Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation
Abstract The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients...
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Nature Portfolio
2021
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oai:doaj.org-article:598a029341804dad921fdd8916fdd6262021-12-02T10:44:08ZDifferentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation10.1038/s41598-021-82467-y2045-2322https://doaj.org/article/598a029341804dad921fdd8916fdd6262021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82467-yhttps://doaj.org/toc/2045-2322Abstract The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN. .Yae Won ParkDongmin ChoiJi Eun ParkSung Soo AhnHwiyoung KimJong Hee ChangSe Hoon KimHo Sung KimSeung-Koo LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
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Medicine R Science Q Yae Won Park Dongmin Choi Ji Eun Park Sung Soo Ahn Hwiyoung Kim Jong Hee Chang Se Hoon Kim Ho Sung Kim Seung-Koo Lee Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| description |
Abstract The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN. . |
| format |
article |
| author |
Yae Won Park Dongmin Choi Ji Eun Park Sung Soo Ahn Hwiyoung Kim Jong Hee Chang Se Hoon Kim Ho Sung Kim Seung-Koo Lee |
| author_facet |
Yae Won Park Dongmin Choi Ji Eun Park Sung Soo Ahn Hwiyoung Kim Jong Hee Chang Se Hoon Kim Ho Sung Kim Seung-Koo Lee |
| author_sort |
Yae Won Park |
| title |
Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| title_short |
Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| title_full |
Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| title_fullStr |
Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| title_full_unstemmed |
Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| title_sort |
differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/598a029341804dad921fdd8916fdd626 |
| work_keys_str_mv |
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