Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer
Abstract Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radio...
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Nature Portfolio
2017
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oai:doaj.org-article:29380d9f555d4f32bed853ac2972c1e02021-12-02T15:05:48ZDelta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer10.1038/s41598-017-00665-z2045-2322https://doaj.org/article/29380d9f555d4f32bed853ac2972c1e02017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00665-zhttps://doaj.org/toc/2045-2322Abstract Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.Xenia FaveLifei ZhangJinzhong YangDennis MackinPeter BalterDaniel GomezDavid FollowillAaron Kyle JonesFrancesco StingoZhongxing LiaoRadhe MohanLaurence CourtNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017) |
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Medicine R Science Q Xenia Fave Lifei Zhang Jinzhong Yang Dennis Mackin Peter Balter Daniel Gomez David Followill Aaron Kyle Jones Francesco Stingo Zhongxing Liao Radhe Mohan Laurence Court Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| description |
Abstract Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response. |
| format |
article |
| author |
Xenia Fave Lifei Zhang Jinzhong Yang Dennis Mackin Peter Balter Daniel Gomez David Followill Aaron Kyle Jones Francesco Stingo Zhongxing Liao Radhe Mohan Laurence Court |
| author_facet |
Xenia Fave Lifei Zhang Jinzhong Yang Dennis Mackin Peter Balter Daniel Gomez David Followill Aaron Kyle Jones Francesco Stingo Zhongxing Liao Radhe Mohan Laurence Court |
| author_sort |
Xenia Fave |
| title |
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| title_short |
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| title_full |
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| title_fullStr |
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| title_full_unstemmed |
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| title_sort |
delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/29380d9f555d4f32bed853ac2972c1e0 |
| work_keys_str_mv |
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