University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany.
Eur J Med Res. 2017 Aug 30;22(1):30. doi: 10.1186/s40001-017-0270-0.
Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure).
This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation.
About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model.
Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.
在乳腺 X 光片中，与低密型乳腺相比，密型乳腺中的肿瘤更易被忽视。因此，需要一种快速、可重复且自动化的方法来评估乳腺 X 光片密度百分比（PMD），以便在诊断性乳腺 X 光检查单位中为女性决定是否应在乳腺 X 光检查之外提供超声检查。由于存在观察者间变异性问题且该过程非常耗时，因此 PMD 评估尚未纳入临床常规工作。本研究旨在探讨是否可以使用全自动生成的乳腺 X 光片纹理特征替代耗时的半自动 PMD 评估来预测患者发生超声可见但乳腺 X 光片（乳腺 X 光失败）上不可见的浸润性乳腺癌的风险。
本观察性研究纳入了在医院诊断性乳腺 X 光检查单位接受治疗的 1334 例浸润性乳腺癌女性。超声检查是该队列的常规诊断手段。进行了基于计算机的阈值 PMD 评估（“观察到的 PMD”），并从每个乳腺 X 光片中获取了 363 个纹理特征。应用了几种变量选择和回归技术（单变量选择、套索、提升、随机森林）来根据纹理特征预测 PMD。然后，将预测的 PMD 值作为新的预测因子与临床预测因子一起用于逻辑回归模型中的掩蔽预测。将这四个具有预测 PMD 的逻辑回归模型进行了相互比较，并与具有观察到的 PMD 的逻辑回归模型进行了比较。通过交叉验证确定了最准确的掩蔽预测。
约 363 个纹理特征中有 120 个被选择用于预测 PMD。提升法的密度预测是预测掩蔽效果的观察 PMD 的最佳替代品。总体而言，相应的逻辑回归模型的性能优于没有乳腺密度的模型（交叉验证 AUC，0.747），但不如具有观察到的 PMD 的模型（0.753）。然而，在被分配的乳腺 X 光失败风险>10%的患者中（约占所有被掩盖肿瘤的一半），基于提升法的模型表现至少与原始 PMD 模型一样准确。
自动生成的纹理特征可以替代半自动确定的 PMD，用于预测乳腺 X 光失败，从而可以发现超过 50%的被掩盖肿瘤。
