NDLI: Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors

Content Provider	Springer Nature : BioMed Central
Author	Yu, Chunhai Li, Ting Yang, Xiaotang Zhang, Ruiping Xin, Lei Zhao, Zhikai Cui, Jingjing
Abstract	Background To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs). Methods A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses. Results Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT, HRT, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set. Conclusions CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-022-00768-8.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-022-00768-8
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2022-03-06
Access Restriction	Open
Subject Keyword	Imaging Radiology Radiomics Computed tomography Thymoma Thymic carcinoma
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

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