Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: Correlation with pathologic prognostic factors

• Published in: Lung cancer (Amsterdam, Netherlands) Netherlands), 2010-12, Vol.70 (3), p.286-294
• Main Authors: Yanagawa, Masahiro, Tanaka, Yuko, Kusumoto, Masahiko, Watanabe, Shunichi, Tsuchiya, Ryosuke, Honda, Osamu, Sumikawa, Hiromitsu, Inoue, Atsuo, Inoue, Masayoshi, Okumura, Meinoshin, Tomiyama, Noriyuki, Johkoh, Takeshi
• Format: Article
• Language: English
• Subjects: Adenocarcinoma - diagnostic imaging; Adenocarcinoma - pathology; Adenocarcinoma - physiopathology; Automatic Data Processing; Biological and medical sciences; Computed tomography (CT); Cone-Beam Computed Tomography - methods; Disease Progression; Feasibility Studies; Ground-glass opacity; Hematology, Oncology and Palliative Medicine; Humans; Image Processing, Computer-Assisted; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lung Neoplasms - physiopathology; Medical sciences; Pathological prognostic factors; Pneumology; Prognosis; Pulmonary/Respiratory; Small peripheral pulmonary adenocarcinoma; Software Validation; Tumors; Tumors of the respiratory system and mediastinum; Volumetric CT
• ISSN: 0169-5002; 1872-8332
• DOI: 10.1016/j.lungcan.2010.03.009

Abstract Purpose To evaluate a custom-developed software for analyzing malignant degrees of small peripheral adenocarcinomas on volumetric CT data compared to pathological prognostic factors. Materials and methods Forty-six adenocarcinomas with a diameter of 2 cm or less from 46 patients were included. The custom-developed software can calculate the volumetric rates of solid parts to whole nodules even though solid parts show a punctate distribution, and automatically classify nodules into the following six types according to the volumetric rates of solid parts: type 1, pure ground-glass opacity (GGO); type 2, semiconsolidation; type 3, small solid part with a GGO halo; type 4, mixed type with an area that consisted of GGO and solid parts which have air-bronchogram or show a punctate distribution; type 5, large solid part with a GGO halo; and type 6, pure solid type. The boundary between solid portion and GGO on CT was decided using two threshold selection methods for segmenting gray-scale images. A radiologist also examined two-dimensional rates of solid parts to total opacity (2D%solid) which was already confirmed with previous reports. Results There were good agreements between the classification determined by the software and radiologists (weighted kappa = 0.778–0.804). Multivariate logistic regression analyses showed that both 2D%solid and computer-automated classification were significantly useful in estimating lymphatic invasion ( p = 0.0007, 0.0027), vascular invasion ( p = 0.003, 0.012), and pleural invasion ( p = 0.021, 0.025). Conclusion Using our custom-developed software, it is feasible to predict the pathological prognostic factors of small peripheral adenocarcinomas.