Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China.
Eur Radiol. 2021 Aug;31(8):5924-5939. doi: 10.1007/s00330-020-07674-z. Epub 2021 Feb 10.
To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer.
A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram's performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor.
The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts.
A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer.
• Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.
建立并验证一种基于多参数 MRI 的放射组学列线图，用于预测早期乳腺癌患者腋窝前哨淋巴结（SLN）的负荷。
回顾性分析了 230 例早期浸润性乳腺癌女性患者的资料。基于中心 1 的术前多参数 MRI 数据构建放射组学特征，然后在中心 1 的验证队列（n=42）和中心 2 的外部测试队列（n=62）中进行验证。采用多变量逻辑回归建立纳入放射组学特征和预测临床及影像学特征的放射组学列线图。通过区分度、校准度和临床实用性评估放射组学列线图的性能，并与基于 MRI 的原发性乳腺肿瘤特征进行比较。
纳入放射组学特征和 MRI 确定的腋窝淋巴结（ALN）负荷的构建放射组学列线图具有良好的校准度，并且在预测 SLN 负荷方面优于单独使用 MRI 确定的 ALN 负荷（训练队列：曲线下面积 [AUC]：0.82 比 0.68 [p<0.001]；验证队列：0.81 比 0.68 [p=0.04]；测试队列：0.81 比 0.58 [p=0.001]）。与基于 MRI 的乳腺肿瘤联合特征相比，放射组学列线图在测试队列中具有更高的 AUC（0.81 比 0.58，p=0.005），在训练队列（0.82 比 0.73，p=0.15）和验证队列（0.81 比 0.65，p=0.31）中具有可比的 AUC。
基于多参数 MRI 的放射组学列线图可用于预测早期乳腺癌患者 SLN 的负荷。
纳入放射组学特征和 MRI 确定的 ALN 负荷的放射组学列线图在预测早期乳腺癌患者 SLN 负荷方面优于单独使用 MRI 确定的 ALN 负荷。
放射组学列线图可能具有比基于 MRI 的乳腺肿瘤联合特征更好的预测能力。
基于多参数 MRI 的放射组学列线图可作为一种非侵入性工具，用于预测早期乳腺癌患者 SLN 的负荷。
