|
Breast cancer may persist within milk ducts, known as ductal carcinoma in situ (DCIS), or advance into surrounding breast tissue, referred to as invasive ductal carcinoma (IDC). Occasionally, the invasiveness of cancer may be underestimated during biopsy, leading to adjustments in the treatment plan based upon unexpected surgical findings. Artificial intelligence (AI) and computer-aided diagnosis (CADx) techniques in medical imaging may have potential in preoperatively predicting whether a lesion is purely DCIS or exhibits a mixture of IDC and DCIS components and could serve as a valuable supplement to biopsy findings. To enhance the evaluation of AI/CADx performance, assessing variability on a lesion-by-lesion basis via a previously-established ‘sureness’ metric could add considerable value. In this study, we evaluated the performance in the task of distinguishing between pure DCIS and mixed IDC/DCIS breast cancers using computer-extracted radiomic features from dynamic contrast-enhanced magnetic resonance imaging using 0.632+ bootstrapping methods (2000 folds) on 550 lesions (135 pure DCIS, 415 mixed IDC/DCIS), and characterized the lesion-based repeatability of the prediction using sureness. The median and 95% CI of the 0.632+-corrected AUC for the task of classifying lesions as pure DCIS or mixed IDC/DCIS was 0.81 [0.75, 0.86]. Sureness varied across the dataset, with combinations of high and low classifier output and high and low sureness for some lesions. These results point to the potential for sureness to provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion has invasive components.
|
