RSNA 2014 

Abstract Archives of the RSNA, 2014


CHE013-b

What are Essential Imaging Findings and Clinical Information for the Diagnosis of Lung Nodules? An Analysis Based on a Large Database of Thin-slice CT Image

Education Exhibits

Presented in 2014

Participants

Takeshi Kubo MD, Abstract Co-Author: Nothing to Disclose
Gakuto Aoyama, Presenter: Employee, Canon Inc
Ryo Sakamoto MD,PhD, Abstract Co-Author: Nothing to Disclose
Masahiro Yakami MD, PhD, Abstract Co-Author: Nothing to Disclose
Koji Fujimoto MD, PhD, Abstract Co-Author: Nothing to Disclose
Kaori Togashi MD, PhD, Abstract Co-Author: Research Grant, Bayer AG Research Grant, DAIICHI SANKYO Group Research Grant, Eisai Co, Ltd Research Grant, FUJIFILM Holdings Corporation Research Grant, Nihon Medi-Physics Co, Ltd Research Grant, Shimadzu Corporation Research Grant, Toshiba Corporation Research Grant, Covidien AG
Yutaka Emoto MD, PhD, Abstract Co-Author: Nothing to Disclose
Hiroyuki Sekiguchi, Abstract Co-Author: Nothing to Disclose
Koji Sakai, Abstract Co-Author: Nothing to Disclose
Masami Kawagishi, Abstract Co-Author: Employee, Canon Inc
Yoshio Iizuka, Abstract Co-Author: Employee, Canon Inc
Keita Nakagomi MSc, Abstract Co-Author: Employee, Canon Inc
Bin Chen, Abstract Co-Author: Employee, Canon Inc
Hiroyuki Yamamoto, Abstract Co-Author: Employee, Canon Inc

TEACHING POINTS

The purpose of this exhibit are; To demonstrate essential clinical information for the diagnosis of lung nodule To describe the distribution of imaging findings observed in primary lung cancer, metastasis and benign nodule on thin-slice CT The knowledge of these nodule characteristics will enhance diagnostic performance of radiologists.

TABLE OF CONTENTS/OUTLINE

We will present the distribution of well-known imaging findings and clinical information for interpreting lung nodules with figures of representative nodule. The distribution was derived from a large data base constructed by the following method; One thousand nodules with pathologically or clinically confirmed diagnoses (primary lung cancer or metastasis or benign) were collected. For each nodule, 37 types of clinical information (e.g. age, sex, smoking history, laboratory data, medical history, etc.) were retrospectively collected. For each nodule, 51 imaging findings (e.g. shape, spiculation, ratio of GGO, air bronchogram, etc.) were scored based on 2 to 7 point scales by the consensus of two board-certified radiologists. As an extra analysis, distributions of both imaging findings and clinical information for solid adenocarcinoma and squamous cell carcinoma were also compared for the better interpretation of solid lung nodules.  

PDF UPLOAD

http://abstract.rsna.org/uploads/2014/14012524/14012524_1q4u.pdf

Cite This Abstract

Kubo, T, Aoyama, G, Sakamoto, R, Yakami, M, Fujimoto, K, Togashi, K, Emoto, Y, Sekiguchi, H, Sakai, K, Kawagishi, M, Iizuka, Y, Nakagomi, K, Chen, B, Yamamoto, H, What are Essential Imaging Findings and Clinical Information for the Diagnosis of Lung Nodules? An Analysis Based on a Large Database of Thin-slice CT Image.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14012524.html