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Lung Cancer. 2010 Dec;70(3):286-94. doi: 10.1016/j.lungcan.2010.03.009. Epub 2010 Apr 14.

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

Author information

1
Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka 565-0871, Japan. m-yanagawa@radiol.med.osaka-u.ac.jp

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 2cm 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.

PMID:
20392516
DOI:
10.1016/j.lungcan.2010.03.009
[Indexed for MEDLINE]

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