Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.
Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Olya
O
Grove
Department of Biomedical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Anders E
AE
Berglund
Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Matthew B
MB
Schabath
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
Hugo J W L
HJ
Aerts
Department of Radiation Oncology, MAASTRO clinic, Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands.
Andre
A
Dekker
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America; Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Hua
H
Wang
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
Emmanuel Rios
ER
Velazquez
Department of Radiation Oncology, MAASTRO clinic, Research Institute GROW, Maastricht University, 6229ET Maastricht, The Netherlands.
Philippe
P
Lambin
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Yuhua
Y
Gu
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Yoganand
Y
Balagurunathan
Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Edward
E
Eikman
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America; Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Robert A
RA
Gatenby
Department of Biomedical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Steven
S
Eschrich
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America; Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States of America.
Robert J
RJ
Gillies
D000230:Adenocarcinoma / Q000000981:diagnostic imaging* / Q000473:pathology*; D000077192:Adenocarcinoma of Lung; D000368:Aged; D019277:Entropy; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed