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Proceedings Paper

Prognostic importance of pleural attachment status measured by pretreatment CT images in patients with stage IA lung adenocarcinoma: measurement of the ratio of the interface between nodule and neighboring pleura to nodule surface area
Author(s): Y. Kawata; N. Niki; M. Kusumoto; H. Ohmatsu; K. Aokage; G. Ishii; Y. Matsumoto; T. Tsuchida; K. Eguchi; M. Kaneko
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Paper Abstract

Screening for lung cancer with low-dose computed tomography (CT) has led to increased recognition of small lung cancers and is expected to increase the rate of detection of early-stage lung cancer. Major concerns in the implementation of the CT screening of large populations include determining the appropriate management of pulmonary nodules found on a scan. The identification of patients with early-stage lung cancer who have a higher risk for relapse and who require more aggressive surveillance has been a target of intense investigation. This study was performed to investigate whether image features of internal intensity in combination with surrounding structure characteristics are associated with an increased risk of relapse in patients with stage IA lung adenocarcinoma. We focused on pleural attachment status which is one of morphological characteristics associated with prognosis in three-dimensional thoracic CT images.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751G (27 February 2018); doi: 10.1117/12.2293468
Show Author Affiliations
Y. Kawata, Tokushima Univ. (Japan)
N. Niki, Tokushima Univ. (Japan)
M. Kusumoto, National Cancer Ctr. Hospital East (Japan)
H. Ohmatsu, Abashiri Prison (Japan)
K. Aokage, National Cancer Ctr. Hospital East (Japan)
G. Ishii, National Cancer Ctr. (Japan)
Y. Matsumoto, National Cancer Ctr. Hospital (Japan)
T. Tsuchida, National Cancer Ctr. Hospital (Japan)
K. Eguchi, Teikyou Univ. School of Medicine (Japan)
M. Kaneko, Tokyo Health Service Association (Japan)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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