Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival

Abstract

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.

Keywords: CT; Feature; Lepidic growth; Prognosis; Quantitative.

Figure 1

Kaplan–Meier survival curves for pleural attachment (A), air bronchogram (B), and lymphadenopathy (C) of all adenocarcinomas, which are statistically significantly associated with overall survival.

Figure 1

Kaplan–Meier survival curves for pleural attachment (A), air bronchogram (B), and lymphadenopathy (C) of all adenocarcinomas, which are statistically significantly associated with overall survival.

Figure 1

Kaplan–Meier survival curves for pleural attachment (A), air bronchogram (B), and lymphadenopathy (C) of all adenocarcinomas, which are statistically significantly associated with overall survival.

Figure 2

Kaplan–Meier survival curves for size (A), lobulation (B), and thickened adjacent bronchovascular bundle (C) of all adenocarcinomas, which are suggestive to be associated with overall survival.

Figure 2

Kaplan–Meier survival curves for size (A), lobulation (B), and thickened adjacent bronchovascular bundle (C) of all adenocarcinomas, which are suggestive to be associated with overall survival.

Figure 2

Kaplan–Meier survival curves for size (A), lobulation (B), and thickened adjacent bronchovascular bundle (C) of all adenocarcinomas, which are suggestive to be associated with overall survival.

Figure 3

Decision tree (A) and Kaplan–Meier survival curves (B) based on CART analyses for the three statistically significant characteristics in Figure 1.

Figure 3

Decision tree (A) and Kaplan–Meier survival curves (B) based on CART analyses for the three statistically significant characteristics in Figure 1.

Figure 4

PCA Plot. The score plot (A) shows a separation between the different subtypes: lepidic predominant adenocarcinomas (green squares), adenocarcinomas without a lepidic component (blue circles), and adenocarcinomas with a small proportion of a lepidic component (orange triangles). The loading plot (B) shows how the characteristics are related to each other and which characteristics separate the subtypes. Characteristics related to tumor are represented by blue circles, surrounding tissues by yellow squares, and associated findings by red triangles.

Figure 4

PCA Plot. The score plot (A) shows a separation between the different subtypes: lepidic predominant adenocarcinomas (green squares), adenocarcinomas without a lepidic component (blue circles), and adenocarcinomas with a small proportion of a lepidic component (orange triangles). The loading plot (B) shows how the characteristics are related to each other and which characteristics separate the subtypes. Characteristics related to tumor are represented by blue circles, surrounding tissues by yellow squares, and associated findings by red triangles.