Fabien Maldonado1, Fenghai Duan2, Sushravya M Raghunath3,4, Srinivasan Rajagopalan3, Ronald A Karwoski3, Kavita Garg5, Erin Greco2, Hrudaya Nath6, Richard A Robb3, Brian J Bartholmai4, Tobias Peikert1. 1. 1 Division of Pulmonary and Critical Care Medicine. 2. 2 Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island. 3. 3 Department of Physiology and Biomedical Engineering, and. 4. 4 Department of Radiology, Mayo Clinic, Rochester, Minnesota. 5. 5 Department of Radiology, University of Colorado, Denver, Colorado; and. 6. 6 Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
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.
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.
Authors: Denise R Aberle; Christine D Berg; William C Black; Timothy R Church; Richard M Fagerstrom; Barbara Galen; Ilana F Gareen; Constantine Gatsonis; Jonathan Goldin; John K Gohagan; Bruce Hillman; Carl Jaffe; Barnett S Kramer; David Lynch; Pamela M Marcus; Mitchell Schnall; Daniel C Sullivan; Dorothy Sullivan; Carl J Zylak Journal: Radiology Date: 2010-11-02 Impact factor: 11.105
Authors: William D Travis; Elisabeth Brambilla; Masayuki Noguchi; Andrew G Nicholson; Kim Geisinger; Yasushi Yatabe; Charles A Powell; David Beer; Greg Riely; Kavita Garg; John H M Austin; Valerie W Rusch; Fred R Hirsch; James Jett; Pan-Chyr Yang; Michael Gould Journal: Proc Am Thorac Soc Date: 2011-09
Authors: P E Van Schil; H Asamura; V W Rusch; T Mitsudomi; M Tsuboi; E Brambilla; W D Travis Journal: Eur Respir J Date: 2011-08-04 Impact factor: 16.671
Authors: Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks Journal: N Engl J Med Date: 2011-06-29 Impact factor: 91.245
Authors: William D Travis; Elisabeth Brambilla; Masayuki Noguchi; Andrew G Nicholson; Kim R Geisinger; Yasushi Yatabe; David G Beer; Charles A Powell; Gregory J Riely; Paul E Van Schil; Kavita Garg; John H M Austin; Hisao Asamura; Valerie W Rusch; Fred R Hirsch; Giorgio Scagliotti; Tetsuya Mitsudomi; Rudolf M Huber; Yuichi Ishikawa; James Jett; Montserrat Sanchez-Cespedes; Jean-Paul Sculier; Takashi Takahashi; Masahiro Tsuboi; Johan Vansteenkiste; Ignacio Wistuba; Pan-Chyr Yang; Denise Aberle; Christian Brambilla; Douglas Flieder; Wilbur Franklin; Adi Gazdar; Michael Gould; Philip Hasleton; Douglas Henderson; Bruce Johnson; David Johnson; Keith Kerr; Keiko Kuriyama; Jin Soo Lee; Vincent A Miller; Iver Petersen; Victor Roggli; Rafael Rosell; Nagahiro Saijo; Erik Thunnissen; Ming Tsao; David Yankelewitz Journal: J Thorac Oncol Date: 2011-02 Impact factor: 15.609
Authors: Ted Way; Heang-Ping Chan; Lubomir Hadjiiski; Berkman Sahiner; Aamer Chughtai; Thomas K Song; Chad Poopat; Jadranka Stojanovska; Luba Frank; Anil Attili; Naama Bogot; Philip N Cascade; Ella A Kazerooni Journal: Acad Radiol Date: 2010-03 Impact factor: 3.173
Authors: Justus E Roos; David Paik; David Olsen; Emily G Liu; Lawrence C Chow; Ann N Leung; Robert Mindelzun; Kingshuk R Choudhury; David P Naidich; Sandy Napel; Geoffrey D Rubin Journal: Eur Radiol Date: 2009-09-16 Impact factor: 5.315
Authors: Benedikt H Heidinger; Kevin R Anderson; Ursula Nemec; Daniel B Costa; Sidhu P Gangadharan; Paul A VanderLaan; Alexander A Bankier Journal: J Thorac Dis Date: 2017-12 Impact factor: 2.895
Authors: Finbar Foley; Srinivasan Rajagopalan; Sushravya M Raghunath; Jennifer M Boland; Ronald A Karwoski; Fabien Maldonado; Brian J Bartholmai; Tobias Peikert Journal: Semin Thorac Cardiovasc Surg Date: 2016-01-08