Abstract：
OBJECTIVE: To determine whether preoperative classification of breast edema on T2-weighted imaging (T2WI) is useful for predicting sentinel lymph node (SLN) metastasis and biological behavior in patients with early-stage breast cancer.
METHODS: This retrospective study involved 341 women with breast cancer who underwent breast MRI from January 2019 to March 2022. Breast edema was scored on a scale of 1-4 on T2WI (1, no edema; 2, peritumoral edema; 3, prepectoral edema; and 4, subcutaneous edema). A logistic regression model was employed for univariate and multivariate analyses. A clinicopathological model was established using independent influencing factors identified in the multivariate analyses, excluding breast edema score (BES). Subsequently, BES was incorporated into this model to establish a combined BES model. The AUC and Delong test were used to examine the additional predictive value of the BES.
RESULTS: Logistic regression analysis showed that breast edema was an independent risk factor for SLN metastasis. The combined BES model significantly improved the predictive performance of SLN metastasis compared with the clinicopathological model alone (AUC, 0.77 vs. 0.71; p=0.005). In addition, the BES was significantly positively correlated with the tumor diameter (p<0.001), histologic grade (p=0.001), Ki-67 index (p<0.001), and non-luminal subtypes (p<0.001).
CONCLUSIONS: The BES on T2WI is useful for predicting SLN metastasis. A higher grade of breast edema is associated with breast cancer aggressiveness and increases the probability of SLN metastasis.
METHODS: This retrospective study involved 341 women with breast cancer who underwent breast MRI from January 2019 to March 2022. Breast edema was scored on a scale of 1-4 on T2WI (1, no edema; 2, peritumoral edema; 3, prepectoral edema; and 4, subcutaneous edema). A logistic regression model was employed for univariate and multivariate analyses. A clinicopathological model was established using independent influencing factors identified in the multivariate analyses, excluding breast edema score (BES). Subsequently, BES was incorporated into this model to establish a combined BES model. The AUC and Delong test were used to examine the additional predictive value of the BES.
RESULTS: Logistic regression analysis showed that breast edema was an independent risk factor for SLN metastasis. The combined BES model significantly improved the predictive performance of SLN metastasis compared with the clinicopathological model alone (AUC, 0.77 vs. 0.71; p=0.005). In addition, the BES was significantly positively correlated with the tumor diameter (p<0.001), histologic grade (p=0.001), Ki-67 index (p<0.001), and non-luminal subtypes (p<0.001).
CONCLUSIONS: The BES on T2WI is useful for predicting SLN metastasis. A higher grade of breast edema is associated with breast cancer aggressiveness and increases the probability of SLN metastasis.
摘要：
目的：确定术前T2加权成像（T2WI）对乳腺癌前哨淋巴结（SLN）转移和生物学行为的预测是否有用。
方法：这项回顾性研究包括从2019年1月至2022年3月接受乳腺MRI检查的341名乳腺癌女性。在T2WI上以1-4的等级对乳房水肿进行评分（1，无水肿；2，瘤周水肿；3，胸前水肿；和4，皮下水肿）。采用逻辑回归模型进行单变量和多变量分析。使用多变量分析中确定的独立影响因素建立了临床病理模型，不包括乳腺水肿评分（BES）。随后，将BES并入该模型以建立组合BES模型。AUC和Delong检验用于检查BES的额外预测值。
结果：Logistic回归分析显示乳腺水肿是SLN转移的独立危险因素。与单独的临床病理模型相比，联合BES模型显着提高了SLN转移的预测性能（AUC，0.77vs.0.71；p=0.005)。此外,BES与肿瘤直径呈显著正相关(p<0.001),组织学分级（p=0.001），Ki-67指数（p<0.001），和非腔亚型（p<0.001）。
结论：T2WI上的BES可用于预测SLN转移。较高的乳腺水肿等级与乳腺癌侵袭性相关，并增加SLN转移的可能性。
方法：这项回顾性研究包括从2019年1月至2022年3月接受乳腺MRI检查的341名乳腺癌女性。在T2WI上以1-4的等级对乳房水肿进行评分（1，无水肿；2，瘤周水肿；3，胸前水肿；和4，皮下水肿）。采用逻辑回归模型进行单变量和多变量分析。使用多变量分析中确定的独立影响因素建立了临床病理模型，不包括乳腺水肿评分（BES）。随后，将BES并入该模型以建立组合BES模型。AUC和Delong检验用于检查BES的额外预测值。
结果：Logistic回归分析显示乳腺水肿是SLN转移的独立危险因素。与单独的临床病理模型相比，联合BES模型显着提高了SLN转移的预测性能（AUC，0.77vs.0.71；p=0.005)。此外,BES与肿瘤直径呈显著正相关(p<0.001),组织学分级（p=0.001），Ki-67指数（p<0.001），和非腔亚型（p<0.001）。
结论：T2WI上的BES可用于预测SLN转移。较高的乳腺水肿等级与乳腺癌侵袭性相关，并增加SLN转移的可能性。
