Wang Wei, Zhang Wenyu, Yu Ting, Wu QingWei, Yang ChengLin, Li Jianbin
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
Front Oncol. 2025 Apr 3;15:1477866. doi: 10.3389/fonc.2025.1477866. eCollection 2025.
Although preoperative prediction of axillary lymph nodes status has been achieved using radiomics and combined models, there is a dearth of research on internal mammary lymph node (IMN) metastasis status prediction. We developed a predictive model by combining clinicopathological factors with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics to accurately predict IMN metastasis in breast cancer.
Patients who had no evidence of IMN metastasis on preoperative images but underwent internal mammary sentinel lymph node biopsy (IM-SLNB) were included in this study. Preoperative DCE-MRI and clinicopathological data of 124 patients with breast cancer were obtained, to developed Clinical, radiomics, and clinical-radiomics models, separately. Decision curve analysis (DCA) was employed to assess the models' clinical applicability.
The resulting area under the curves (AUCs) were 0.913, 0.831, 0.964 for the clinical model, the radiomics model, and the clinical-radiomics model, respectively. The Delong test revealed significant differences in the receiver operating characteristic (ROC) curves only between the clinical and clinical-radiomics models (all P<0.05). DCA substantiated the clinical-radiomics model's optimal predictive efficiency, enhanced discriminatory ability, and maximum benefit. The AUC (95% confidence interval: 0.935-0.993) of the clinical-radiomics model is 0.964. Repeated k-fold cross validation showed that average accuracy and Standard deviation of clinical-radiomics model are 90.23% and 8.45%, respectively. And the calibration slope of clinical-radiomics model is 1.08(p=0.071).
Although the clinical model was effective in predicting IMN status, the addition of DCEMRI radiomics significantly improved the predictive value of the clinical-radiomics model, which showed excellent discrimination, calibration, and stability. This suggests that the clinic-radiomics model has potential for preoperative assessment of IMN metastasis risk in breast cancer patients, but external validation is needed to confirm its clinical utility. IMN irradiation is recommended for early patients with high IMN metastasis risk, and overtreatment should be avoided for patients with low metastasis risk.
尽管利用放射组学和联合模型已实现对腋窝淋巴结状态的术前预测，但关于内乳淋巴结（IMN）转移状态预测的研究却很匮乏。我们通过将临床病理因素与术前动态对比增强磁共振成像（DCE-MRI）放射组学相结合，开发了一种预测模型，以准确预测乳腺癌中的IMN转移。
本研究纳入了术前影像无IMN转移证据但接受了内乳前哨淋巴结活检（IM-SLNB）的患者。获取了124例乳腺癌患者的术前DCE-MRI和临床病理数据，分别建立了临床、放射组学和临床-放射组学模型。采用决策曲线分析（DCA）评估模型的临床适用性。
临床模型、放射组学模型和临床-放射组学模型的曲线下面积（AUC）分别为0.913、0.831、0.964。德龙检验显示，仅临床模型与临床-放射组学模型之间的受试者工作特征（ROC）曲线存在显著差异（所有P<0.05）。DCA证实了临床-放射组学模型具有最佳的预测效率、增强的鉴别能力和最大益处。临床-放射组学模型的AUC（95%置信区间：0.935-0.993）为0.964。重复k折交叉验证显示，临床-放射组学模型的平均准确率和标准差分别为90.23%和8.45%。临床-放射组学模型的校准斜率为1.08（p=0.071）。
尽管临床模型在预测IMN状态方面有效，但加入DCE-MRI放射组学显著提高了临床-放射组学模型的预测价值，该模型显示出优异的鉴别、校准和稳定性。这表明临床-放射组学模型在乳腺癌患者IMN转移风险的术前评估中具有潜力，但需要外部验证以确认其临床效用。对于IMN转移风险高的早期患者，建议进行IMN放疗，而对于转移风险低的患者应避免过度治疗。
