Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis.
IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Heather M Whitney, Karen Drukker, Alexandra Edwards, Maryellen L Giger
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引用次数: 0
Abstract
Purpose: Breast cancer may persist within milk ducts (ductal carcinoma in situ, DCIS) or advance into surrounding breast tissue (invasive ductal carcinoma, IDC). Occasionally, invasiveness in cancer may be underestimated during biopsy, leading to adjustments in the treatment plan based on unexpected surgical findings. Artificial intelligence/computer-aided diagnosis (AI/CADx) techniques in medical imaging may have the potential to predict whether a lesion is purely DCIS or exhibits a mixture of IDC and DCIS components, serving as a valuable supplement to biopsy findings. To enhance the evaluation of AI/CADx performance, assessing variability on a lesion-by-lesion basis via likelihood assurance measures could add value.
Approach: 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). Lesion-based likelihood assurance was measured using a sureness metric based on the 95% confidence interval of the classifier output for each lesion.
Results: The median and 95% CI of the 0.632+-corrected area under the receiver operating characteristic curve for the task of classifying lesions as pure DCIS or mixed IDC/DCIS were 0.81 [0.75, 0.86]. The sureness metric varied across the dataset with a range of 0.0002 (low sureness) to 0.96 (high sureness), with combinations of high and low classifier output and high and low sureness for some lesions.
Conclusions: Sureness metrics can provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion is invasive.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.