey Jay; Stinchcombe, Thomas; Vokes, Everett; Pang, Herbert.

In: Lung Cancer, Vol. 90, No. 2, 01.11.2015, p. 281-287.

Research output: Contribution to journalArticle

Wang, X, Gu, L, Zhang, Y, Sargent, DJ, Richards, W, Ganti, AK, Crawford, J, Cohen, HJ, Stinchcombe, T, Vokes, E & Pang, H 2015, 'Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups', Lung Cancer, vol. 90, no. 2, pp. 281-287. https://doi.org/10.1016/j.lungcan.2015.08.007
Wang, Xiaofei ; Gu, Lin ; Zhang, Ying ; Sargent, Daniel J. ; Richards, William ; Ganti, Apar Kishor ; Crawford, Jeffery ; Cohen, Harvey Jay ; Stinchcombe, Thomas ; Vokes, Everett ; Pang, Herbert. / Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups. In: Lung Cancer. 2015 ; Vol. 90, No. 2. pp. 281-287.
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abstract = "Purpose: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. Patients and methods: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. Results: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference = 0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference = 0.015 and 0.033, respectively. Conclusion: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.",
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AU - Zhang, Ying

AU - Sargent, Daniel J.

AU - Richards, William

AU - Ganti, Apar Kishor

AU - Crawford, Jeffery

AU - Cohen, Harvey Jay

AU - Stinchcombe, Thomas

AU - Vokes, Everett

AU - Pang, Herbert

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N2 - Purpose: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. Patients and methods: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. Results: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference = 0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference = 0.015 and 0.033, respectively. Conclusion: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.

AB - Purpose: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. Patients and methods: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. Results: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference = 0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference = 0.015 and 0.033, respectively. Conclusion: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.

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