Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

Abstract Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, w...

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Bibliographic Details
Main Authors: Yuto Sugai, Noriyuki Kadoya, Shohei Tanaka, Shunpei Tanabe, Mariko Umeda, Takaya Yamamoto, Kazuya Takeda, Suguru Dobashi, Haruna Ohashi, Ken Takeda, Keiichi Jingu
Format: Article
Language:English
Published: BMC 2021-04-01
Series:Radiation Oncology
Subjects:
Radiomics
Prognosis prediction
Feature selection
Subgroup analysis
Lung cancer
Online Access:https://doi.org/10.1186/s13014-021-01810-9

Internet

https://doi.org/10.1186/s13014-021-01810-9

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