Quantification of the CT appearance of non-small cell lungcancer(NSCLC) is of interest in a number of clinical and investigational applications. The purpose of this work is to present a quantitative five-category (α, β, γ, δ, and ɛ) classification method based on CT histogram analysis of NSCLC and to determine the prognostic value of this quantitative classification.Methods:
Institutional review board approval and informed consent were obtained at the National Cancer Center Hospital. A total of 454 patients with NSCLC (maximum lesion size of 3 cm) were enrolled. Each lesion was measured using multidetector CT at the same tube voltage, reconstruction interval, beam collimation, and reconstructed slice thickness. Two observers segmented NSCLC nodules from the CTimages by using a semi-automated three-dimensional technique. The two observers classified NSCLCs into one of five categories from the visual assessment of CT histograms obtained from each nodule segmentation result. Interobserver variability in the classification was computed with Cohen’s κ statistic. Any disagreements were resolved by consensus between the two observers to define the gold standard of the classification. Using a classification and regression tree (CART), the authors obtained a decision tree for a quantitative five-category classification. To assess the impact of the nodule segmentation on the classification, the variability in classifications obtained by two decision trees for the nodule segmentation results was also calculated with the Cohen’s κ statistic. The authors calculated the association of recurrence with prognostic factors including classification, sex, age, tumor diameter, smoking status, disease stage, histological type, lymphatic permeation, and vascular invasion using both univariate and multivariate Cox regression analyses.Results:
The κ values for interobserver agreement of the classification using two nodule segmentation results were 0.921 (P < 0.001) and 0.903 (P < 0.001), respectively. The κ values for the variability in the classification task using two decision trees were 0.981 (P < 0.001) and 0.981 (P < 0.001), respectively. All the NSCLCs were classified into one of five categories (type α, n = 8; type β, n = 38; type γ, n = 103; type δ, n = 112; type ɛ, n = 193) by using a decision tree. Using a multivariate Cox regression analysis, the classification (hazard ratio 5.64; P = 0.008) and disease stage (hazard ratio 8.33; P < 0.001) were identified as being associated with an increased recurrence risk.Conclusions:
The quantitative five-category classifier presented here has the potential to provide an objective classification of NSCLC nodules that is strongly correlated with prognostic factors.
This study was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (21103005) and a Grant-in-Aid for Scientific Research (C) (23500518).
II. MATERIALS AND METHODS
II.A. Patient data collection
II.C. Nodule Segmentation
II.D. Five-category classification of NSCLCs based on CT histogram analysis
II.E. Quantitative five-category classification using a decision tree
II.F. Interobserver variability in the classification task
II.G. Impact of nodule segmentation on the classification
II.H. Impact of nodule-size on repeatability of the segmentation task and repeatability of the classification task
II.I. Effect of the classification on recurrence-free survival
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