A prediction model to help with oncologic mediastinal evaluation for radiation: Homer

Gabriela Martinez-Zayas, Francisco A. Almeida, Michael J. Simoff, Lonny Yarmus, Sofia Molina, Benjamin Young, David Feller-Kopman, Ala Eddin S. Sagar, Thomas Gildea, Labib G. Debiane, Horiana B. Grosu, Roberto F. Casal, Muhammad H. Arain, George A. Eapen, Carlos A. Jimenez, Laila Z. Noor, Shiva Baghaie, Juhee Song, Liang Li, David E. Ost

Research output: Contribution to journalArticle

Abstract

Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2j3) disease is important. Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2j3 disease. Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2j3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals. Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2j3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P, 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34). Conclusions: This prediction model can estimate the probability of N0, N1, and N2j3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

Original languageEnglish (US)
Pages (from-to)212-223
Number of pages12
JournalAmerican journal of respiratory and critical care medicine
Volume201
Issue number2
DOIs
StatePublished - Jan 15 2020

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Radiation
Non-Small Cell Lung Carcinoma
Calibration
Area Under Curve
Radiotherapy
ROC Curve
Positron-Emission Tomography
Decision Making
Adenocarcinoma
Logistic Models
Regression Analysis

Keywords

  • Endobronchial ultrasound
  • Lung cancer
  • Lung cancer staging
  • Mediastinal adenopathy

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine

Cite this

Martinez-Zayas, G., Almeida, F. A., Simoff, M. J., Yarmus, L., Molina, S., Young, B., ... Ost, D. E. (2020). A prediction model to help with oncologic mediastinal evaluation for radiation: Homer. American journal of respiratory and critical care medicine, 201(2), 212-223. https://doi.org/10.1164/rccm.201904-0831OC

A prediction model to help with oncologic mediastinal evaluation for radiation : Homer. / Martinez-Zayas, Gabriela; Almeida, Francisco A.; Simoff, Michael J.; Yarmus, Lonny; Molina, Sofia; Young, Benjamin; Feller-Kopman, David; Sagar, Ala Eddin S.; Gildea, Thomas; Debiane, Labib G.; Grosu, Horiana B.; Casal, Roberto F.; Arain, Muhammad H.; Eapen, George A.; Jimenez, Carlos A.; Noor, Laila Z.; Baghaie, Shiva; Song, Juhee; Li, Liang; Ost, David E.

In: American journal of respiratory and critical care medicine, Vol. 201, No. 2, 15.01.2020, p. 212-223.

Research output: Contribution to journalArticle

Martinez-Zayas, G, Almeida, FA, Simoff, MJ, Yarmus, L, Molina, S, Young, B, Feller-Kopman, D, Sagar, AES, Gildea, T, Debiane, LG, Grosu, HB, Casal, RF, Arain, MH, Eapen, GA, Jimenez, CA, Noor, LZ, Baghaie, S, Song, J, Li, L & Ost, DE 2020, 'A prediction model to help with oncologic mediastinal evaluation for radiation: Homer', American journal of respiratory and critical care medicine, vol. 201, no. 2, pp. 212-223. https://doi.org/10.1164/rccm.201904-0831OC
Martinez-Zayas, Gabriela ; Almeida, Francisco A. ; Simoff, Michael J. ; Yarmus, Lonny ; Molina, Sofia ; Young, Benjamin ; Feller-Kopman, David ; Sagar, Ala Eddin S. ; Gildea, Thomas ; Debiane, Labib G. ; Grosu, Horiana B. ; Casal, Roberto F. ; Arain, Muhammad H. ; Eapen, George A. ; Jimenez, Carlos A. ; Noor, Laila Z. ; Baghaie, Shiva ; Song, Juhee ; Li, Liang ; Ost, David E. / A prediction model to help with oncologic mediastinal evaluation for radiation : Homer. In: American journal of respiratory and critical care medicine. 2020 ; Vol. 201, No. 2. pp. 212-223.
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abstract = "Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2j3) disease is important. Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2j3 disease. Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2j3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals. Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2j3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P, 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34). Conclusions: This prediction model can estimate the probability of N0, N1, and N2j3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.",
keywords = "Endobronchial ultrasound, Lung cancer, Lung cancer staging, Mediastinal adenopathy",
author = "Gabriela Martinez-Zayas and Almeida, {Francisco A.} and Simoff, {Michael J.} and Lonny Yarmus and Sofia Molina and Benjamin Young and David Feller-Kopman and Sagar, {Ala Eddin S.} and Thomas Gildea and Debiane, {Labib G.} and Grosu, {Horiana B.} and Casal, {Roberto F.} and Arain, {Muhammad H.} and Eapen, {George A.} and Jimenez, {Carlos A.} and Noor, {Laila Z.} and Shiva Baghaie and Juhee Song and Liang Li and Ost, {David E.}",
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TY - JOUR

T1 - A prediction model to help with oncologic mediastinal evaluation for radiation

T2 - Homer

AU - Martinez-Zayas, Gabriela

AU - Almeida, Francisco A.

AU - Simoff, Michael J.

AU - Yarmus, Lonny

AU - Molina, Sofia

AU - Young, Benjamin

AU - Feller-Kopman, David

AU - Sagar, Ala Eddin S.

AU - Gildea, Thomas

AU - Debiane, Labib G.

AU - Grosu, Horiana B.

AU - Casal, Roberto F.

AU - Arain, Muhammad H.

AU - Eapen, George A.

AU - Jimenez, Carlos A.

AU - Noor, Laila Z.

AU - Baghaie, Shiva

AU - Song, Juhee

AU - Li, Liang

AU - Ost, David E.

PY - 2020/1/15

Y1 - 2020/1/15

N2 - Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2j3) disease is important. Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2j3 disease. Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2j3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals. Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2j3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P, 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34). Conclusions: This prediction model can estimate the probability of N0, N1, and N2j3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

AB - Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2j3) disease is important. Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2j3 disease. Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2j3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals. Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2j3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P, 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34). Conclusions: This prediction model can estimate the probability of N0, N1, and N2j3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

KW - Endobronchial ultrasound

KW - Lung cancer

KW - Lung cancer staging

KW - Mediastinal adenopathy

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