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

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer
Pages73-94
Number of pages22
DOIs
Publication statusPublished - 01-01-2020

Publication series

NameAdvances in Experimental Medicine and Biology
Volume1213
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

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All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Teramoto, A., Yamada, A., Tsukamoto, T., Imaizumi, K., Toyama, H., Saito, K., & Fujita, H. (2020). Decision Support System for Lung Cancer Using PET/CT and Microscopic Images. In Advances in Experimental Medicine and Biology (pp. 73-94). (Advances in Experimental Medicine and Biology; Vol. 1213). Springer. https://doi.org/10.1007/978-3-030-33128-3_5