1. Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients
- Author
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Mattea Welch, Andre Dekker, Kathy Han, Warren D. Foltz, Michal Kazmierski, David A. Jaffray, Leonard Wee, Jessica Weiss, Sandra Fiset, Alberto Traverso, Adam Gladwish, Radiotherapie, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, and RS: FSE BISS
- Subjects
Intraclass correlation ,Feature extraction ,Normalization (image processing) ,Uterine Cervical Neoplasms ,Bin ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,REPRODUCIBILITY ,Image Processing, Computer-Assisted ,medicine ,Humans ,Effective diffusion coefficient ,Radiology, Nuclear Medicine and imaging ,Quantization (image processing) ,MRI ACQUISITION ,Mathematics ,Observer Variation ,Reproducibility ,Radiomics ,medicine.diagnostic_test ,business.industry ,Apparent Diffusion Coefficient ,Reproducibility of Results ,Magnetic resonance imaging ,Pattern recognition ,Hematology ,Diffusion Magnetic Resonance Imaging ,Oncology ,030220 oncology & carcinogenesis ,RELIABILITY ,Cervical cancer ,Female ,Artificial intelligence ,business ,MRI - Abstract
Purpose: The aims of this study are to evaluate the stability of radiomic features from Apparent Diffusion Coefficient (ADC) maps of cervical cancer with respect to: (1) reproducibility in inter-observer delineation, and (2) image pre-processing (normalization/quantization) prior to feature extraction.Materials and methods: Two observers manually delineated the tumor on ADC maps derived from pretreatment diffusion-weighted Magnetic Resonance imaging of 81 patients with FIGO stage IB-IVA cervical cancer. First-order, shape, and texture features were extracted from the original and filtered images considering 5 different normalizations (four taken from the available literature, and one based on urine ADC) and two different quantization techniques (fixed-bin widths from 0.05 to 25, and fixed-bin count). Stability of radiomic features was assessed using intraclass correlation coefficient (ICC): poor (ICC Results: The approach using urine-normalized values together with a smaller bin width (0.05) was the most reproducible (428/552, 78% features with ICC >= 0.75); the fixed-bin count approach was the least (215/552, 39% with ICC >= 0.75). Without normalization, using a fixed bin width of 25, 348/552 (63%) of features had an ICC >= 0.75. Overall, 26% (range 25-30%) of the features were volume-dependent (rho >= 0.6). None of the volume-independent shape features were found to be reproducible.Conclusion: Applying normalization prior to features extraction increases the reproducibility of ADC-based radiomics features. When normalization is applied, a fixed-bin width approach with smaller widths is suggested. (C) 2019 The Author(s). Published by Elsevier B.V.
- Published
- 2020
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