Quantitative Computed Tomography Features for Predicting Tumor Recurrence in Patients with Surgically Resected Adenocarcinoma of the Lung

Hyun Jung Koo, Yu Sub Sung, Woo Hyun Shim, Hai Xu, Chang-Min Choi, Hyeong Ryul Kim, Jung Bok Lee and Mi Young Kim

PLOS ONE, 2017, vol. 12, issue 1, 1-14

Abstract: Purpose: The purpose of this study was to determine if preoperative quantitative computed tomography (CT) features including texture and histogram analysis measurements are associated with tumor recurrence in patients with surgically resected adenocarcinoma of the lung. Methods: The study included 194 patients with surgically resected lung adenocarcinoma who underwent preoperative CT between January 2013 and December 2013. Quantitative CT feature analysis of the lung adenocarcinomas were performed using in-house software based on plug-in package for ImageJ. Ten quantitative features demonstrating the tumor size, attenuation, shape and texture were extracted. The CT parameters obtained from 1-mm and 5-mm data were compared using intraclass correlation coefficients. Univariate and multivariable logistic regression methods were used to investigate the association between tumor recurrence and preoperative CT findings. Results: The 1-mm and 5-mm data were highly correlated in terms of diameter, perimeter, area, mean attenuation and entropy. Circularity and aspect ratio were moderately correlated. However, skewness and kurtosis were poorly correlated. Multivariable logistic regression analysis revealed that area (odds ratio [OR], 1.002 for each 1-mm2 increase; P = 0.003) and mean attenuation (OR, 1.005 for each 1.0-Hounsfield unit increase; P = 0.022) were independently associated with recurrence. The receiver operating curves using these two independent predictive factors showed high diagnostic performance in predicting recurrence (C-index = 0.81, respectively). Conclusion: Tumor area and mean attenuation are independently associated with recurrence in patients with surgically resected adenocarcinoma of the lung.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0167955

DOI: 10.1371/journal.pone.0167955

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