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Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

机译:通过组织学和乳房Xmmopare特征,可以在活组织检查之前预测导管癌的脑梗塞癌的呼起吗?

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Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.
机译:在原位(DCIS)原位(DCIS)上减少与导管癌相关的过度降血和过度处理需要准确地预测癌症筛查的侵入性潜力。在这项工作中,我们调查了术前组织学和乳房X光学特征的效用,以预测DCI的升高。目标是提供有意保守的基线绩效,使用来自放射科和病理学家的易于使用的数据以及仅线性模型。我们对99例DCIS患者进行了回顾性分析。在明确的手术时,这25个被营造成侵入性癌症。统计分析研究了包括由立体定向核心针活检(SCNB)报告(SCNB)报告(SCNB)报告(SCNB)报告和由专家乳房放射学家注释的乳房X线检查特征的术前因子。此外,我们基于这些特征构建了分类模型,以试图预测DCI中的隐匿性侵入分量,通过接收器操作特征(ROC)曲线分析评估的泛化性能。包括核等级和DCIS亚型的组织学特征在纯DCIS和DCIS Plus侵入性疾病中没有显示出统计学上显着的差异。然而,三种乳房X线图,即DCIS病变的主要轴长度,Bi-RAD级别的怀疑和放射科医师的评估确实达到了统计学意义。使用这三种统计学显着的特征作为输入,线性判别模型能够将DCIS加入患者与纯DCIS的患者区分开,AUC-ROC等于0.62。总体而言,用于乳房筛查的乳房X线照片含有可由放射科医师感知的有用信息,并帮助预测DCIS中的隐匿性侵袭性组分。

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