Abstract #0054

Impact of retrospective gradient nonlinearity correction on lesion ADCs and performance in the ECOG-ACRIN A6702 multicenter breast DWI trial

Debosmita Biswas¹, Justin Romanoff², Dariya Malyarenko³, Wesley Surrento⁴, Habib Rahbar¹, Nola Hylton⁵, David C Newitt⁵, Thomas L Chenevert³, and Savannah C Partridge¹

¹Radiology, University of Washington, Seattle, WA, United States, ²Center for Statistical Sciences, Brown University, Providence, RI, United States, ³Radiology, University of Michigan, Ann Arbor, MI, United States, ⁴Biomedical and Health Informatics, University of Washington, Seattle, WA, United States, ⁵Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States

Gradient nonlinearity (GNL) correction shows potential to improve the accuracy of ADC values collected across different MRI platforms. Here, we retrospectively applied GNL correction to breast DWI datasets collected in the ECOG-ACRIN A6702 trial by pixel-wise scaling of the ADC map with correction factor map. Our findings confirm that GNL significantly impacts multicenter breast lesion ADC values, and that GNL-based ADC errors vary significantly across MRI vendors and gradient systems. Therefore, GNL correction is important for implementation of generalizable ADC thresholds for separating benign and malignant lesions.

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