Risk stratification of probably benign mammographic lesions (BI-RADS 3) with Bayesian networks

在乳房X光检查中发现的被归类为BI-RADS(乳房成像：报告和数据系统)3类的病变，根据定义，恶性肿瘤的可能性低于2%。有几个描述符(例如，圆形、簇状微钙化)可以将BI-RADS分类为3类。该项目的目的是使用贝叶斯网络来检查是否可以将这些病变分层为恶性概率较高的组和恶性概率较低的组。通过考虑多个诊断变量的概率分布，贝叶斯网络允许计算阳性预测值。变量用节点表示，而变量之间的统计依赖关系用边表示。有几种算法可以用来建立贝叶斯网络，其中包括朴素的情况(假设所有诊断变量都有条件独立性)和树形扩充的朴素贝叶斯，允许每个变量增加一个相关性。要得出概率分布的可靠估计(例如，有多少癌症是高密度的，假设它们有毛刺的边缘)，需要大量的经验数据。威斯康星大学麦迪逊分校拥有根据BI-RADS词典对132.319例乳房X光检查进行评级的数据集。这些检查是在1999年至2011年间进行的。整个数据集与美国国家癌症登记处进行了匹配。这意味着在检查中观察到的变化有可靠的疾病状态(恶性/良性)。此外，每个患者都有关于乳腺癌家族史、乳腺癌个人史、激素替代治疗状况和乳房密度的记录，根据这些数据，归纳出BI-RADS 3病变的分类算法。初步讨论认为，将专家节点纳入经验贝叶斯网络是一种很有前途的方法。该专家节点不会从经验数据中获得其概率分布。它将完全以科学文献为基础。例如，结节区分典型和非典型的BI-RADS 3病变。通过这种方式，它可能能够区分那些在没有适当理由的情况下被分配到BI-RADS 3的组，并且具有更低或更高的恶性风险。整个方法的临床应用是为了降低乳房X光检查中可疑发现的比率，从而减少额外需要的成像次数。

Lesions detected in mammographic examinations which are assigned BI-RADS (Breast Imaging: Reporting and Data System) category 3 have a probability of malignancy less than two percent, per definition. There are several descriptors (e.g. round, clustered microcalcifications) that allow for the assignment of BI-RADS category 3. The aim of the project is to use Bayesian networks to examine whether stratification of these lesions into groups with higher, and groups with lower probability of malignancy is possible. Bayesian networks allow for the calculation of the positive predictive value, by taking the probability distributions of multiple diagnostic variables into account. Variables are represented as nodes, whereas the statistical dependencies among them are represented as edges. There are several algorithms available to induce a Bayesian network, among them being the naive case (conditional independence is assumed for all diagnostic variables) and a tree-augmented naive Bayes, that allows one additional dependency per variable.To derive reliable estimates of the probability distributions (for example, how many cancers are hyperdense, given that they have a spiculated margin), a huge amount of empirical data is necessary. The University of Wisconsin, Madison, has access to a dataset of 132.319 mammographies rated according to the BI-RADS lexicon. The examinations were performed between 1999 and 2011. The whole dataset was matched with the US national cancer registry. This means that there is a reliable disease status (malignant/benign) for observed changes in the examinations. Additionally, for every patient there is a record about family history of breast cancer, personal history of breast cancer, hormone replacement therapy status and breast density.With this data, classification algorithms for the BI-RADS 3 lesions are induced. A preliminary discussion has resulted in the conviction that the inclusion of an expert node into empirical Bayesian networks is a promising approach. This expert node will not get its probability distribution from the empirical data. It will solely be based on the scientific literature. For example the node distinguishes between typical and atypical BI-RADS 3 lesions. In this way it is possibly able to distinguish groups that were assigned BI-RADS 3 without proper justification, and have a much lower or much higher risk for malignancy. The clinical use of the whole approach is to lower the rate of equivocal findings in mammography, and therefore reduce the number of additionally required imaging.