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Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

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Title
Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
Author(s)
Hwan-ho Cho; Lee, Ho Yun; Eunjin Kim; Lee, Geewon; Kim, Jonghoon; Junmo Kwon; Hyunjin Park
Publication Date
2021-11-12
Journal
Communications Biology, v.4, no.1
Publisher
Nature Research
Abstract
© 2021, The Author(s).Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.
URI
https://pr.ibs.re.kr/handle/8788114/10867
DOI
10.1038/s42003-021-02814-7
ISSN
2399-3642
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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