A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly

Autor: Mudan Zhang, Xuntao Yin, Wuchao Li, Yan Zha, Xianchun Zeng, Xiaoyong Zhang, Jingjing Cui, Zhong Xue, Rongpin Wang, Chen Liu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: BMC Medical Imaging, Vol 23, Iss 1, Pp 1-13 (2023)
Druh dokumentu: article
ISSN: 1471-2342
DOI: 10.1186/s12880-023-01145-9
Popis: 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
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