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
Journal of Medical Imaging Pub Date : 2025-11-01 Epub Date: 2025-06-18 DOI:10.1117/1.JMI.12.S2.S22012
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.

动态增强磁共振成像将乳腺癌分类为单纯导管原位癌或浸润性成分的确定性:可能性保证指标在计算机辅助诊断中的应用
目的:乳腺癌可能持续存在于乳管内(导管原位癌,DCIS)或进展到周围乳腺组织(浸润性导管癌,IDC)。偶尔,肿瘤的侵袭性可能在活检中被低估,导致根据意外的手术结果调整治疗计划。医学成像中的人工智能/计算机辅助诊断(AI/CADx)技术可能有潜力预测病变是纯粹的DCIS还是表现为IDC和DCIS成分的混合,作为活检结果的有价值补充。为了加强对AI/CADx性能的评估,通过可能性保证措施来评估每个病变的可变性可以增加价值。方法:我们对550个病变(135个单纯DCIS, 415个混合IDC/DCIS)采用0.632+ bootstrapping方法(2000倍),使用计算机提取的动态对比增强磁共振成像放射特征来评估区分单纯DCIS和混合IDC/DCIS乳腺癌的性能。基于病变的可能性保证使用基于每个病变分类器输出的95%置信区间的可信度度量来测量。结果:将病变分类为单纯DCIS或混合IDC/DCIS的受试者工作特征曲线下0.632+校正区域的中位数和95% CI为0.81[0.75,0.86]。在整个数据集中,可信度度量的范围从0.0002(低可信度)到0.96(高可信度),分类器输出的高低和某些病变的高低可信度相结合。结论:确定性指标可以为CADx算法术前预测病变是否具有侵袭性的能力提供额外的见解。
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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
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.
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