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

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 <0.75); good (0.75 = 0.90). Dependencies of the features with tumor volume were assessed using Spearman's correlation coefficient (rho).

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.