Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing.
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| Title: | Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing. |
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| Authors: | Li, Ming-De1 (AUTHOR), Cheng, Mei-Qing1 (AUTHOR), Chen, Li-Da1 (AUTHOR), Hu, Hang-Tong1 (AUTHOR), Zhang, Jian-Chao1 (AUTHOR), Ruan, Si-Min1 (AUTHOR), Huang, Hui1 (AUTHOR), Kuang, Ming1,2 (AUTHOR), Lu, Ming-De1,2 (AUTHOR), Li, Wei1 (AUTHOR) liwei259@mail.sysu.edu.cn, Wang, Wei1 (AUTHOR) wangw73@mail.sysu.edu.cn |
| Source: | European Radiology. Sep2022, Vol. 32 Issue 9, p5843-5851. 9p. 1 Color Photograph, 3 Charts, 3 Graphs. |
| Abstract: | Objectives: To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing. Materials and methods: A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms. Reproducible ultrasomics features were selected based on coefficients of variation. Results: A total of 108 US images from three scanners were obtained; 5253 ultrasomics features including seven categories of features were extracted and evaluated for each US image. From intra-US machine analysis, 37.0–38.8% of features showed good reproducibility. From inter-US machine analysis, 42.8% (2248/5253) of features exhibited good reproducibility. From segmentation location analysis, 55.7–57.6% of features showed good reproducibility. No significant difference in the normalized feature ranges was found between the 100 features extracted by the Ultrasomics and PyRadiomics platforms with the same algorithm (p = 0.563). A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed, most of which were wavelet and shearlet features. Conclusions: Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. Wavelet and shearlet features showed the best reproducibility across all procedures. Key Points: • Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. • A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed. • Wavelet and shearlet features showed the best reproducibility across all procedures. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Objectives: To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing. Materials and methods: A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms. Reproducible ultrasomics features were selected based on coefficients of variation. Results: A total of 108 US images from three scanners were obtained; 5253 ultrasomics features including seven categories of features were extracted and evaluated for each US image. From intra-US machine analysis, 37.0–38.8% of features showed good reproducibility. From inter-US machine analysis, 42.8% (2248/5253) of features exhibited good reproducibility. From segmentation location analysis, 55.7–57.6% of features showed good reproducibility. No significant difference in the normalized feature ranges was found between the 100 features extracted by the Ultrasomics and PyRadiomics platforms with the same algorithm (p = 0.563). A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed, most of which were wavelet and shearlet features. Conclusions: Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. Wavelet and shearlet features showed the best reproducibility across all procedures. Key Points: • Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. • A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed. • Wavelet and shearlet features showed the best reproducibility across all procedures. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09387994 |
| DOI: | 10.1007/s00330-022-08662-1 |
