Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Korea.
Department of Computational Science and Engineering, Yonsei University, Seoul, Korea.
Sci Rep. 2020 Feb 19;10(1):2976. doi: 10.1038/s41598-020-59923-2.
This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), n = 182; validation set (Philips scanner), n = 49). The Clinical and Rad models to predict systemic recurrence were built up and the models were externally validated. In the training set, the Rad score was significantly higher in the group with systemic recurrence (median, -8.430) than the group without (median, -9.873, P < 0.001). The C-index of the Rad model to predict systemic recurrence in the training set was 0.97, which was significantly higher than in the Clinical model (0.879; P = 0.009). When the models were externally validated, the C-index of the Rad model was 0.848, lower than the 0.939 of the Clinical model, although the difference was not statistically significant (P = 0.100). The Rad model for predicting systemic recurrence in TNBC showed a significantly higher C-index than the Clinical model. However, external validation with a different MRI scanner did not show the Rad model to be superior over the Clinical model.
这篇论文评估了乳腺磁共振成像（MRI）的三维放射组学特征作为预测三阴性乳腺癌（TNBC）全身复发的预后因素，并使用不同的 MRI 扫描仪验证了结果。Rad 评分是从 231 例 TNBC 的三维放射组学特征中生成的（训练集（GE 扫描仪），n=182；验证集（Philips 扫描仪），n=49）。建立了预测全身复发的临床和 Rad 模型，并对模型进行了外部验证。在训练集中，全身复发组的 Rad 评分（中位数，-8.430）明显高于无全身复发组（中位数，-9.873，P<0.001）。Rad 模型预测训练集中全身复发的 C 指数为 0.97，明显高于临床模型（0.879；P=0.009）。当模型进行外部验证时，Rad 模型的 C 指数为 0.848，低于临床模型的 0.939，但差异无统计学意义（P=0.100）。预测 TNBC 全身复发的 Rad 模型的 C 指数明显高于临床模型。然而，使用不同的 MRI 扫描仪进行外部验证并未显示 Rad 模型优于临床模型。
