Romeo Valeria, Kapetas Panagiotis, Clauser Paola, Baltzer Pascal A T, Rasul Sazan, Gibbs Peter, Hacker Marcus, Woitek Ramona, Pinker Katja, Helbich Thomas H
Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80138 Naples, Italy.
Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria.
Cancers (Basel). 2022 Aug 16;14(16):3944. doi: 10.3390/cancers14163944.
To investigate whether a machine learning (ML)-based radiomics model applied to F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC.
Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions.
Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images.
A ML-based radiomics model applied to F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a "virtual biopsy" might be performed with radiomics signatures.
研究应用于F-FDG PET/MRI的基于机器学习（ML）的放射组学模型在乳腺癌（BC）分子亚型分类中是否有效，特别是在区分三阴性（TN）与其他BC分子亚型方面是否有效。
纳入86例患有98个BC病灶的患者（Luminal A = 10例，Luminal B = 51例，HER2+ = 12例，TN = 25例），并对其乳房进行同步F-FDG PET/MRI检查。在T2w、DCE、DWI和PET图像上对BC病灶进行三维分割。计算定量扩散和代谢参数并提取放射组学特征。使用LASSO回归选择数据，并由具有五折交叉验证的精细高斯支持向量机（SVM）分类器用于识别三阴乳腺癌（TNBC）病灶。
基于定量参数和/或放射组学特征的不同组合建立了八个放射组学模型。发现结合从ADC和PET图像中提取的基于一阶、邻域灰度依赖矩阵和大小区域矩阵的放射组学特征的模型具有最佳性能（曲线下面积0.887，准确率82.8%，灵敏度79.7%，特异性86%，阳性预测值85.3%，阴性预测值80.8%）。
应用于F-FDG PET/MRI的基于ML的放射组学模型能够以高精度无创地将TNBC病灶与其他BC分子亚型区分开来。从未来的角度来看，可能会利用放射组学特征进行“虚拟活检”。
