Decision Support System for Lung Cancer Using PET/CT and Microscopic Images

Atsushi Teramoto, Ayumi Yamada, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, Hiroshi Toyama, Kuniaki Saito, Hiroshi Fujita

研究成果: 書籍/レポート タイプへの寄稿

13 被引用数 (Scopus)

抄録

Lung cancer is the most common cancer among men and the third most common among women in the world. Many diagnostic techniques have been introduced to diagnose lung cancer. Positron emission tomography (PET)/computed tomography (CT) examination is an image diagnostic method that performs automatic detection and distinction of lung lesions. In addition, pathological examination by biopsy is performed for lesions that are suspected of being malignant, and appropriate treatment methods are applied according to the diagnosis results. Currently, lung cancer diagnosis is performed through coordination between respiratory, radiation, and pathological diagnosis experts, but there are some tasks, such as image diagnosis, that require a large amount of time and effort to complete. Therefore, we developed a decision support system using PET/CT and microscopic images at the time of image diagnosis, which leads to appropriate treatment. In this chapter, we introduce the proposed system using deep learning and radiomic techniques.

本文言語英語
ホスト出版物のタイトルAdvances in Experimental Medicine and Biology
出版社Springer
ページ73-94
ページ数22
DOI
出版ステータス出版済み - 2020

出版物シリーズ

名前Advances in Experimental Medicine and Biology
1213
ISSN(印刷版)0065-2598
ISSN(電子版)2214-8019

All Science Journal Classification (ASJC) codes

  • 生化学、遺伝学、分子生物学一般

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