Abstract: Object Detection for Breast Diffusion-weighted Imaging

Diffusion-weighted imaging (DWI) is a rapidly emerging unenhanced MRI technique in oncologic breast imaging. This IRB approved study included n=818 patients (with n=618 malignant lesions in n=268 patients). All patients underwent a clinically indicated multiparametric breast 3T MRI examination, including a multi-b-value DWI acquisition (50,750,1500). We utilized nnDetection, a state-of-the-art self-configuring object detection model, with certain breast cancer-specific extensions to train a detection model. The model was trained with the following extensions: (i) apparent diffusion coefficient (ADC) as additional input, (ii) random bias field, random spike, and random ghosting augmentations, (iii) a size-balanced data loader to ensure that the fewer large lesions were given an equal chance to be picked in a mini-batch and (iv) replacement of the loss function with a size-adjusted focal loss, to prioritize finding primary lesions while disincentivizing small indeterminate false positives. The model was able to achieve an AUC of 0.88 in 5-fold cross-validation using only the DWI acquisition, and compares favorably against multireader performance metrics reported for screening mammography in large studies in the literature (0.81, 0.87, 0.81). It also achieved 0.70 FROC for primary lesions, indicating a relevant localization ability. This study shows that AI has the ability to complement breast cancer screening assessment in DWI-based examinations. This work was originally published at RSNA 2023 [1].