Petrillo Antonella, Fusco Roberta, Di Bernardo Elio, Petrosino Teresa, Barretta Maria Luisa, Porto Annamaria, Granata Vincenza, Di Bonito Maurizio, Fanizzi Annarita, Massafra Raffaella, Petruzzellis Nicole, Arezzo Francesca, Boldrini Luca, La Forgia Daniele
Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
Medical Oncology Division, Igea SpA, 80013 Naples, Italy.
Cancers (Basel). 2022 Apr 25;14(9):2132. doi: 10.3390/cancers14092132.
To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer.
A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon-Mann-Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented.
In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm.
The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
评估影像组学特征，以：区分恶性与良性病变；预测低级别与中高级别；识别激素受体阳性或阴性；以及鉴别与乳腺癌相关的人表皮生长因子受体2阳性与阴性。
本回顾性研究纳入了182例已知乳腺病变且接受了对比增强乳腺X线摄影的患者。参考标准为病理结果（118例恶性病变和64例良性病变）。通过从头尾位（CC）和内外斜位（MLO）视图手动分割感兴趣区域，共提取了837个纹理指标。使用了非参数Wilcoxon-Mann-Whitney检验、受试者工作特征曲线、逻辑回归和基于树的机器学习算法。采用了自适应合成采样平衡方法并实施了特征选择过程。
在单变量分析中，考虑在CC视图上提取的original_gldm_DependenceNonUniformity特征时，恶性与良性病变的分类表现最佳（准确率为88.98%）。分级分类的准确率达到83.65%，而激素受体存在情况分类的准确率略低（81.65%）；提取的特征分别为original_glrlm_RunEntropy和original_gldm_DependenceNonUniformity。多变量分析结果在使用两个或更多特征作为预测因子对CC视图图像中的恶性与良性病变进行分类时表现最佳（非正则化逻辑回归的最大测试准确率为95.83%）。考虑从MLO视图图像中提取的特征，使用分类树算法预测分级时获得了最佳测试准确率（91.67%）。仅从CC和MLO视图中提取的两个特征的组合，测试准确率值始终大于或等于90.00%，唯一的例外是人类表皮生长因子受体2的预测，使用随机森林算法时表现最佳（测试准确率为89.29%）。
结果证实，通过对对比增强乳腺X线摄影图像中提取的纹理特征进行单变量和多变量分析，可以以令人满意的准确率识别乳腺恶性病变，并区分肿瘤的组织学结果和一些分子亚型（主要是激素受体阳性肿瘤）。
