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首页> 外文期刊>Acta Radiologica >Application of breast MRI for prediction of lymph node metastases - systematic approach using 17 individual descriptors and a dedicated decision tree.
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Application of breast MRI for prediction of lymph node metastases - systematic approach using 17 individual descriptors and a dedicated decision tree.

机译：乳房MRI在预测淋巴结转移中的应用-使用17个个体描述符和专用决策树的系统方法。

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BACKGROUND: The presence of lymph node metastases (LNMs) is one of the most important prognostic factors in breast cancer. PURPOSE: To correlate a detailed catalog of 17 descriptors in breast MRI (bMRI) with the presence of LNMs and to identify useful combinations of such descriptors for the prediction of LNMs using a dedicated decision tree. MATERIAL AND METHODS: A standardized protocol and study design was applied in this IRB-approved study (T1-weighted FLASH; 0.1 mmol/kg body weight Gd-DTPA; T2-weighted TSE; histological verification after bMRI). Two experienced radiologists performed prospective evaluation of the previously acquired examination in consensus. In every lesion 17 previously published descriptors were assessed. Subgroups of primary breast cancers with (N+: 97) and without LNM were created (N-: 253). The prevalence and diagnostic accuracy of each descriptor were correlated with the presence of LNM (chi-square test; diagnostic odds ratio/DOR). To identify useful combinations of descriptors for the prediction of LNM a chi-squared automatic interaction detection (CHAID) decision tree was applied. RESULTS: Seven of 17 descriptors were significantly associated with LNMs. The most accurate were "Skin thickening" (P < 0.001; DOR 5.9) and "Internal enhancement" (P < 0.001; DOR
机译：背景：淋巴结转移（LNMs）的存在是乳腺癌最重要的预后因素之一。目的：将乳腺MRI（bMRI）中17个描述符的详细目录与LNM的存在相关联，并使用专用决策树确定此类描述符对LNM预测的有用组合。材料和方法：标准化的方案和研究设计应用于该IRB批准的研究（T1加权FLASH; 0.1 mmol / kg体重Gd-DTPA; T2加权TSE; bMRI后的组织学验证）。两位经验丰富的放射科医生对以前获得的检查进行了前瞻性评估。在每个病变中，评估了17个先前发布的描述子。创建了具有（N +：97）和没有LNM的原发性乳腺癌亚组（N-：253）。每个描述符的患病率和诊断准确性与LNM的存在相关（卡方检验;诊断比值比/ DOR）。为了识别用于预测LNM的描述符的有用组合，应用了卡方自动交互检测（CHAID）决策树。结果：17个描述符中的七个与LNM显着相关。最准确的是“皮肤增厚”（P <0.001; DOR 5.9）和“内部增强”（P <0.001; DOR <或= 13.7）。 CHAID决策树确定了以下有用的描述符组合：“皮肤增厚”加上在没有“皮肤增厚”，“水肿”和“不规则边界”的情况下乳头线的破坏，N +的可能性为0％（P <0.05） 。结论：我们的数据表明所选的乳房MRI描述符与淋巴结状态密切相关。如果存在此类描述符，则可单独使用或组合使用这些描述符来准确预测LNM并分层患者的预后。

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