Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics
PurposeBy using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1013085/full |
| _version_ | 1797895434146938880 |
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| author | Yunfei Li Xinrui Gao Xuemei Tang Sheng Lin Haowen Pang |
| author_facet | Yunfei Li Xinrui Gao Xuemei Tang Sheng Lin Haowen Pang |
| author_sort | Yunfei Li |
| collection | DOAJ |
| description | PurposeBy using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model.MethodsCT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set.ResultsThree radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85.ConclusionThe automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research. |
| first_indexed | 2024-04-10T07:25:33Z |
| format | Article |
| id | doaj.art-ce9d56abc0d345ccb7dddb1987fb4f21 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T07:25:33Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-ce9d56abc0d345ccb7dddb1987fb4f212023-02-24T07:40:23ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-02-011310.3389/fonc.2023.10130851013085Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomicsYunfei LiXinrui GaoXuemei TangSheng LinHaowen PangPurposeBy using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model.MethodsCT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set.ResultsThree radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85.ConclusionThe automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.https://www.frontiersin.org/articles/10.3389/fonc.2023.1013085/fullcomputed tomography (CT)radiomicskidneykidney tumorautomatic classification |
| spellingShingle | Yunfei Li Xinrui Gao Xuemei Tang Sheng Lin Haowen Pang Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics Frontiers in Oncology computed tomography (CT) radiomics kidney kidney tumor automatic classification |
| title | Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| title_full | Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| title_fullStr | Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| title_full_unstemmed | Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| title_short | Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| title_sort | research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics |
| topic | computed tomography (CT) radiomics kidney kidney tumor automatic classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1013085/full |
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