Shin-Hyung Park1,2, Hyejin Lim3,4, Bong Kyung Bae3, Myong Hun Hahm5, Gun Oh Chong6,7,8, Shin Young Jeong9, Jae-Chul Kim3. 1. Department of Radiation Oncology, School of Medicine, Kyungpook National University Hospital, 130 Dongduk-Ro, Jung-Gu, Daegu, 41944, Republic of Korea. shinhyungpark@knu.ac.kr. 2. Cardiovascular Research Institute, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. shinhyungpark@knu.ac.kr. 3. Department of Radiation Oncology, School of Medicine, Kyungpook National University Hospital, 130 Dongduk-Ro, Jung-Gu, Daegu, 41944, Republic of Korea. 4. Cardiovascular Research Institute, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. 5. Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. 6. Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. 7. Department of Obstetrics and Gynecology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea. 8. Clinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. 9. Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
Abstract
BACKGROUND: Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer. METHODS: This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I). RESULTS: After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness. CONCLUSION: Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.
BACKGROUND: Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer. METHODS: This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I). RESULTS: After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness. CONCLUSION: Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.
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