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首页> 外文期刊>Journal of Digital Imaging >Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer
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Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

机译：知识发现结果的验证：密度可预测乳腺癌

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The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5–17.6), irregular mass shape (OR 10.0, CI 3.4–29.5), spiculated mass margin (OR 20.4, CI 1.9–222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings.
机译：我们研究的目的是使用归纳逻辑程序设计（ILP）和条件概率来识别和量化高乳房质量密度与乳房恶性肿瘤之间的关联，并在独立的数据集中验证这种关联。我们对62,219例乳腺X线摄影异常运行了ILP算法。我们将Aleph ILP系统设置为每个恶性肿瘤产生10,000条规则，召回率> 5％，准确率> 25％。 Aleph报告了每个恶性肿瘤发现的最佳规则。总共学习了80条独特的规则。放射科医生检查了所有规则并确定了可能有趣的规则。 24％的学习规则中出现了高乳房质量密度。我们通过计算给定每个乳房X射线摄影描述符的恶性概率，确认了每个有趣的规则。高质量密度是排名第五高的预测因子。为了验证独立数据集中质量密度与恶性肿瘤之间的关联，我们收集了2005年至2007年间进行的180次连续乳房活检的数据。我们建立了以良性或恶性结局为因变量的logistic模型，同时控制了潜在的混杂因素。我们根据二分变量计算了优势比。在我们的逻辑回归模型中，独立的预测因子具有较高的乳房质量密度（OR 6.6，CI 2.5–17.6），不规则的肿块形状（OR 10.0，CI 3.4–29.5），细微的切缘（OR 20.4，CI 1.9–222.8）和受试者年龄（β= 0.09，p <0.0001）显着预测了恶性肿瘤。 ILP和条件概率均表明，高乳房质量密度是恶性肿瘤的重要辅助指标，这种关联在前瞻性收集的乳房X线照片发现的独立数据集中得到了证实。

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