Hua Wang1, Matthew B Schabath2, Ying Liu1, Anders E Berglund3, Gregory C Bloom3, Jongphil Kim4, Olya Stringfield5, Edward A Eikman6, Donald L Klippenstein6, John J Heine5, Steven A Eschrich3, Zhaoxiang Ye7, Robert J Gillies8. 1. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, PR China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 2. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 3. Department of Biomedical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 4. Department of Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 5. Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 6. Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 7. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, PR China. Electronic address: yezhaoxiang@163.com. 8. Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL; Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. Electronic address: robert.gillies@moffitt.org.
Abstract
UNLABELLED: In this study we developed 25 computed tomography descriptors among 117 patients with lung adenocarcinoma to semiquantitatively assess their association with overall survival. Pleural attachment was significantly associated with an increased risk of death and texture was most important for distinguishing histological subtypes. This approach has the potential to support automated analyses and develop decision-support clinical tools. BACKGROUND: Computed tomography (CT) characteristics derived from noninvasive images that represent the entire tumor might have diagnostic and prognostic value. The purpose of this study was to assess the association of a standardized set of semiquantitative CT characteristics of lung adenocarcinoma with overall survival. PATIENTS AND METHODS: An initial set of CT descriptors was developed to semiquantitatively assess lung adenocarcinoma in patients (n = 117) who underwent resection. Survival analyses were used to determine the association between each characteristic and overall survival. Principle component analysis (PCA) was used to determine characteristics that might differentiate histological subtypes. RESULTS: Characteristics significantly associated with overall survival included pleural attachment (P < .001), air bronchogram (P = .03), and lymphadenopathy (P = .02). Multivariate analyses revealed pleural attachment was significantly associated with an increased risk of death overall (hazard ratio [HR], 3.21; 95% confidence interval [CI], 1.53-6.70) and among patients with lepidic predominant adenocarcinomas (HR, 5.85; 95% CI, 1.75-19.59), and lymphadenopathy was significantly associated with an increased risk of death among patients with adenocarcinomas without a predominant lepidic component (HR, 3.07; 95% CI, 1.09-8.70). A PCA model showed that texture (ground-glass opacity component) was most important for separating the 2 subtypes. CONCLUSION: A subset of the semiquantitative characteristics described herein has prognostic importance and provides the ability to distinguish between different histological subtypes of lung adenocarcinoma.
UNLABELLED: In this study we developed 25 computed tomography descriptors among 117 patients with lung adenocarcinoma to semiquantitatively assess their association with overall survival. Pleural attachment was significantly associated with an increased risk of death and texture was most important for distinguishing histological subtypes. This approach has the potential to support automated analyses and develop decision-support clinical tools. BACKGROUND: Computed tomography (CT) characteristics derived from noninvasive images that represent the entire tumor might have diagnostic and prognostic value. The purpose of this study was to assess the association of a standardized set of semiquantitative CT characteristics of lung adenocarcinoma with overall survival. PATIENTS AND METHODS: An initial set of CT descriptors was developed to semiquantitatively assess lung adenocarcinoma in patients (n = 117) who underwent resection. Survival analyses were used to determine the association between each characteristic and overall survival. Principle component analysis (PCA) was used to determine characteristics that might differentiate histological subtypes. RESULTS: Characteristics significantly associated with overall survival included pleural attachment (P < .001), air bronchogram (P = .03), and lymphadenopathy (P = .02). Multivariate analyses revealed pleural attachment was significantly associated with an increased risk of death overall (hazard ratio [HR], 3.21; 95% confidence interval [CI], 1.53-6.70) and among patients with lepidic predominant adenocarcinomas (HR, 5.85; 95% CI, 1.75-19.59), and lymphadenopathy was significantly associated with an increased risk of death among patients with adenocarcinomas without a predominant lepidic component (HR, 3.07; 95% CI, 1.09-8.70). A PCA model showed that texture (ground-glass opacity component) was most important for separating the 2 subtypes. CONCLUSION: A subset of the semiquantitative characteristics described herein has prognostic importance and provides the ability to distinguish between different histological subtypes of lung adenocarcinoma.
Authors: T Honda; T Kondo; S Murakami; H Saito; F Oshita; H Ito; M Tsuboi; H Nakayama; T Yokose; Y Kameda; T Isobe; K Yamada Journal: Clin Radiol Date: 2012-11-10 Impact factor: 2.350
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919
Authors: Hua Wang; Matthew B Schabath; Ying Liu; Olya Stringfield; Yoganand Balagurunathan; John J Heine; Steven A Eschrich; Zhaoxiang Ye; Robert J Gillies Journal: Clin Lung Cancer Date: 2015-11-12 Impact factor: 4.785
Authors: Juheon Lee; Yi Cui; Xiaoli Sun; Bailiang Li; Jia Wu; Dengwang Li; Michael F Gensheimer; Billy W Loo; Maximilian Diehn; Ruijiang Li Journal: Eur Radiol Date: 2017-08-07 Impact factor: 5.315
Authors: Ying Liu; Yoganand Balagurunathan; Thomas Atwater; Sanja Antic; Qian Li; Ronald C Walker; Gary T Smith; Pierre P Massion; Matthew B Schabath; Robert J Gillies Journal: Clin Cancer Res Date: 2016-09-23 Impact factor: 12.531