Lenga Lukas, Bernatz Simon, Martin Simon S, Booz Christian, Solbach Christine, Mulert-Ernst Rotraud, Vogl Thomas J, Leithner Doris
Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, 60590 Frankfurt, Germany.
Department of Gynecology and Obstetrics, University Hospital Frankfurt, 60590 Frankfurt, Germany.
Cancers (Basel). 2021 May 18;13(10):2431. doi: 10.3390/cancers13102431.
Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.
双能CT（DECT）碘图能够定量碘浓度，作为组织血管化的标志物。我们研究了源自分期DECT的碘图放射组学特征是否能够预测乳腺癌转移状态，以及原发性乳腺癌和转移灶之间是否存在纹理差异。回顾性纳入了77例未经治疗且活检证实为乳腺癌的患者（41例非转移性，36例转移性）。从碘图上的感兴趣区域提取包括一阶、二阶和高阶指标以及形状描述符在内的放射组学特征。经过主成分分析后，使用多层感知器人工神经网络（MLP-NN）进行分类（70%的病例用于训练，30%用于验证）。组织病理学作为参考标准。MLP-NN预测转移状态的训练集和验证集的曲线下面积（AUC）分别高达0.94，准确率分别高达92.6%和82.6%。原发性肿瘤和转移组织的区分在训练集中的AUC高达0.87，准确率高达82.8%，在验证集中AUC为0.87，准确率为85.7%。因此，基于DECT碘图的放射组学特征可能预测乳腺癌患者的转移状态。此外，原发性和转移性乳腺癌组织之间的微观结构差异可能通过DECT放射组学特征的差异来反映。
