Yang Xiao, Lu Zhou, Tan Xiaoying, Shao Lin, Shi Jie, Dou Weiqiang, Sun Zongqiong
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
GE Healthcare, MR Research China, Beijing, China.
Quant Imaging Med Surg. 2024 Jun 1;14(6):3789-3802. doi: 10.21037/qims-24-1. Epub 2024 Apr 29.
The noninvasive prediction of sentinel lymph node (SLN) metastasis using quantitative magnetic resonance imaging (MRI), particularly with synthetic MRI (syMRI), is an emerging field. This study aimed to explore the potential added benefits of syMRI over conventional MRI and diffusion-weighted imaging (DWI) in predicting metastases in SLNs.
This retrospective study consecutively enrolled 101 patients who were diagnosed with breast cancer (BC) and underwent SLN biopsy from December 2022 to October 2023 at the Affiliated Hospital of Jiangnan University. These patients underwent preoperative MRI including conventional MRI, DWI, and syMRI and were categorized into two groups according to the postoperative pathological results: those with and without metastatic SLNs. MRI morphological features, DWI, and syMRI-derived quantitative parameters of breast tumors were statistically compared between these two groups. Binary logistic regression was used to separately develop predictive models for determining the presence of SLN involvement, with variables that exhibited significant differences being incorporated. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis, including the area under the curve (AUC), sensitivity, and specificity.
Compared to the group of 54 patients with BC but no metastatic SLNs, the group of 47 patients with BC and metastatic SLNs had a significantly larger maximum axis diameter [metastatic SLNs: median 2.40 cm, interquartile range (IQR) 1.50-3.00 cm; no metastatic SLNs: median 1.80 cm, IQR 1.37-2.50 cm; P=0.03], a higher proton density (PD) (78.44±11.92 69.20±10.63 pu; P<0.001), and a lower apparent diffusion coefficient (ADC) (metastatic SLNs: median 0.91×10 mm/s, IQR 0.79-1.01 mm/s; no metastatic SLNs: median 1.02×10 mm/s, IQR 0.92-1.12 mm/s; P=0.001). Moreover, the prediction model with maximum axis diameter and ADC yielded an AUC of 0.71 [95% confidence interval (CI): 0.618-0.802], with a sensitivity of 78.72% and a specificity of 51.85%; After addition of syMRI-derived PD to the prediction model, the AUC increased significantly to 0.86 (AUC: 0.86 0.71; 95% CI: 0.778-0.922; P=0.002), with a sensitivity of 80.85% and a specificity of 81.50%.
Combined with conventional MRI and DWI, syMRI can offer additional value in enhancing the predictive performance of determining SLN status before surgery in patients with BC.
使用定量磁共振成像（MRI），尤其是合成MRI（syMRI）对前哨淋巴结（SLN）转移进行无创预测是一个新兴领域。本研究旨在探讨syMRI相较于传统MRI和扩散加权成像（DWI）在预测SLN转移方面的潜在附加益处。
本回顾性研究连续纳入了2022年12月至2023年10月在江南大学附属医院被诊断为乳腺癌（BC）并接受SLN活检的101例患者。这些患者术前接受了包括传统MRI、DWI和syMRI在内的MRI检查，并根据术后病理结果分为两组：有和无SLN转移的患者。对两组患者的乳腺肿瘤的MRI形态学特征、DWI以及syMRI衍生的定量参数进行统计学比较。采用二元逻辑回归分别建立用于确定SLN受累情况的预测模型，并纳入表现出显著差异的变量。通过受试者操作特征（ROC）曲线分析评估每个模型的性能，包括曲线下面积（AUC）、敏感性和特异性。
与54例BC但无SLN转移的患者组相比，47例BC且有SLN转移的患者组的最大轴径明显更大[SLN转移：中位数2.40 cm，四分位间距（IQR）1.50 - 3.00 cm；无SLN转移：中位数1.80 cm，IQR 1.37 - 2.50 cm；P = 0.03]，质子密度（PD）更高（78.44±11.92对69.20±10.63 pu；P < 0.001），表观扩散系数（ADC）更低（SLN转移：中位数0.91×10⁻³mm²/s，IQR 0.79 - 1.01×10⁻³mm²/s；无SLN转移：中位数1.02×10⁻³mm²/s，IQR 0.92 - 1.12×10⁻³mm²/s；P = 0.001）。此外，最大轴径和ADC的预测模型的AUC为0.71[95%置信区间（CI）：0.618 - 0.802]，敏感性为78.72%，特异性为51.85%；在预测模型中加入syMRI衍生的PD后，AUC显著增加至0.86（AUC：0.86对0.71；95% CI：0.778 - 0.922；P = 0.002），敏感性为80.85%，特异性为81.50%。
与传统MRI和DWI相结合，syMRI在提高BC患者术前确定SLN状态的预测性能方面可提供附加价值。
