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

Abstract

PurposeThe 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 methodsTwo 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). ResultsThe 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. ConclusionApplying 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.