Variability and bias in the calculation of iodine uptake with an adaptive background subtraction method in...

Objective.This work evaluates variability and bias in the calculation of iodine uptake from contrast-enhanced dual-energy mammography (CEM) images using an adaptive background subtraction method.Approach.The iodine uptake images were custom-generated using weighted subtraction, followed by subtraction of a smooth background estimated by interpolation from neighboring regions outside the lesion (adaptive background subtraction). Three main sources of variability and bias were assessed: differences in breast tissue linear attenuation coefficients, stability of the mammography system, and intra- and inter-observer variability in lesion segmentation. Tissue-related effects were evaluated theoretically. System stability was assessed using weekly phantom measurements acquired over a 4-year period. For observer variability, four radiologists performed two segmentation rounds of iodine-enhancing regions in 40 CEM studies. Typical uptake values were below 5 mg cm^-2. Whenever possible, variability was expressed as the 95% coverage interval (2σ), where σ represents the standard deviation. Main results.Theoretical evaluation showed that tissue composition differences may introduce biases in iodine uptake exceeding 0.5 mg cm^-2, although typical lesion compositions produce biases below the visual detection threshold. Long-term phantom measurements demonstrated excellent system stability over four years (2σ ≈0.2 mg cm^-2, and limit of detection = 0.24 mg cm^-2), with a 7% change in calibration following a workstation upgrade. In the clinical dataset, the four radiologists achieved high detection agreement when segmenting the primary lesions and greater variability for secondary lesions; the inter-observer variability in mean uptake was approximately 2σ ≈0.6 mg cm^-2.Significance.These findings indicate that iodine uptake quantification in CEM using an adaptive background subtraction method is technically robust, which is a key element for exploring its potential usefulness as an imaging biomarker.