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  1. 医学医療系 (Faculty of Medicine)
  2. 野口 雅之 (Noguchi Masayuki)
  1. コンテンツタイプ (Contents Type)
  2. 雑誌発表論文等 (Journal article, etc.)
  3. M~
  4. Medicine

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma

http://hdl.handle.net/2241/00159155
d611087f-7e0f-45b1-8aca-2777e6757fc8
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Medicine_98-25.pdf Medicine_98-25 (1.1 MB)
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item type Journal Article(1)
公開日 2019-12-12
タイトル
タイトル Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma
言語
言語 eng
資源タイプ
タイプ journal article
著者 野口, 雅之

× 野口, 雅之

WEKO 124641
e-Rad 00198582
筑波大学研究者総覧 0000001620

野口, 雅之

ja-Kana ノグチ, マサユキ

en NOGUCHI, Masayuki

Search repository
Yanagawa, Masahiro

× Yanagawa, Masahiro

WEKO 216716

en Yanagawa, Masahiro

Search repository
Niioka, Hirohiko

× Niioka, Hirohiko

WEKO 216717

en Niioka, Hirohiko

Search repository
Hata, Akinori

× Hata, Akinori

WEKO 216718

en Hata, Akinori

Search repository
Kikuchi, Noriko

× Kikuchi, Noriko

WEKO 216719

en Kikuchi, Noriko

Search repository
Honda, Osamu

× Honda, Osamu

WEKO 216720

en Honda, Osamu

Search repository
Kurakami, Hiroyuki

× Kurakami, Hiroyuki

WEKO 216721

en Kurakami, Hiroyuki

Search repository
Morii, Eiichi

× Morii, Eiichi

WEKO 216722

en Morii, Eiichi

Search repository
Watanabe, Yoshiyuki

× Watanabe, Yoshiyuki

WEKO 216723

en Watanabe, Yoshiyuki

Search repository
Miyake, Jun

× Miyake, Jun

WEKO 216724

en Miyake, Jun

Search repository
Tomiyama, Noriyuki

× Tomiyama, Noriyuki

WEKO 216725

en Tomiyama, Noriyuki

Search repository
抄録
内容記述 To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.

Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.

No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P>. 11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P=. 98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P=. 0005), but significantly superior specificity (P=. 02).

Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.
書誌情報 Medicine

巻 98, 号 25, p. e16119, 発行日 2019-06
ISSN
収録物識別子 0025-7974
PubMed番号
DOI
権利
権利情報 © 2019 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NCND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
著者版フラグ
値 publisher
出版者
出版者 Wolters Kluwer Health, Inc.
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