Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China.
Clinical Medical College of Jining Medical University, Jining 272000, China.
Eur J Radiol. 2022 Sep;154:110392. doi: 10.1016/j.ejrad.2022.110392. Epub 2022 Jun 3.
To explore the clinical value of diffusional kurtosis imaging (DKI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting genotypes and prognostic factors of breast cancer.
A total of 130 female patients with pathologically-confirmed breast cancer and DKI and DCE-MRI data were reviewed retrospectively. Two radiologists independently evaluated mean diffusivity (MD) and mean kurtosis (MK) for the DKI model and volume transfer constant (K), reverse rate constant (Kep), and extracellular extravascular volume ratio (Ve) for the DCE-MRI model for post-processing analyses. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic efficacies.
MK, K, and Kep values were significantly higher in the high-grade Nottingham prognostic index (NPI) group (NPI ≥ 3.4) than in the low-grade NPI group (NPI < 3.4) (p < 0.01). The K had significantly greater area under ROC curve (AUC) than Kep and MK in predicting the NPI (p = 0.038 and 0.0217, respectively). Higher K, Kep, and MK values were observed in the high Ki-67 expression (≥14%) group than in the low Ki-67 expression (<14%) group (p < 0.05). Moreover, the MK value had better diagnostic performance than the K and Kep values in identifying Ki-67 expression status (p = 0.0097 and 0.0008, respectively). The combined model (MD + MK + K + Ve) had a significantly higher AUC than the single parameters for differentiating between luminal A/B and non-luminal subtypes (p = 0.003, < 0.001, 0.001, and < 0.001, respectively). The Human epidermal growth factor receptor 2-positive group had higher MD and Ve values than the other subtype groups (p < 0.05), and the Ve had a sensitivity of 100%. Moreover, the Ve AUC was significantly higher than that for MD in the identification of the triple-negative subtype (p = 0.048).
K of DCE-MRI and MK of DKI demonstrated good diagnostic performance in predicting the prognostic factors of breast cancer. Additionally, the combination of the DCE-MRI and DKI models can improve the efficiency of predicting breast cancer genotypes.
探讨扩散峰度成像（DKI）和动态对比增强磁共振成像（DCE-MRI）在预测乳腺癌基因型和预后因素方面的临床价值。
回顾性分析了 130 例经病理证实的乳腺癌女性患者的 DKI 和 DCE-MRI 数据。两位放射科医生分别对 DKI 模型的平均弥散度（MD）和平均峰度（MK）以及 DCE-MRI 模型的容积转移常数（K）、反向速率常数（Kep）和细胞外细胞外容积比（Ve）进行后处理分析。采用受试者工作特征（ROC）曲线分析诊断效能。
高级别诺丁汉预后指数（NPI）组（NPI≥3.4）的 MK、K 和 Kep 值明显高于低级别 NPI 组（NPI<3.4）（p<0.01）。K 在预测 NPI 方面的 ROC 曲线下面积（AUC）显著大于 Kep 和 MK（p=0.038 和 0.0217）。Ki-67 高表达（≥14%）组的 K、Kep 和 MK 值明显高于 Ki-67 低表达（<14%）组（p<0.05）。此外，MK 值在识别 Ki-67 表达状态方面的诊断性能优于 K 和 Kep 值（p=0.0097 和 0.0008）。MD+MK+K+Ve 联合模型在区分管腔 A/B 和非管腔亚型方面的 AUC 显著高于单一参数（p=0.003，<0.001，0.001 和<0.001）。人表皮生长因子受体 2 阳性组的 MD 和 Ve 值明显高于其他亚型组（p<0.05），Ve 值的灵敏度为 100%。此外，Ve 的 AUC 在识别三阴性亚型方面明显高于 MD（p=0.048）。
DCE-MRI 的 K 和 DKI 的 MK 在预测乳腺癌预后因素方面具有良好的诊断性能。此外，DCE-MRI 和 DKI 模型的联合可以提高预测乳腺癌基因型的效率。
