Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients. (February 2020)

• Record Type: Journal Article
• Authors: Traverso, Alberto; Kazmierski, Michal; Welch, Mattea L.; Weiss, Jessica; Fiset, Sandra; Foltz, Warren D.; Gladwish, Adam; Dekker, Andre; Jaffray, David; Wee, Leonard; Han, Kathy
• Is Part Of: Radiotherapy and oncology. Volume 143(2020)
• Page Start: 88
• Page End: 94
• Publication Date: 2020-02
• Subjects: Radiomics -- MRI -- Apparent Diffusion Coefficient -- Cervical cancer -- Reproducibility; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2019.08.008
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: This study examined the stability of radiomic features in ADC maps of cervix cancer patients. Interobserver variability in contouring and image pre-processing (normalization/quantization) affected feature reproducibility. Urine-based normalization with fixed-bin width quantization approach was the most reproducible. 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 pre-treatment 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 < 0.75); good (0.75 ≤ ICC ≤ 0.89), and excellent (ICC ≥ 0.90). Dependencies of the features with tumor volume were assessed using Spearman's correlation coefficient ( ρ ). 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 ( ρ ≥ 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.