Zhao Yanjie, Chen Rong, Zhang Ting, Chen Chaoyue, Muhelisa Muhetaer, Huang Jingting, Xu Yan, Ma Xuelei
Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Department of Radiology, Guiqian International General Hospital, Guiyang, China.
Front Oncol. 2021 Oct 18;11:552634. doi: 10.3389/fonc.2021.552634. eCollection 2021.
Differential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.
This current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.
All five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.
The evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.
乳腺良恶性病变的鉴别诊断对于后续治疗至关重要。纹理分析和机器学习的最新进展可能会为这个问题带来新的解决方案。
本研究纳入了2014年1月至2017年8月期间在我院诊断并接受磁共振成像检查的265例患者（乳腺良性病变：乳腺恶性病变 = 71:194）。患者被随机分为训练组和验证组（4:1），两名放射科医生从对比增强T1加权图像中提取纹理特征。我们进行了五种不同的特征选择方法，包括距离相关、梯度提升决策树（GBDT）、最小绝对收缩和选择算子（LASSO）、随机森林（RF）、极端梯度提升（Xgboost），并基于线性判别分析（LDA）算法建立了五个独立的分类模型。
所有五个模型在区分乳腺恶性病变和良性病变方面都显示出了良好的结果，训练组和验证组中受试者操作特征（ROC）曲线下面积（AUC）均高于0.830。鉴别能力较好的模型是LDA + 梯度提升决策树（GBDT）的组合。训练组的敏感性、特异性、AUC和准确性分别为0.814、0.883、0.922和0.868；LDA + 随机森林（RF）在训练组中的AUC为0.906，也显示出了良好的结果。
本研究的证据虽然初步，但表明MRI纹理分析和LDA算法的组合可以区分乳腺良性病变和恶性病变。该领域进一步的多中心研究将对结果的验证有很大帮助。
