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Sun Y, Wang S, Liu Z, You C, Li R, Mao N, Duan S, Lynn HS, Gu Y. Identifying factors that may influence the classification performance of radiomics models using contrast-enhanced mammography (CEM) images. Cancer Imaging 2022; 22:22. [PMID: 35550658 PMCID: PMC9101829 DOI: 10.1186/s40644-022-00460-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background Radiomics plays an important role in the field of oncology. Few studies have focused on the identification of factors that may influence the classification performance of radiomics models. The goal of this study was to use contrast-enhanced mammography (CEM) images to identify factors that may potentially influence the performance of radiomics models in diagnosing breast lesions. Methods A total of 157 women with 161 breast lesions were included. Least absolute shrinkage and selection operator (LASSO) regression and the random forest (RF) algorithm were employed to construct radiomics models. The classification result for each lesion was obtained by using 100 rounds of five-fold cross-validation. The image features interpreted by the radiologists were used in the exploratory factor analyses. Univariate and multivariate analyses were performed to determine the association between the image features and misclassification. Additional exploratory analyses were performed to examine the findings. Results Among the lesions misclassified by both LASSO and RF ≥ 20% of the iterations in the cross-validation and those misclassified by both algorithms ≤5% of the iterations, univariate analysis showed that larger lesion size and the presence of rim artifacts and/or ripple artifacts were associated with more misclassifications among benign lesions, and smaller lesion size was associated with more misclassifications among malignant lesions (all p < 0.050). Multivariate analysis showed that smaller lesion size (odds ratio [OR] = 0.699, p = 0.002) and the presence of air trapping artifacts (OR = 35.568, p = 0.025) were factors that may lead to misclassification among malignant lesions. Additional exploratory analyses showed that benign lesions with rim artifacts and small malignant lesions (< 20 mm) with air trapping artifacts were misclassified by approximately 50% more in rate compared with benign and malignant lesions without these factors. Conclusions Lesion size and artifacts in CEM images may affect the diagnostic performance of radiomics models. The classification results for lesions presenting with certain factors may be less reliable. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00460-8.
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Affiliation(s)
- Yuqi Sun
- Department of Biostatistics, Key Laboratory on Public Health Safety of the Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Simin Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, No. 270 Dongan Road, Shanghai, 200032, China
| | - Ziang Liu
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, No. 270 Dongan Road, Shanghai, 200032, China
| | - Ruimin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Road, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, No. 270 Dongan Road, Shanghai, 200032, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Shandong, 264000, China
| | - Shaofeng Duan
- GE Healthcare China, No. 1 Huatuo Road, Shanghai, 210000, China
| | - Henry S Lynn
- Department of Biostatistics, Key Laboratory on Public Health Safety of the Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Road, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, No. 270 Dongan Road, Shanghai, 200032, China.
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