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Lv W, Yuan Q, Wang Q, Ma J, Jiang J, Yang W, Feng Q, Chen W, Rahmim A, Lu L. Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT. Eur Radiol 2018. [PMID: 29520429 DOI: 10.1007/s00330-018-5343-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
OBJECTIVES To investigate the impact of parameter settings as used for the generation of radiomics features on their robustness and disease differentiation (nasopharyngeal carcinoma (NPC) versus chronic nasopharyngitis (CN) in FDG PET/CT imaging). METHODS We studied 106 patients (69/37 NPC/CN, pathology confirmed), and extracted 57 radiomics features under different parameter settings. Robustness was assessed by the intra-class correlation coefficient (ICC). Logistic regression with leave-one-out cross validation was used to generate classification probabilities, and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS Varying averaging strategies and symmetry, 4/26 GLCM features showed poor range of pairwise ICCs of 0.02-0.98, while depicting good AUCs of 0.82-0.91. Varying distances, 5/26 GLCM features showed ICCs of 0.82-0.99 while corresponding AUCs were 0.52-0.91. 6/13 GLRLM features showed both high AUC (0.81-0.89) and high ICC (0.85-0.99) regarding to averaging strategies. 7/13 GLSZM features showed AUCs of 0.81-0.90 while having ICCs of 0.01-0.99 under different neighbourhoods. 2/5 NGTDM features showed AUCs of 0.81-0.85 while having ICCs of 0.19-0.89 for different window sizes. Differentiating a subset of NPC (stages I-II) form CN, both SumEntropy and SZLGE achieved significantly higher AUCs than metabolically active tumour volume (AUC: 0.91 vs. 0.72, p<0.01). CONCLUSIONS Radiomics features depicting poor absolute-scale robustness regarding to parameter settings can still lead to good diagnostic performance. As such, robustness of radiomics features should not be overemphasized for removal of features towards assessment of clinical tasks. For differentiating NPC from CN, some radiomics features (e.g. SumEntropy, SZLGE, LGZE) outperformed conventional metrics. KEY POINTS • Poor robustness did not necessarily translate into poor differentiation performance. • Absolute-scale robustness of radiomics features should not be overemphasized. • Radiomics features SumEntropy, SZLGE and LGZE outperformed conventional metrics.
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Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Jianhua Ma
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China.
| | - Jun Jiang
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Wei Yang
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, 601 N. Caroline St, Baltimore, MD, 21287, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, 3101 Wyman Park Drive, Baltimore, MD, 21218, USA
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Baiyun District, Guangzhou, Guangdong, 510515, China.
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