A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly
Abstract Background The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in pr...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-023-01145-9 |
| _version_ | 1797629877563686912 |
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| author | Mudan Zhang Xuntao Yin Wuchao Li Yan Zha Xianchun Zeng Xiaoyong Zhang Jingjing Cui Zhong Xue Rongpin Wang Chen Liu |
| author_facet | Mudan Zhang Xuntao Yin Wuchao Li Yan Zha Xianchun Zeng Xiaoyong Zhang Jingjing Cui Zhong Xue Rongpin Wang Chen Liu |
| author_sort | Mudan Zhang |
| collection | DOAJ |
| description | Abstract Background The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. Methods A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. Results The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. Conclusions Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19. |
| first_indexed | 2024-03-11T10:59:58Z |
| format | Article |
| id | doaj.art-34836178661c436eaa0f50b67726eeb8 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-11T10:59:58Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-34836178661c436eaa0f50b67726eeb82023-11-12T12:33:31ZengBMCBMC Medical Imaging1471-23422023-11-0123111310.1186/s12880-023-01145-9A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairlyMudan Zhang0Xuntao Yin1Wuchao Li2Yan Zha3Xianchun Zeng4Xiaoyong Zhang5Jingjing Cui6Zhong Xue7Rongpin Wang8Chen Liu9Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s HospitalDepartment of Radiology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical UniversityDepartment of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s HospitalDepartment of Nephrology, Guizhou Provincial People’s HospitalDepartment of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s HospitalDepartment of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s HospitalShanghai United Imaging Intelligence, Co., LtdShanghai United Imaging Intelligence, Co., LtdDepartment of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s HospitalDepartment of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University)Abstract Background The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. Methods A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. Results The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. Conclusions Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.https://doi.org/10.1186/s12880-023-01145-9Adrenal glandPeriadrenal fatAuto-segmentationCOVID-19Radiomics |
| spellingShingle | Mudan Zhang Xuntao Yin Wuchao Li Yan Zha Xianchun Zeng Xiaoyong Zhang Jingjing Cui Zhong Xue Rongpin Wang Chen Liu A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly BMC Medical Imaging Adrenal gland Periadrenal fat Auto-segmentation COVID-19 Radiomics |
| title | A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly |
| title_full | A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly |
| title_fullStr | A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly |
| title_full_unstemmed | A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly |
| title_short | A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly |
| title_sort | radiomics based approach using adrenal gland and periadrenal fat ct images to allocate covid 19 health care resources fairly |
| topic | Adrenal gland Periadrenal fat Auto-segmentation COVID-19 Radiomics |
| url | https://doi.org/10.1186/s12880-023-01145-9 |
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