Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival.
OBJECTIVE
Quantification of the CT appearance of non-small cell lung cancer (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 CT images 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.
Institute of Technology and Science, The University of Tokushima, Tokushima, Japan.
Yoshiki
Y
Kawata
Noboru
N
Niki
Hironobu
H
Ohmatsu
Masahiko
M
Kusumoto
Takaaki
T
Tsuchida
Kenji
K
Eguchi
Masahiro
M
Kaneko
Noriyuki
N
Moriyama
D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000401:mortality*; D018572:Disease-Free Survival; D038622:Internationality; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000401:mortality* / Q000473:pathology; D009364:Neoplasm Recurrence, Local / Q000000981:diagnostic imaging* / Q000401:mortality*; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D016019:Survival Analysis; D015996:Survival Rate; D014057:Tomography, X-Ray Computed / Q000379:methods*