Noninvasive computed tomography-based risk stratification of lung adenocarcinomas in the national lung screening trial

Fabien Maldonado, Fenghai Duan, Sushravya M. Raghunath, Srinivasan Rajagopalan, Ronald A. Karwoski, Kavita Garg, Erin Greco, Hrudaya Nath, Richard A. Robb, Brian Jack Bartholmai, Tobias D Peikert

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Rationale: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. Objectives: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. Methods: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. Measurements and Main Results: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P,0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. Conclusions: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

Original languageEnglish (US)
Pages (from-to)737-744
Number of pages8
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume192
Issue number6
DOIs
StatePublished - Sep 15 2015

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Tomography
Lung
Disease-Free Survival
Lung Neoplasms
Adenocarcinoma
Survival Analysis
Kaplan-Meier Estimate
Adenocarcinoma of lung
Early Detection of Cancer
Software
Smoking
Mortality
Neoplasms
Therapeutics

Keywords

  • Image analysis
  • Individualized medicine
  • Lung adenocarcinoma
  • Risk stratification

ASJC Scopus subject areas

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

Cite this

Noninvasive computed tomography-based risk stratification of lung adenocarcinomas in the national lung screening trial. / Maldonado, Fabien; Duan, Fenghai; Raghunath, Sushravya M.; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Garg, Kavita; Greco, Erin; Nath, Hrudaya; Robb, Richard A.; Bartholmai, Brian Jack; Peikert, Tobias D.

In: American Journal of Respiratory and Critical Care Medicine, Vol. 192, No. 6, 15.09.2015, p. 737-744.

Research output: Contribution to journalArticle

Maldonado, Fabien ; Duan, Fenghai ; Raghunath, Sushravya M. ; Rajagopalan, Srinivasan ; Karwoski, Ronald A. ; Garg, Kavita ; Greco, Erin ; Nath, Hrudaya ; Robb, Richard A. ; Bartholmai, Brian Jack ; Peikert, Tobias D. / Noninvasive computed tomography-based risk stratification of lung adenocarcinomas in the national lung screening trial. In: American Journal of Respiratory and Critical Care Medicine. 2015 ; Vol. 192, No. 6. pp. 737-744.
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AU - Maldonado, Fabien

AU - Duan, Fenghai

AU - Raghunath, Sushravya M.

AU - Rajagopalan, Srinivasan

AU - Karwoski, Ronald A.

AU - Garg, Kavita

AU - Greco, Erin

AU - Nath, Hrudaya

AU - Robb, Richard A.

AU - Bartholmai, Brian Jack

AU - Peikert, Tobias D

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N2 - Rationale: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. Objectives: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. Methods: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. Measurements and Main Results: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P,0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. Conclusions: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

AB - Rationale: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. Objectives: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. Methods: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. Measurements and Main Results: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P,0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. Conclusions: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

KW - Image analysis

KW - Individualized medicine

KW - Lung adenocarcinoma

KW - Risk stratification

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