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美国、乳腺钼靶检查及组织病理学评估以识别低核级导管原位癌

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ.

作者信息

Lee Si Eun, Kim Ga Ram, Han Kyunghwa, Kim Eun Hwa, Kim Eun-Kyung, Kim Min Jung, Yoon Jung Hyun, Park Vivian Youngjean, Moon Hee Jung

机构信息

From the Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea (S.E.L., E.K.K.); Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (G.R.K., K.H., M.J.K., J.H.Y., V.Y.P.); Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea (E.H.K.); and Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju 220-701, Korea (H.J.M.).

出版信息

Radiology. 2022 May;303(2):276-284. doi: 10.1148/radiol.211425. Epub 2022 Feb 15.

DOI:10.1148/radiol.211425
PMID:35166586
Abstract

Background Low nuclear grade ductal carcinoma in situ (DCIS) identified at biopsy can be upgraded to intermediate to high nuclear grade DCIS at surgery. Methods that confirm low nuclear grade are needed to consider nonsurgical approaches for these patients. Purpose To develop a preoperative model to identify low nuclear grade DCIS and to evaluate factors associated with low nuclear grade DCIS at biopsy that was not upgraded to intermediate to high nuclear grade DCIS at surgery. Materials and Methods In this retrospective study, 470 women (median age, 50 years; interquartile range, 44-58 years) with 477 pure DCIS lesions at surgical histopathologic evaluation were included (January 2010 to December 2015). Patients were divided into the training set ( = 330) or validation set ( = 147) to develop a preoperative model to identify low nuclear grade DCIS. Features at US (mass, nonmass) and at mammography (morphologic characteristics, distribution of microcalcification) were reviewed. The upgrade rate of low nuclear grade DCIS was calculated, and multivariable regression was used to evaluate factors for associations with low nuclear grade DCIS that was not upgraded later. Results A preoperative model that included lesions manifesting as a mass at US without microcalcification and no comedonecrosis at biopsy was used to identify low nuclear grade DCIS, with a high area under the receiver operating characteristic curve of 0.97 (95% CI: 0.94, 1.00) in the validation set. The upgrade rate of low nuclear grade DCIS at biopsy was 38.8% (50 of 129). Ki-67 positivity (odds ratio, 0.04; 95% CI: 0.0003, 0.43; = .005) was inversely associated with constant low nuclear grade DCIS. Conclusion The upgrade rate of low nuclear grade ductal carcinoma in situ (DCIS) at biopsy to intermediate to high nuclear grade DCIS at surgery occurred in more than a third of patients; low nuclear grade DCIS at final histopathologic evaluation could be identified if the mass was viewed at US without microcalcifications and had no comedonecrosis at histopathologic evaluation of biopsy. © RSNA, 2022 See also the editorial by Rahbar in this issue.

摘要

背景

活检时确诊的低核级导管原位癌(DCIS)在手术时可能升级为中核级至高核级DCIS。需要有确认低核级的方法,以便考虑对这些患者采用非手术治疗方法。目的:建立一种术前模型,以识别低核级DCIS,并评估活检时与低核级DCIS相关且在手术时未升级为中核级至高核级DCIS的因素。材料与方法:在这项回顾性研究中,纳入了470名女性(中位年龄50岁;四分位间距44 - 58岁),她们在手术组织病理学评估时有477个单纯DCIS病灶(2010年1月至2015年12月)。将患者分为训练集(n = 330)或验证集(n = 147),以建立识别低核级DCIS的术前模型。回顾了超声(肿块、非肿块)和乳腺X线摄影(形态学特征、微钙化分布)的特征。计算低核级DCIS的升级率,并使用多变量回归评估与后期未升级的低核级DCIS相关因素之间的关联。结果:采用一个术前模型来识别低核级DCIS,该模型包括超声表现为肿块且无微钙化以及活检时无粉刺样坏死的病灶,在验证集中其受试者操作特征曲线下面积高达0.97(95%CI:0.94,1.00)。活检时低核级DCIS的升级率为38.8%(129例中的50例)。Ki-67阳性(比值比,0.04;95%CI:0.0003,0.43;P = 0.005)与持续低核级DCIS呈负相关。结论:活检时低核级导管原位癌(DCIS)在手术时升级为中核级至高核级DCIS的发生率超过三分之一;如果超声检查发现肿块无微钙化且活检组织病理学评估无粉刺样坏死,则可在最终组织病理学评估时识别出低核级DCIS。©RSNA,2022 另见本期Rahbar的社论。

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引用本文的文献

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J Clin Med. 2025 Apr 20;14(8):2842. doi: 10.3390/jcm14082842.
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Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.将放射组学整合到使用机器学习的低核级导管原位癌预测模型中。
Sci Rep. 2025 Mar 3;15(1):7505. doi: 10.1038/s41598-025-92080-y.
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A Nomogram Using Imaging Features to Predict Ipsilateral Breast Tumor Recurrence After Breast-Conserving Surgery for Ductal Carcinoma In Situ.
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