Manon A G Bakker, Maria de Lurdes Ovalho, Nuno Matela, Ana M Mota
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引用次数: 0
摘要
乳腺癌是全世界最常见的癌症。治疗方法及其成功与否在很大程度上取决于肿瘤的组织学。本研究旨在探索利用从筛查数字乳腺 X 射线摄影(DM)图像中提取的放射学特征预测乳腺癌分子亚型的潜力。研究使用 OPTIMAM 乳房 X 射线摄影图像数据库(OMI-DB)进行了一项回顾性研究。进行了四种二元分类任务:管腔 A 与非管腔 A、管腔 B 与非管腔 B、TNBC 与非 TNBC 和 HER2 与非 HER2。特征选择是通过皮尔逊相关性和 LASSO 进行的。使用支持向量机(SVM)和天真贝叶斯(NB)ML分类器,并以准确率和接收者工作特征曲线下面积(AUC)评估其性能。研究共纳入了 186 名患者:其中管腔 A 型 58 例,管腔 B 型 35 例,TNBC 型 52 例,HER2 型 41 例。SVM 分类器在测试期间的 AUC 分别为:管腔 A 0.855、管腔 B 0.812、TNBC 0.789 和 HER2 0.755。NB 分类器在测试期间的 AUC 分别为管腔 A 0.714、管腔 B 0.746、TNBC 0.593 和 HER2 0.714。在管腔 A(p = 0.0268)和 TNBC(p = 0.0073)方面,SVM 分类器的统计显著性优于 NB。我们的研究显示了放射组学在无创乳腺癌亚型分类方面的潜力。
Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images.
Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images. A retrospective study was performed using the OPTIMAM Mammography Image Database (OMI-DB). Four binary classification tasks were performed: luminal A vs. non-luminal A, luminal B vs. non-luminal B, TNBC vs. non-TNBC, and HER2 vs. non-HER2. Feature selection was carried out by Pearson correlation and LASSO. The support vector machine (SVM) and naive Bayes (NB) ML classifiers were used, and their performance was evaluated with the accuracy and the area under the receiver operating characteristic curve (AUC). A total of 186 patients were included in the study: 58 luminal A, 35 luminal B, 52 TNBC, and 41 HER2. The SVM classifier resulted in AUCs during testing of 0.855 for luminal A, 0.812 for luminal B, 0.789 for TNBC, and 0.755 for HER2, respectively. The NB classifier showed AUCs during testing of 0.714 for luminal A, 0.746 for luminal B, 0.593 for TNBC, and 0.714 for HER2. The SVM classifier outperformed NB with statistical significance for luminal A (p = 0.0268) and TNBC (p = 0.0073). Our study showed the potential of radiomics for non-invasive breast cancer subtype classification.